From 0f777108037e2366bdaf5ba3c514a1d6bb2258c0 Mon Sep 17 00:00:00 2001 From: Repo Maintainer Date: Fri, 2 Oct 2026 13:10:15 +0200 Subject: [PATCH 1/8] fix(ml): deliver verified forecasts and portfolio opportunity analysis --- .gitattributes | 22 +- .gitignore | 5 + api/advanced_analytics_endpoints.py | 921 +-- api/alerts_endpoints.py | 6 +- api/coingecko_proxy_router.py | 8 +- api/crypto_toolbox_endpoints.py | 27 +- api/health_router.py | 10 +- api/ml/model_endpoints.py | 3 +- api/ml/monitoring_endpoints.py | 203 +- api/ml/prediction_endpoints.py | 165 +- api/ml/registry_endpoints.py | 15 +- api/ml/training_endpoints.py | 43 +- api/ml/unified_contract_endpoints.py | 231 +- api/ml_bourse_endpoints.py | 472 +- api/ml_crypto_endpoints.py | 272 +- api/portfolio_optimization_endpoints.py | 192 +- api/risk_bourse_endpoints.py | 3 +- api/risk_endpoints.py | 7 +- api/schemas/ml_contract.py | 472 +- api/startup.py | 109 +- api/unified_ml_endpoints.py | 5 +- config/ml_capability_registry.json | 30 + data/ml_verified/crypto/ADA.csv | 3089 +++++++++ data/ml_verified/crypto/ADA.json | 12 + data/ml_verified/crypto/BCH.csv | 2499 +++++++ data/ml_verified/crypto/BCH.json | 12 + data/ml_verified/crypto/BNB.csv | 3251 +++++++++ data/ml_verified/crypto/BNB.json | 12 + data/ml_verified/crypto/BTC.csv | 3332 ++++++++++ data/ml_verified/crypto/BTC.json | 12 + data/ml_verified/crypto/DOT.csv | 2235 +++++++ data/ml_verified/crypto/DOT.json | 12 + data/ml_verified/crypto/ETH.csv | 3332 ++++++++++ data/ml_verified/crypto/ETH.json | 12 + data/ml_verified/crypto/LINK.csv | 2815 ++++++++ data/ml_verified/crypto/LINK.json | 12 + data/ml_verified/crypto/LTC.csv | 3214 +++++++++ data/ml_verified/crypto/LTC.json | 12 + data/ml_verified/crypto/SOL.csv | 2242 +++++++ data/ml_verified/crypto/SOL.json | 12 + data/ml_verified/crypto/XRP.csv | 3072 +++++++++ data/ml_verified/crypto/XRP.json | 12 + data/ml_verified/cycles/BTC.csv | 5790 +++++++++++++++++ data/ml_verified/cycles/BTC.json | 12 + data/ml_verified/stocks/AAPL.csv | 5218 +++++++++++++++ data/ml_verified/stocks/AAPL.json | 10 + data/ml_verified/stocks/AGGS.SW.csv | 2145 ++++++ data/ml_verified/stocks/AGGS.SW.json | 18 + data/ml_verified/stocks/AMD.csv | 5218 +++++++++++++++ data/ml_verified/stocks/AMD.json | 10 + data/ml_verified/stocks/BRK-B.csv | 5218 +++++++++++++++ data/ml_verified/stocks/BRK-B.json | 10 + data/ml_verified/stocks/CHSPI.SW.csv | 3121 +++++++++ data/ml_verified/stocks/CHSPI.SW.json | 10 + data/ml_verified/stocks/CRSP.csv | 2500 +++++++ data/ml_verified/stocks/CRSP.json | 10 + data/ml_verified/stocks/CSPX.AS.csv | 4000 ++++++++++++ data/ml_verified/stocks/CSPX.AS.json | 10 + data/ml_verified/stocks/FLXI.DE.csv | 1845 ++++++ data/ml_verified/stocks/FLXI.DE.json | 10 + data/ml_verified/stocks/GLEN.L.csv | 3880 +++++++++++ data/ml_verified/stocks/GLEN.L.json | 10 + data/ml_verified/stocks/GOOGL.csv | 5218 +++++++++++++++ data/ml_verified/stocks/GOOGL.json | 10 + data/ml_verified/stocks/IWDA.AS.csv | 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| 10 + data/ml_verified/stocks/TSLA.csv | 4089 ++++++++++++ data/ml_verified/stocks/TSLA.json | 10 + data/ml_verified/stocks/UBSG.SW.csv | 5223 +++++++++++++++ data/ml_verified/stocks/UBSG.SW.json | 10 + data/ml_verified/stocks/UETW.DE.csv | 1860 ++++++ data/ml_verified/stocks/UETW.DE.json | 10 + data/ml_verified/stocks/VGK.csv | 5218 +++++++++++++++ data/ml_verified/stocks/VGK.json | 10 + data/ml_verified/stocks/VWO.csv | 5218 +++++++++++++++ data/ml_verified/stocks/VWO.json | 10 + data/ml_verified/stocks/WFRD.csv | 1442 ++++ data/ml_verified/stocks/WFRD.json | 10 + data/ml_verified/stocks/XGDU.MI.csv | 1528 +++++ data/ml_verified/stocks/XGDU.MI.json | 18 + data/ml_verified/stocks/XLI.csv | 5218 +++++++++++++++ data/ml_verified/stocks/XLI.json | 10 + data/ml_verified/stocks/XLP.csv | 5218 +++++++++++++++ data/ml_verified/stocks/XLP.json | 10 + data/ml_verified/stocks/XLU.csv | 5218 +++++++++++++++ data/ml_verified/stocks/XLU.json | 10 + .../XGDU.MI.csv | 1529 +++++ .../XGDU.MI.json | 10 + .../IWDA.AS.csv | 4354 +++++++++++++ .../IWDA.AS.json | 10 + .../SLHN.SW.csv | 5223 +++++++++++++++ .../SLHN.SW.json | 10 + .../AGGS.SW.csv | 2146 ++++++ .../AGGS.SW.json | 10 + .../SMH.L.csv | 1470 +++++ .../SMH.L.json | 10 + deploy/ml-production/release_robot2.py | 278 + docs/MARKET_OPPORTUNITIES_SYSTEM.md | 711 +- .../MARKET_OPPORTUNITIES_AUDIT_2026-10-01.md | 77 + ...KET_OPPORTUNITIES_DEPLOYMENT_2026-10-02.md | 57 + .../MARKET_OPPORTUNITIES_RELAY_2026-10-02.md | 31 + .../MARKET_OPPORTUNITIES_UI_2026-10-02.md | 27 + .../ML_RELIABILITY_DELIVERY_2026-09-30.md | 228 + .../ML_RELIABILITY_INVENTORY_2026-09-30.md | 49 + .../ML_RELIABILITY_PROTOCOL_2026-09-30.md | 68 + .../ML_RELIABILITY_RELEASE_2026-09-30.md | 77 + ...ABILITY_REPORTED_REGRESSIONS_2026-09-30.md | 202 + ...L_RELIABILITY_ROBOT2_PREVIEW_2026-09-30.md | 136 + docs/audit/README.md | 7 + models/validated_risk/crypto_ADA_30d.json | 86 + models/validated_risk/crypto_ADA_7d.json | 70 + models/validated_risk/crypto_BCH_30d.json | 86 + models/validated_risk/crypto_BCH_7d.json | 86 + models/validated_risk/crypto_BNB_30d.json | 64 + models/validated_risk/crypto_BNB_7d.json | 64 + models/validated_risk/crypto_BTC_30d.json | 64 + models/validated_risk/crypto_BTC_7d.json | 92 + models/validated_risk/crypto_DOT_30d.json | 64 + models/validated_risk/crypto_DOT_7d.json | 64 + models/validated_risk/crypto_ETH_30d.json | 70 + models/validated_risk/crypto_ETH_7d.json | 70 + models/validated_risk/crypto_LINK_30d.json | 64 + models/validated_risk/crypto_LINK_7d.json | 70 + models/validated_risk/crypto_LTC_30d.json | 64 + models/validated_risk/crypto_LTC_7d.json | 64 + models/validated_risk/crypto_SOL_30d.json | 70 + models/validated_risk/crypto_SOL_7d.json | 70 + models/validated_risk/crypto_XRP_30d.json | 64 + models/validated_risk/crypto_XRP_7d.json | 70 + models/validated_risk/registry.json | 436 ++ models/validated_risk/stocks_AAPL_30d.json | 86 + models/validated_risk/stocks_AAPL_7d.json | 64 + 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models/validated_risk/stocks_QQQ_30d.json | 64 + models/validated_risk/stocks_QQQ_7d.json | 64 + models/validated_risk/stocks_SLHN.SW_30d.json | 64 + models/validated_risk/stocks_SLHN.SW_7d.json | 64 + models/validated_risk/stocks_SPY_30d.json | 64 + models/validated_risk/stocks_SPY_7d.json | 64 + models/validated_risk/stocks_TSLA_30d.json | 64 + models/validated_risk/stocks_TSLA_7d.json | 64 + models/validated_risk/stocks_VGK_30d.json | 64 + models/validated_risk/stocks_VGK_7d.json | 64 + models/validated_risk/stocks_VWO_30d.json | 64 + models/validated_risk/stocks_VWO_7d.json | 64 + models/validated_risk/stocks_WFRD_30d.json | 86 + models/validated_risk/stocks_WFRD_7d.json | 64 + models/validated_risk/stocks_XLI_30d.json | 64 + models/validated_risk/stocks_XLI_7d.json | 64 + models/validated_risk/stocks_XLP_30d.json | 64 + models/validated_risk/stocks_XLP_7d.json | 64 + models/validated_risk/stocks_XLU_30d.json | 64 + models/validated_risk/stocks_XLU_7d.json | 64 + 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.../evaluations/stocks/KO-7d.json | 1055 +++ .../evaluations/stocks/META-30d.json | 665 ++ .../evaluations/stocks/META-7d.json | 665 ++ .../evaluations/stocks/MSFT-30d.json | 1055 +++ .../evaluations/stocks/MSFT-7d.json | 1055 +++ .../evaluations/stocks/NVDA-30d.json | 1055 +++ .../evaluations/stocks/NVDA-7d.json | 1055 +++ .../evaluations/stocks/PLTR-30d.json | 191 + .../evaluations/stocks/PLTR-7d.json | 191 + .../evaluations/stocks/QQQ-30d.json | 1055 +++ .../evaluations/stocks/QQQ-7d.json | 1055 +++ .../evaluations/stocks/SLHN.SW-30d.json | 1055 +++ .../evaluations/stocks/SLHN.SW-7d.json | 1055 +++ .../evaluations/stocks/SMH.L-30d.json | 8 + .../evaluations/stocks/SMH.L-7d.json | 8 + .../evaluations/stocks/SPY-30d.json | 1055 +++ .../evaluations/stocks/SPY-7d.json | 1055 +++ .../evaluations/stocks/SSLN.L-30d.json | 8 + .../evaluations/stocks/SSLN.L-7d.json | 8 + .../evaluations/stocks/TSLA-30d.json | 785 +++ .../evaluations/stocks/TSLA-7d.json | 785 +++ .../evaluations/stocks/UBSG.SW-30d.json | 8 + .../evaluations/stocks/UBSG.SW-7d.json | 8 + .../evaluations/stocks/UETW.DE-30d.json | 8 + .../evaluations/stocks/UETW.DE-7d.json | 8 + .../evaluations/stocks/VGK-30d.json | 1055 +++ .../evaluations/stocks/VGK-7d.json | 1055 +++ .../evaluations/stocks/VWO-30d.json | 1055 +++ .../evaluations/stocks/VWO-7d.json | 1055 +++ .../evaluations/stocks/WFRD-30d.json | 155 + .../evaluations/stocks/WFRD-7d.json | 155 + .../evaluations/stocks/WRDUSW_CHF.SW-30d.json | 8 + .../evaluations/stocks/WRDUSW_CHF.SW-7d.json | 8 + .../evaluations/stocks/XGDU.MI-30d.json | 8 + .../evaluations/stocks/XGDU.MI-7d.json | 8 + .../evaluations/stocks/XLI-30d.json | 1055 +++ .../evaluations/stocks/XLI-7d.json | 1055 +++ .../evaluations/stocks/XLP-30d.json | 1055 +++ .../evaluations/stocks/XLP-7d.json | 1055 +++ .../evaluations/stocks/XLU-30d.json | 1055 +++ .../evaluations/stocks/XLU-7d.json | 1055 +++ requirements.txt | 3 + scripts/acquire_cycle_reference.py | 61 + scripts/evaluate_ml_risk.py | 119 + scripts/package_ml_reliability.py | 115 + scripts/repair_ml_stock_observations.py | 95 + services/ml/auto_trainer.py | 42 +- services/ml/bourse/currency_detector.py | 11 + services/ml/bourse/fund_exposure.py | 133 + services/ml/bourse/market_analysis.py | 412 ++ services/ml/bourse/market_snapshot.py | 226 + services/ml/bourse/opportunity_scanner.py | 112 +- .../ml/bourse/recommendations_orchestrator.py | 85 +- services/ml/bourse/stocks_adapter.py | 436 +- services/ml/cycle_diagnostics.py | 105 + services/ml/live_observations.py | 284 + services/ml/model_registry.py | 12 + services/ml/models/btc_regime_detector.py | 52 +- services/ml/orchestrator.py | 2 +- services/ml/portfolio_context.py | 93 + services/ml/reliability.py | 380 ++ services/ml/risk_evaluation.py | 164 + services/ml/training_executor.py | 1 + services/ml_pipeline_manager_optimized.py | 169 +- services/monitoring/phase3_health_monitor.py | 5 +- services/risk/bourse/data_fetcher.py | 4 +- services/risk/bourse/specialized_analytics.py | 22 +- static/admin-dashboard.html | 48 +- static/advanced-risk.html | 15 +- static/ai-dashboard.html | 1029 +-- static/alias-manager.html | 70 +- static/analytics-unified.html | 2 +- static/analytics-unified.js | 94 +- static/bourse-analytics.html | 259 +- static/bourse-recommendations.html | 442 +- static/components/WealthContextBar.js | 8 + static/components/market-opportunities.js | 299 + static/components/ml-overview.js | 160 + static/components/ml-result-table.js | 50 + static/components/nav.js | 42 +- static/components/stock-ml-insights.js | 46 + static/components/toast.js | 12 +- static/components/view-toggle.js | 19 +- static/core/bourse-source-context.js | 13 + static/core/risk-data-orchestrator.js | 11 +- static/core/risk-request.js | 10 + static/core/role-policy.js | 1 + static/core/selected-source.js | 32 + static/css/bourse-analytics.css | 17 + static/css/bourse-recommendations.css | 121 + static/css/risk-dashboard.css | 7 + static/cycle-analysis.html | 499 +- static/global-config.js | 3 +- static/market-regimes.html | 93 +- .../analytics-unified-main-controller.js | 2 +- .../analytics-unified-tabs-controller.js | 219 +- static/modules/btc-regime-chart.js | 33 +- static/modules/dashboard-main-controller.js | 26 +- static/modules/eth-regime-chart.js | 4 + .../modules/portfolio-optimization-logic.js | 15 +- static/modules/rebalance-controller.js | 10 +- .../modules/risk-dashboard-main-controller.js | 23 +- static/modules/stock-regime-chart.js | 6 +- static/monitoring.html | 1 + static/shared-ml-functions.js | 139 +- static/tests/bourse-source-context.test.js | 13 + static/tests/market-opportunities.test.js | 126 + static/tests/ml-feedback-regressions.test.js | 19 + static/tests/ml-overview.test.js | 72 + static/tests/selected-source.test.js | 38 + static/tests/stock-ml-insights.test.js | 28 + tests/conftest.py | 11 + tests/unit/test_bourse_followup_contracts.py | 41 +- tests/unit/test_market_opportunities_api.py | 71 + tests/unit/test_market_opportunities_v2.py | 290 + tests/unit/test_ml_feedback_regressions.py | 49 + tests/unit/test_ml_oct1_repairs.py | 149 + tests/unit/test_ml_preview_regressions.py | 40 + tests/unit/test_ml_reliability.py | 468 ++ tests/unit/test_ml_reported_regressions.py | 136 + tests/unit/test_score_registry.py | 4 +- tests/unit/test_stock_adapter_reload.py | 72 +- tests/unit/test_stock_ml_loading.py | 44 + 358 files changed, 231380 insertions(+), 6335 deletions(-) create mode 100644 config/ml_capability_registry.json create mode 100644 data/ml_verified/crypto/ADA.csv create mode 100644 data/ml_verified/crypto/ADA.json create mode 100644 data/ml_verified/crypto/BCH.csv create mode 100644 data/ml_verified/crypto/BCH.json create mode 100644 data/ml_verified/crypto/BNB.csv create mode 100644 data/ml_verified/crypto/BNB.json create mode 100644 data/ml_verified/crypto/BTC.csv create mode 100644 data/ml_verified/crypto/BTC.json 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outputs/ml-reliability/evaluations/stocks/XLI-30d.json create mode 100644 outputs/ml-reliability/evaluations/stocks/XLI-7d.json create mode 100644 outputs/ml-reliability/evaluations/stocks/XLP-30d.json create mode 100644 outputs/ml-reliability/evaluations/stocks/XLP-7d.json create mode 100644 outputs/ml-reliability/evaluations/stocks/XLU-30d.json create mode 100644 outputs/ml-reliability/evaluations/stocks/XLU-7d.json create mode 100644 scripts/acquire_cycle_reference.py create mode 100644 scripts/evaluate_ml_risk.py create mode 100644 scripts/package_ml_reliability.py create mode 100644 scripts/repair_ml_stock_observations.py create mode 100644 services/ml/bourse/fund_exposure.py create mode 100644 services/ml/bourse/market_analysis.py create mode 100644 services/ml/bourse/market_snapshot.py create mode 100644 services/ml/cycle_diagnostics.py create mode 100644 services/ml/live_observations.py create mode 100644 services/ml/portfolio_context.py create mode 100644 services/ml/reliability.py create mode 100644 services/ml/risk_evaluation.py create mode 100644 static/components/market-opportunities.js create mode 100644 static/components/ml-overview.js create mode 100644 static/components/ml-result-table.js create mode 100644 static/components/stock-ml-insights.js create mode 100644 static/core/risk-request.js create mode 100644 static/core/role-policy.js create mode 100644 static/core/selected-source.js create mode 100644 static/tests/bourse-source-context.test.js create mode 100644 static/tests/market-opportunities.test.js create mode 100644 static/tests/ml-feedback-regressions.test.js create mode 100644 static/tests/ml-overview.test.js create mode 100644 static/tests/selected-source.test.js create mode 100644 static/tests/stock-ml-insights.test.js create mode 100644 tests/unit/test_market_opportunities_api.py create mode 100644 tests/unit/test_market_opportunities_v2.py create mode 100644 tests/unit/test_ml_feedback_regressions.py create mode 100644 tests/unit/test_ml_oct1_repairs.py create mode 100644 tests/unit/test_ml_preview_regressions.py create mode 100644 tests/unit/test_ml_reliability.py create mode 100644 tests/unit/test_ml_reported_regressions.py create mode 100644 tests/unit/test_stock_ml_loading.py diff --git a/.gitattributes b/.gitattributes index ac2d3094..f9fea2c2 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,10 +1,18 @@ -# Force normalisation texte et prévenir les BOM accidentels -* text=auto -*.py text eol=lf -*.js text eol=lf -*.html text eol=lf -*.css text eol=lf -*.json text eol=lf +# Force normalisation texte et prévenir les BOM accidentels +* text=auto +*.py text eol=lf +*.js text eol=lf +*.html text eol=lf +*.css text eol=lf +*.json text eol=lf *.md text eol=lf *.sh text eol=lf *.ps1 text eol=crlf + +# Validated artifact receipts fingerprint exact bytes; preserve across checkout platforms. +services/ml/reliability.py -text +services/ml/risk_evaluation.py -text +api/schemas/ml_contract.py -text +data/ml_verified/** -text +models/validated_risk/** -text +outputs/ml-reliability/evaluations/** -text diff --git a/.gitignore b/.gitignore index eef4dbae..00322190 100644 --- a/.gitignore +++ b/.gitignore @@ -185,3 +185,8 @@ phase3_*_results.json # Pytest reports pytest_report.json junit_*.xml + +# Verified public datasets and generated validation/release artifacts +/data/ml_verified/ +/models/validated_risk/ +/outputs/ml-reliability/ diff --git a/api/advanced_analytics_endpoints.py b/api/advanced_analytics_endpoints.py index 7618dcae..9c53fa6c 100644 --- a/api/advanced_analytics_endpoints.py +++ b/api/advanced_analytics_endpoints.py @@ -1,770 +1,193 @@ -""" -API Endpoints pour les Analytics Avancés -Métriques de performance sophistiquées et analyse de drawdown -""" +"""Advanced diagnostics for the authenticated selected source, without simulated fallback.""" +from typing import Optional -from fastapi import APIRouter, HTTPException, Query, Depends -from pydantic import BaseModel, Field -from typing import Dict, List, Any, Optional, Tuple -import logging -from datetime import datetime, timedelta -import math -import statistics +import numpy as np import pandas as pd -from api.utils.cache import cache_get, cache_set, cache_clear_expired +from fastapi import APIRouter, Depends, HTTPException, Query +from pydantic import BaseModel + from api.deps import get_required_user -from connectors.cointracking_api import get_current_balances +from services.ml.portfolio_context import read_context from services.price_history import get_cached_history +from services.portfolio_metrics import portfolio_metrics_service -logger = logging.getLogger(__name__) - -router = APIRouter(prefix="/analytics/advanced", tags=["advanced-analytics"]) - -class DrawdownPeriod(BaseModel): - """Période de drawdown détaillée""" - start_date: datetime - end_date: Optional[datetime] = None - peak_value: float - trough_value: float - drawdown_pct: float - duration_days: int - recovery_days: Optional[int] = None - is_recovered: bool = False +router = APIRouter(prefix='/analytics/advanced', tags=['advanced-analytics']) class AdvancedMetrics(BaseModel): - """Advanced performance metrics.""" - # Métriques de base total_return_pct: float annualized_return_pct: float volatility_pct: float - sharpe_ratio: float - - # Métriques de drawdown + sharpe_ratio: float | None max_drawdown_pct: float avg_drawdown_pct: float max_drawdown_duration_days: int avg_drawdown_duration_days: float - drawdown_periods: List[DrawdownPeriod] - - # Ratios avancés - calmar_ratio: float # Annualized return / Max Drawdown - sortino_ratio: float # Return vs downside deviation - omega_ratio: float # Probability weighted ratio - - # Métriques de distribution - skewness: float - kurtosis: float - var_95: float # Value at Risk 95% - cvar_95: float # Conditional VaR 95% - - # Métriques de timing + drawdown_periods: list[dict] + calmar_ratio: float | None + sortino_ratio: float | None + omega_ratio: float | None + skewness: float | None + kurtosis: float | None + var_95: float + cvar_95: float best_month_pct: float worst_month_pct: float positive_months_pct: float - win_loss_ratio: float + win_loss_ratio: float | None + provenance: dict class TimeSeriesData(BaseModel): - """Time-series data for charts.""" - dates: List[str] - portfolio_values: List[float] - returns: List[float] - cumulative_returns: List[float] - drawdowns: List[float] - rolling_sharpe: List[float] - rolling_volatility: List[float] - -@router.get("/metrics", response_model=AdvancedMetrics) -async def get_advanced_metrics( - user: str = Depends(get_required_user), - days: int = Query(365, description="Number of days of history"), - benchmark: Optional[str] = Query(None, description="Benchmark symbol (BTC, ETH, etc.)") -): - """ - Calculer les métriques de performance avancées - """ - # CACHE DÉSACTIVÉ pour forcer l'utilisation des vraies données! - logger.info(f" Cache désactivé - calcul en direct des métriques pour user={user}, {days} jours") - - try: - # NOUVEAU: Utiliser le service centralisé de métriques pour garantir la cohérence avec Risk Dashboard - try: - from services.portfolio_metrics import portfolio_metrics_service - from connectors.cointracking_api import get_current_balances - from services.price_history import get_cached_history - import pandas as pd - - logger.info(f" STARTING centralized metrics service for Advanced Analytics - user={user}, {days} days") - - # Récupérer les balances actuelles (isolées par user) - balances_response = await get_current_balances(source="cointracking", user_id=user) - if not balances_response.get("items"): - logger.error("No portfolio data available for centralized calculation") - raise HTTPException(status_code=404, detail="No portfolio data available") - - balances = balances_response["items"] - - # Récupérer les données de prix historiques (même logique que Risk Dashboard) - price_data = {} - for balance in balances: - symbol = balance.get('symbol', '').upper() - if symbol and balance.get('value_usd', 0) > 10: # Filtre minimum - try: - prices = get_cached_history(symbol, days=days+10) - if prices and len(prices) > days//2: - timestamps = [pd.Timestamp.fromtimestamp(p[0]) for p in prices] - values = [p[1] for p in prices] - price_data[symbol] = pd.Series(values, index=timestamps) - except Exception as e: - logger.warning(f"Failed to get price data for {symbol}: {e}") - - if len(price_data) < 2: - logger.error(f"Insufficient price data: only {len(price_data)} assets have price history") - raise HTTPException(status_code=503, detail="Insufficient price data for centralized calculation") - - # Créer DataFrame des prix - price_df = pd.DataFrame(price_data).ffill().dropna() - - # CALCULER AVEC LE SERVICE CENTRALISÉ (même calculs que Risk Dashboard) - centralized_metrics = portfolio_metrics_service.calculate_portfolio_metrics( - price_data=price_df, - balances=balances, - confidence_level=0.95 - ) - - # Convertir vers le format AdvancedMetrics pour compatibilité API - metrics = AdvancedMetrics( - total_return_pct=centralized_metrics.total_return_pct, - annualized_return_pct=centralized_metrics.annualized_return_pct, - volatility_pct=centralized_metrics.volatility_annualized * 100, - sharpe_ratio=centralized_metrics.sharpe_ratio, - max_drawdown_pct=centralized_metrics.max_drawdown * 100, - avg_drawdown_pct=centralized_metrics.current_drawdown * 100, - max_drawdown_duration_days=centralized_metrics.max_drawdown_duration_days, - avg_drawdown_duration_days=centralized_metrics.max_drawdown_duration_days // 2, - drawdown_periods=[], # Computed separately - calmar_ratio=centralized_metrics.calmar_ratio, - sortino_ratio=centralized_metrics.sortino_ratio, - omega_ratio=1.5, # Not available in centralized service yet - skewness=centralized_metrics.skewness, - kurtosis=centralized_metrics.kurtosis, - var_95=centralized_metrics.var_95_1d * 100, - cvar_95=centralized_metrics.cvar_95_1d * 100, - best_month_pct=8.0, # Estimated from positive_months_pct - worst_month_pct=-8.0, # Estimated - positive_months_pct=centralized_metrics.positive_months_pct, - win_loss_ratio=centralized_metrics.win_loss_ratio - ) - - logger.info(f" CENTRALIZED metrics for Advanced Analytics: Sharpe={centralized_metrics.sharpe_ratio:.2f}, Vol={centralized_metrics.volatility_annualized:.2%}, MaxDD={centralized_metrics.max_drawdown:.2%}") - - except Exception as e: - logger.error(f" CENTRALIZED METRICS FAILED: {e}") - logger.error(f" Exception type: {type(e)}") - import traceback - logger.error(f" Traceback: {traceback.format_exc()}") - # Fallback to mock data (avoid recursive calls) - mock_data = _generate_mock_performance_data(days) - metrics = _calculate_advanced_metrics(mock_data) - logger.warning(" USING MOCK DATA - Metrics will NOT match Risk Dashboard!") - - # PAS DE CACHE pour garantir les vraies données - logger.info(" Calcul sans cache terminé - données temps réel") - - return metrics - - except Exception as e: - logger.error(f"Error calculating advanced metrics: {str(e)}") - raise HTTPException(status_code=500, detail="Error calculating metrics") - -@router.get("/timeseries", response_model=TimeSeriesData) -async def get_timeseries_data( - user: str = Depends(get_required_user), - days: int = Query(365, description="Number of days of history"), - granularity: str = Query("daily", description="Granularity: daily, weekly, monthly") -): - """ - Récupérer les données de série temporelle pour les graphiques - """ - # CACHE DÉSACTIVÉ pour forcer les vraies données temporelles! - logger.info(f" Cache timeseries désactivé - calcul en direct pour user={user}, {days} jours") - + dates: list[str] + portfolio_values: list[float] + returns: list[float] + cumulative_returns: list[float] + drawdowns: list[float] + rolling_sharpe: list[float | None] + rolling_volatility: list[float | None] + provenance: dict + +async def _performance(days, user, source): try: - # Utiliser la même logique centralisée que les métriques pour la cohérence - try: - from services.portfolio_metrics import portfolio_metrics_service - from connectors.cointracking_api import get_current_balances - from services.price_history import get_cached_history - import pandas as pd - - # Récupérer les données avec la même logique (isolées par user) - balances_response = await get_current_balances(source="cointracking", user_id=user) - if balances_response.get("items"): - balances = balances_response["items"] - - # Même logique de récupération des prix - price_data = {} - for balance in balances: - symbol = balance.get('symbol', '').upper() - if symbol and balance.get('value_usd', 0) > 10: - try: - prices = get_cached_history(symbol, days=days+10) - if prices and len(prices) > days//2: - timestamps = [pd.Timestamp.fromtimestamp(p[0]) for p in prices] - values = [p[1] for p in prices] - price_data[symbol] = pd.Series(values, index=timestamps) - except Exception as e: - logger.warning(f"Failed to get price history for {symbol}: {e}") - continue - - if len(price_data) >= 2: - price_df = pd.DataFrame(price_data).ffill().dropna() - portfolio_returns = portfolio_metrics_service._calculate_weighted_portfolio_returns(price_df, balances) - - # Calculer la courbe de valeur - cumulative_value = (1 + portfolio_returns).cumprod() * 100000 # Base 100k - running_max = cumulative_value.expanding().max() - drawdowns = ((cumulative_value - running_max) / running_max * 100).tolist() - - # Rolling metrics simplifiés - rolling_sharpe = [0] * len(portfolio_returns) - rolling_volatility = [0] * len(portfolio_returns) - - # Construire la réponse avec données cohérentes - timeseries = TimeSeriesData( - dates=[d.strftime("%Y-%m-%d") for d in portfolio_returns.index], - portfolio_values=cumulative_value.tolist(), - returns=(portfolio_returns * 100).tolist(), # Convert to % - cumulative_returns=((cumulative_value / 100000 - 1) * 100).tolist(), # Total return in % - drawdowns=drawdowns, - rolling_sharpe=rolling_sharpe, - rolling_volatility=rolling_volatility - ) - - logger.info(f" Generated centralized timeseries: {len(portfolio_returns)} points") - else: - logger.error("Insufficient centralized data for timeseries generation") - raise HTTPException(status_code=503, detail="Insufficient centralized data") - else: - logger.error("No portfolio data available for timeseries") - raise HTTPException(status_code=404, detail="No portfolio data") - - except Exception as e: - logger.warning(f"Centralized timeseries failed: {e}, using mock fallback") - # Fallback aux données mock (avoid recursive calls) - real_data = _generate_mock_performance_data(days) - - timeseries = TimeSeriesData( - dates=[d.strftime("%Y-%m-%d") for d, _ in real_data["price_history"]], - portfolio_values=[v for _, v in real_data["price_history"]], - returns=real_data["daily_returns"], - cumulative_returns=real_data["cumulative_returns"], - drawdowns=real_data["drawdowns"], - rolling_sharpe=real_data["rolling_sharpe"], - rolling_volatility=real_data["rolling_volatility"] - ) - - # PAS DE CACHE pour garantir données temps réel - logger.info(" Timeseries calculé sans cache - données réelles") - - return timeseries - - except Exception as e: - logger.error(f"Error getting timeseries data: {str(e)}") - raise HTTPException(status_code=500, detail="Error getting timeseries data") - -@router.get("/drawdown-analysis") -async def analyze_drawdowns( - user: str = Depends(get_required_user), - days: int = Query(365, description="Number of days of history"), - min_duration: int = Query(5, description="Minimum duration in days") -) -> dict: - """ - Analyser les périodes de drawdown en détail - """ - try: - # Utiliser les données mock pour l'analyse des drawdowns (TODO: utiliser portfolio user) - real_data = _generate_mock_performance_data(days) - drawdown_periods = _analyze_drawdown_periods(real_data, min_duration) - - # Statistiques des drawdowns - if drawdown_periods: - avg_drawdown = sum(d.drawdown_pct for d in drawdown_periods) / len(drawdown_periods) - avg_duration = sum(d.duration_days for d in drawdown_periods) / len(drawdown_periods) - max_duration = max(d.duration_days for d in drawdown_periods) - recovery_rate = sum(1 for d in drawdown_periods if d.is_recovered) / len(drawdown_periods) - else: - avg_drawdown = avg_duration = max_duration = recovery_rate = 0 - - return { - "summary": { - "total_drawdown_periods": len(drawdown_periods), - "avg_drawdown_pct": avg_drawdown, - "avg_duration_days": avg_duration, - "max_duration_days": max_duration, - "recovery_rate": recovery_rate - }, - "periods": drawdown_periods - } - - except Exception as e: - logger.error(f"Error analyzing drawdowns: {str(e)}") - raise HTTPException(status_code=500, detail="Error analyzing drawdowns") - -@router.get("/strategy-comparison") -async def compare_strategies( - user: str = Depends(get_required_user), - strategies: List[str] = Query(["rebalancing", "buy_hold", "momentum"], description="Strategies to compare"), - days: int = Query(365, description="Analysis period") -) -> dict: - """ - Comparer les performances de différentes stratégies - """ - try: - comparison = {} - - for strategy in strategies: - mock_data = _generate_mock_performance_data(days, strategy_bias=strategy) - metrics = _calculate_advanced_metrics(mock_data) - - comparison[strategy] = { - "total_return": metrics.total_return_pct, - "sharpe_ratio": metrics.sharpe_ratio, - "max_drawdown": metrics.max_drawdown_pct, - "volatility": metrics.volatility_pct, - "calmar_ratio": metrics.calmar_ratio, - "positive_months": metrics.positive_months_pct - } - - # Calculer les rankings - rankings = {} - for metric in ["total_return", "sharpe_ratio", "calmar_ratio", "positive_months"]: - rankings[metric] = sorted( - strategies, - key=lambda s: comparison[s][metric], - reverse=True - ) - - # Pour max_drawdown et volatility, plus bas = mieux - for metric in ["max_drawdown", "volatility"]: - rankings[metric] = sorted( - strategies, - key=lambda s: abs(comparison[s][metric]), - reverse=False - ) - - return { - "comparison": comparison, - "rankings": rankings, - "best_overall": _calculate_overall_score(comparison, strategies) - } - - except Exception as e: - logger.error(f"Error comparing strategies: {str(e)}") - raise HTTPException(status_code=500, detail="Error comparing strategies") - -@router.get("/risk-metrics") -async def get_risk_metrics( - user: str = Depends(get_required_user), - days: int = Query(365, description="Analysis period"), - confidence_level: float = Query(0.95, description="Confidence level for VaR") -) -> dict: - """ - Calculer les métriques de risque avancées - """ - try: - # Utiliser les données mock pour les métriques de risque (TODO: utiliser portfolio user) - real_data = _generate_mock_performance_data(days) - returns = real_data["daily_returns"] - drawdowns = real_data["drawdowns"] - - # VaR et CVaR - var_95 = _calculate_var(returns, confidence_level) - cvar_95 = _calculate_cvar(returns, confidence_level) - - # Mesures de queue de distribution - skewness = _calculate_skewness(returns) - kurtosis = _calculate_kurtosis(returns) - - # Drawdown metrics - max_dd = min(drawdowns) - avg_dd = sum(d for d in drawdowns if d < 0) / max(1, sum(1 for d in drawdowns if d < 0)) - - return { - "value_at_risk": { - f"var_{int(confidence_level*100)}": var_95, - f"cvar_{int(confidence_level*100)}": cvar_95 - }, - "distribution_metrics": { - "skewness": skewness, - "kurtosis": kurtosis, - "is_normal_distribution": abs(skewness) < 0.5 and abs(kurtosis - 3) < 1 - }, - "drawdown_metrics": { - "max_drawdown_pct": max_dd, - "avg_drawdown_pct": avg_dd, - "time_in_drawdown_pct": sum(1 for d in drawdowns if d < -1) / len(drawdowns) * 100 - } - } - - except Exception as e: - logger.error(f"Error calculating risk metrics: {str(e)}") - raise HTTPException(status_code=500, detail="Error calculating risk metrics") - -async def _generate_real_performance_data(days: int, user_id: str) -> Dict[str, Any]: - """Générer des données de performance réelles en utilisant le service centralisé (PLUS D'APPELS API)""" - try: - # UTILISATION DIRECTE DU SERVICE CENTRALISÉ - Plus d'appels HTTP récursifs! - from services.portfolio_metrics import portfolio_metrics_service - from connectors.cointracking_api import get_current_balances - from services.price_history import get_cached_history - import pandas as pd - - logger.info(f" GENERATING TIMESERIES DATA using centralized service - user={user_id}, {days} days (no HTTP calls)") - - # Récupérer les données avec la même logique que les métriques (isolées par user) - balances_response = await get_current_balances(source="cointracking", user_id=user_id) - if not balances_response.get("items"): - logger.error("No portfolio data available for drawdown timeseries") - raise HTTPException(status_code=404, detail="No portfolio data available for timeseries") - - balances = balances_response["items"] - - # Récupérer les données de prix historiques (même logique) - price_data = {} - for balance in balances: - symbol = balance.get('symbol', '').upper() - if symbol and balance.get('value_usd', 0) > 10: # Filtre minimum - try: - prices = get_cached_history(symbol, days=days+10) - if prices and len(prices) > days//2: - timestamps = [pd.Timestamp.fromtimestamp(p[0]) for p in prices] - values = [p[1] for p in prices] - price_data[symbol] = pd.Series(values, index=timestamps) - except Exception as e: - logger.warning(f"Failed to get price data for {symbol}: {e}") - - if len(price_data) < 2: - logger.error(f"Insufficient price data for timeseries: only {len(price_data)} assets") - raise HTTPException(status_code=503, detail="Insufficient price data for centralized timeseries") - - # Créer DataFrame des prix - price_df = pd.DataFrame(price_data).ffill().dropna() - - # CALCULER AVEC LE SERVICE CENTRALISÉ pour les métriques ET les rendements - centralized_metrics = portfolio_metrics_service.calculate_portfolio_metrics( - price_data=price_df, - balances=balances, - confidence_level=0.95 - ) - - # Utiliser la méthode privée pour obtenir les rendements pondérés (même calcul que métriques) - portfolio_returns = portfolio_metrics_service._calculate_weighted_portfolio_returns(price_df, balances) - - # Générer la série temporelle cohérente avec les métriques centralisées - portfolio_history = [] - daily_returns = [] - cumulative_returns = [] - drawdowns = [] - rolling_sharpe = [] - rolling_volatility = [] - - # Calculer la courbe de valeur du portfolio (100k base) - portfolio_value_series = (1 + portfolio_returns).cumprod() * 100000 - running_max = portfolio_value_series.expanding().max() - drawdown_series = (portfolio_value_series - running_max) / running_max * 100 - - # Construire les listes pour compatibilité API - for i, (date, portfolio_value) in enumerate(portfolio_value_series.items()): - portfolio_history.append((date.to_pydatetime(), portfolio_value)) - daily_returns.append(portfolio_returns.iloc[i] * 100) # Convert to % - cumulative_returns.append((portfolio_value / 100000 - 1) * 100) # Total return % - drawdowns.append(drawdown_series.iloc[i]) - - # Rolling metrics simplifiés (cohérence avec l'approche centralisée) - if i >= 30: - window_returns = portfolio_returns.iloc[max(0, i-29):i+1] - rolling_vol = window_returns.std() * math.sqrt(252) - rolling_mean = window_returns.mean() * 252 - rolling_sharpe_val = (rolling_mean - 0.02) / max(rolling_vol, 0.01) - rolling_sharpe.append(rolling_sharpe_val) - rolling_volatility.append(rolling_vol) - else: - rolling_sharpe.append(0) - rolling_volatility.append(0) - - logger.info(f" Generated centralized timeseries: {len(portfolio_history)} points") - logger.info(f"Metrics consistency - Sharpe={centralized_metrics.sharpe_ratio:.2f}, Vol={centralized_metrics.volatility_annualized:.2%}, MaxDD={centralized_metrics.max_drawdown:.2%}") - - return { - "price_history": portfolio_history, - "daily_returns": daily_returns, - "cumulative_returns": cumulative_returns, - "drawdowns": drawdowns, - "rolling_sharpe": rolling_sharpe, - "rolling_volatility": rolling_volatility, - "centralized_metrics": centralized_metrics, # Inclure les métriques centralisées - "centralized_direct": True # Marquer comme utilisation directe du service - } - - except Exception as e: - logger.error(f"Error generating centralized timeseries data: {e}") - logger.warning("Falling back to mock data due to error") - return _generate_mock_performance_data(days) - -def _generate_mock_performance_data(days: int, strategy_bias: str = "rebalancing") -> Dict[str, Any]: - """Générer des données de performance simulées""" - import random - - # Paramètres selon la stratégie - params = { - "rebalancing": {"mean_return": 0.0008, "volatility": 0.025, "trend": 0.0002}, - "buy_hold": {"mean_return": 0.0006, "volatility": 0.035, "trend": 0.0001}, - "momentum": {"mean_return": 0.0012, "volatility": 0.040, "trend": 0.0003} - } - - param = params.get(strategy_bias, params["rebalancing"]) - - # Générer l'historique de prix - price_history = [] - current_price = 10000.0 - current_date = datetime.now() - timedelta(days=days) - - daily_returns = [] - cumulative_returns = [] - cum_return = 0 - - for i in range(days): - # Return quotidien avec tendance et volatilité - daily_return = random.normalvariate(param["mean_return"] + param["trend"], param["volatility"]) - current_price *= (1 + daily_return) - - price_history.append((current_date, current_price)) - daily_returns.append(daily_return * 100) # En pourcentage - - cum_return = ((current_price / 10000.0) - 1) * 100 - cumulative_returns.append(cum_return) - - current_date += timedelta(days=1) - - # Calculer les drawdowns - drawdowns = [] - peak = 10000.0 - - for _, price in price_history: - if price > peak: - peak = price - drawdown = ((price - peak) / peak) * 100 - drawdowns.append(drawdown) - - # Sharpe ratio rolling (30 jours) - rolling_sharpe = [] - rolling_volatility = [] - - for i in range(len(daily_returns)): - if i < 30: - rolling_sharpe.append(0) - rolling_volatility.append(0) - else: - window_returns = daily_returns[i-30:i] - avg_return = sum(window_returns) / 30 - vol = statistics.stdev(window_returns) * math.sqrt(252) # Annualisé - sharpe = (avg_return * 252) / max(vol, 0.1) - - rolling_sharpe.append(sharpe) - rolling_volatility.append(vol) - - return { - "price_history": price_history, - "daily_returns": daily_returns, - "cumulative_returns": cumulative_returns, - "drawdowns": drawdowns, - "rolling_sharpe": rolling_sharpe, - "rolling_volatility": rolling_volatility - } - -def _calculate_advanced_metrics(data: Dict[str, Any]) -> AdvancedMetrics: - """Calculer toutes les métriques avancées (harmonisées avec Risk Dashboard)""" - returns = data["daily_returns"] - drawdowns = data["drawdowns"] - price_history = data["price_history"] - - # Si les données proviennent du service centralisé, utiliser les vraies métriques DIRECTEMENT - if data.get("centralized_direct") and data.get("centralized_metrics"): - centralized_metrics = data["centralized_metrics"] - - # UTILISER LES MÉTRIQUES CENTRALISÉES DIRECTEMENT (zéro calcul supplémentaire) - total_return = data["cumulative_returns"][-1] if data["cumulative_returns"] else 0 - annualized_return = centralized_metrics.annualized_return_pct - volatility = centralized_metrics.volatility_annualized * 100 # Convert to % - sharpe_ratio = centralized_metrics.sharpe_ratio - max_drawdown = centralized_metrics.max_drawdown * 100 # Already negative - sortino_ratio = centralized_metrics.sortino_ratio - calmar_ratio = centralized_metrics.calmar_ratio - var_95 = centralized_metrics.var_95_1d * 100 # Convert to % - cvar_95 = centralized_metrics.cvar_95_1d * 100 # Convert to % - skewness = centralized_metrics.skewness - kurtosis = centralized_metrics.kurtosis - max_drawdown_duration = centralized_metrics.max_drawdown_duration_days - - logger.info(f" Using DIRECT centralized metrics - Sharpe: {sharpe_ratio:.2f}, Vol: {volatility:.1f}%, Max DD: {max_drawdown:.1f}%") - - else: - # Calculs classiques pour les données non-harmonisées - total_return = data["cumulative_returns"][-1] - days = len(returns) - annualized_return = ((1 + total_return/100) ** (365/days) - 1) * 100 - - volatility = statistics.stdev(returns) * math.sqrt(252) - - # Ratios de risque - risk_free_rate = 2.0 # 2% annuel - sharpe_ratio = (annualized_return - risk_free_rate) / max(volatility, 0.1) - - max_drawdown = min(drawdowns) - calmar_ratio = annualized_return / max(abs(max_drawdown), 1) - - # Sortino ratio (vs downside deviation) - downside_returns = [r for r in returns if r < 0] - downside_deviation = statistics.stdev(downside_returns) * math.sqrt(252) if downside_returns else volatility - sortino_ratio = (annualized_return - risk_free_rate) / max(downside_deviation, 0.1) - - # Métriques de distribution - skewness = _calculate_skewness(returns) - kurtosis = _calculate_kurtosis(returns) - var_95 = _calculate_var(returns, 0.95) - cvar_95 = _calculate_cvar(returns, 0.95) - max_drawdown_duration = 30 - - # Métriques communes - avg_drawdown = sum(d for d in drawdowns if d < 0) / max(1, sum(1 for d in drawdowns if d < 0)) - positive_returns = [r for r in returns if r > 0] - negative_returns = [r for r in returns if r < 0] - - return AdvancedMetrics( - total_return_pct=total_return, - annualized_return_pct=annualized_return, - volatility_pct=volatility, - sharpe_ratio=sharpe_ratio, - max_drawdown_pct=max_drawdown, - avg_drawdown_pct=avg_drawdown, - max_drawdown_duration_days=max_drawdown_duration, - avg_drawdown_duration_days=12, # Simulé - drawdown_periods=[], # Calculé séparément - calmar_ratio=calmar_ratio, - sortino_ratio=sortino_ratio, - omega_ratio=1.5, # Simulé - skewness=skewness, - kurtosis=kurtosis, - var_95=var_95, - cvar_95=cvar_95, - best_month_pct=max(returns) if returns else 0, - worst_month_pct=min(returns) if returns else 0, - positive_months_pct=len(positive_returns) / len(returns) * 100 if returns else 50, - win_loss_ratio=abs(sum(positive_returns) / max(sum(negative_returns), -1)) if negative_returns else 0 - ) - -def _analyze_drawdown_periods(data: Dict[str, Any], min_duration: int) -> List[DrawdownPeriod]: - """Analyser les périodes de drawdown""" - drawdowns = data["drawdowns"] - price_history = data["price_history"] - + context = await read_context(user, source, 'crypto') + except (ValueError, FileNotFoundError, OSError) as exc: + raise HTTPException(503, detail='Selected portfolio source is unavailable') from exc + balances = context['items'] + if not balances: + raise HTTPException(404, detail='No holdings in the selected source') + prices = {} + minimum=max(32,min(days+1,182)) + excluded_stale=0 + today=pd.Timestamp.now(tz='UTC').normalize() + for balance in balances: + symbol = str(balance.get('symbol') or '').upper() + if not symbol or float(balance.get('value_usd') or 0) <= 0: + continue + observations = get_cached_history(symbol, days=days+3) + if observations and len(observations) >= minimum: + series = pd.Series([p[1] for p in observations], index=pd.to_datetime([p[0] for p in observations], unit='s', utc=True)) + series = series.groupby(series.index.normalize()).last() + # Do not fabricate missing closes or include today's incomplete close. + series = series[series.index < today] + if series.empty or series.index[-1] < today-pd.Timedelta(days=2): + excluded_stale+=1 + continue + gaps=np.flatnonzero((series.index[1:]-series.index[:-1]).days!=1) + if len(gaps): + series=series.iloc[gaps[-1]+1:] + if len(series)>=minimum and (series > 0).all() and np.isfinite(series).all(): + prices[symbol] = series + if not prices: + raise HTTPException(503, detail='Historical observations are unavailable for the selected holdings') + frame = pd.DataFrame(prices).dropna().tail(days+1) + if len(frame) < 30 or not ((frame.index[1:]-frame.index[:-1]).days==1).all(): + raise HTTPException(503, detail='Insufficient consecutive daily observations; no simulated fallback') + returns = portfolio_metrics_service._calculate_weighted_portfolio_returns(frame, balances).dropna() + if not np.isfinite(returns).all() or (returns <= -1).any(): + raise HTTPException(503, detail='Invalid historical portfolio returns') + total = sum(float(b.get('value_usd') or 0) for b in balances) + covered = sum(float(b.get('value_usd') or 0) for b in balances if str(b.get('symbol') or '').upper() in prices) + provenance = {'source':source, 'source_used':context['source_used'], 'data_as_of':returns.index[-1].isoformat(), + 'coverage_by_current_value':covered/total if total > 0 else None, 'annualization':365, + 'data_start':frame.index[0].isoformat(),'observed_returns':len(returns),'requested_days':days, + 'minimum_asset_closes':minimum,'excluded_stale_assets':excluded_stale, + 'availability':'Partial' if covered < total else 'Available', + 'method':'current_holdings_weighted_historical_returns', 'value_unit':'normalized index, initial value 100', + 'reason':'Retrospective reconstruction using current holdings and cached daily observations, not actual account NAV or a strategy backtest. Missing, stale or insufficient histories are excluded; covered weights are normalized. Only the latest consecutive common window is used, without filling gaps. Historical cache provider provenance is not certified for forecasting. No future forecast.'} + return returns, provenance + +def _equity(returns): + values=(1+returns).cumprod()*100 + return pd.concat([pd.Series([100.],index=[returns.index[0]-pd.Timedelta(days=1)]),values]) + +def _drawdowns(values, minimum=1): + drawdown = values/values.cummax()-1 periods = [] - in_drawdown = False - current_period = None - - for i, (date, price) in enumerate(price_history): - dd = drawdowns[i] - - if dd < -1 and not in_drawdown: # Début de drawdown - in_drawdown = True - current_period = { - "start_date": date, - "start_idx": i, - "peak_value": price / (1 + dd/100), # Reconstituer le pic - "trough_value": price, - "max_dd": dd - } - - elif in_drawdown: - if dd < current_period["max_dd"]: # Nouveau creux - current_period["trough_value"] = price - current_period["max_dd"] = dd - - if dd >= -0.5: # Sortie de drawdown - duration = i - current_period["start_idx"] - - if duration >= min_duration: - periods.append(DrawdownPeriod( - start_date=current_period["start_date"], - end_date=date, - peak_value=current_period["peak_value"], - trough_value=current_period["trough_value"], - drawdown_pct=current_period["max_dd"], - duration_days=duration, - is_recovered=True - )) - - in_drawdown = False - current_period = None - - return periods - -def _calculate_var(returns: List[float], confidence_level: float) -> float: - """Calculer Value at Risk""" - sorted_returns = sorted(returns) - index = int((1 - confidence_level) * len(sorted_returns)) - return sorted_returns[index] if sorted_returns else 0 - -def _calculate_cvar(returns: List[float], confidence_level: float) -> float: - """Calculer Conditional Value at Risk""" - var = _calculate_var(returns, confidence_level) - tail_returns = [r for r in returns if r <= var] - return sum(tail_returns) / len(tail_returns) if tail_returns else 0 - -def _calculate_skewness(returns: List[float]) -> float: - """Calculer l'asymétrie (skewness)""" - if len(returns) < 3: - return 0 - - mean_return = sum(returns) / len(returns) - variance = sum((r - mean_return) ** 2 for r in returns) / len(returns) - std_dev = math.sqrt(variance) - - if std_dev == 0: - return 0 - - skew_sum = sum(((r - mean_return) / std_dev) ** 3 for r in returns) - return skew_sum / len(returns) - -def _calculate_kurtosis(returns: List[float]) -> float: - """Calculer l'aplatissement (kurtosis)""" - if len(returns) < 4: - return 3 # Distribution normale - - mean_return = sum(returns) / len(returns) - variance = sum((r - mean_return) ** 2 for r in returns) / len(returns) - std_dev = math.sqrt(variance) - - if std_dev == 0: - return 3 - - kurt_sum = sum(((r - mean_return) / std_dev) ** 4 for r in returns) - return kurt_sum / len(returns) - -def _calculate_overall_score(comparison: Dict, strategies: List[str]) -> str: - """Calculer le score global pour le classement""" - scores = {} - - for strategy in strategies: - metrics = comparison[strategy] - # Score pondéré (plus haut = mieux) - score = ( - metrics["total_return"] * 0.3 + - metrics["sharpe_ratio"] * 20 * 0.25 + - metrics["calmar_ratio"] * 20 * 0.2 + - (100 - abs(metrics["max_drawdown"])) * 0.15 + - metrics["positive_months"] * 0.1 - ) - scores[strategy] = score - - return max(scores.items(), key=lambda x: x[1])[0] + start = None + peak_date = values.index[0] + for date, dd in drawdown.items(): + if dd == 0: + if start is not None: + duration = (date-start).days + if duration >= minimum: + period = values.loc[start:date] + periods.append({'start_date':start.isoformat(), 'end_date':date.isoformat(), 'peak_value':float(values.loc[start]), + 'trough_value':float(period.min()), 'drawdown_pct':float(drawdown.loc[start:date].min()*100), + 'duration_days':duration, 'recovery_days':(date-period.idxmin()).days, 'is_recovered':True}) + start = None + peak_date = date + elif start is None: + start = peak_date + if start is not None and (values.index[-1]-start).days >= minimum: + period = values.loc[start:] + periods.append({'start_date':start.isoformat(), 'end_date':None, 'peak_value':float(values.loc[start]), + 'trough_value':float(period.min()), 'drawdown_pct':float(drawdown.loc[start:].min()*100), + 'duration_days':(values.index[-1]-start).days, 'recovery_days':None, 'is_recovered':False}) + return drawdown, periods + +def _calculate(returns, provenance): + values = _equity(returns) + dd, periods = _drawdowns(values) + total = values.iloc[-1]/100-1 + span = max(1, (returns.index[-1]-returns.index[0]).days+1) + annual = (1+total)**(365/span)-1 + vol = returns.std(ddof=0)*np.sqrt(365) + downside = np.sqrt(np.minimum(returns,0).pow(2).mean()*365) + monthly = (1+returns).resample('ME').prod()-1 + losses = abs(returns[returns<0].sum()) + gains = returns[returns>0].sum() + quantile = float(returns.quantile(.05)) + tail = returns[returns <= quantile] + return AdvancedMetrics(total_return_pct=total*100, annualized_return_pct=annual*100, + volatility_pct=vol*100, sharpe_ratio=float((returns.mean()*365-.02)/vol) if vol>0 else None, + max_drawdown_pct=float(dd.min()*100), avg_drawdown_pct=float(dd[dd<0].mean()*100) if (dd<0).any() else 0, + max_drawdown_duration_days=max((p['duration_days'] for p in periods),default=0), + avg_drawdown_duration_days=float(np.mean([p['duration_days'] for p in periods])) if periods else 0, + drawdown_periods=periods, calmar_ratio=float(annual/abs(dd.min())) if dd.min()<0 else None, + sortino_ratio=float((returns.mean()*365-.02)/downside) if downside>0 else None, + omega_ratio=float(gains/losses) if losses>0 else None, skewness=float(returns.skew()) if returns.std()>0 else None, + kurtosis=float(returns.kurtosis()+3) if returns.std()>0 else None, var_95=quantile*100, + cvar_95=float(tail.mean()*100), best_month_pct=float(monthly.max()*100), worst_month_pct=float(monthly.min()*100), + positive_months_pct=float((monthly>0).mean()*100), win_loss_ratio=float(gains/losses) if losses>0 else None, provenance=provenance) + +@router.get('/metrics', response_model=AdvancedMetrics) +async def get_advanced_metrics(user: str = Depends(get_required_user), days: int = Query(365,ge=30,le=3650), benchmark: Optional[str] = None, source: str = Query('cointracking',min_length=1,max_length=100)): + if benchmark: + raise HTTPException(422,detail='Benchmark comparison is not implemented; no substitute is shown') + returns, provenance = await _performance(days,user,source) + return _calculate(returns,provenance) + +@router.get('/timeseries', response_model=TimeSeriesData) +async def get_timeseries_data(user: str = Depends(get_required_user), days: int = Query(365,ge=30,le=3650), granularity: str = Query('daily',pattern='^(daily|weekly|monthly)$'), source: str = Query('cointracking',min_length=1,max_length=100)): + returns, provenance = await _performance(days,user,source) + if granularity != 'daily': + returns = (1+returns).resample('W' if granularity=='weekly' else 'ME').prod()-1 + values = _equity(returns) + dd,_ = _drawdowns(values) + factor = {'daily':365,'weekly':52,'monthly':12}[granularity] + rolling_vol = returns.rolling(30).std(ddof=0)*np.sqrt(factor) + rolling_sharpe = (returns.rolling(30).mean()*factor-.02)/rolling_vol.replace(0,np.nan) + def clean(series): + return [float(x) if pd.notna(x) and np.isfinite(x) else None for x in series] + return TimeSeriesData(dates=values.index.strftime('%Y-%m-%d').tolist(),portfolio_values=values.tolist(),returns=[0.]+(returns*100).tolist(), + cumulative_returns=((values/100-1)*100).tolist(),drawdowns=(dd*100).tolist(),rolling_sharpe=[None]+clean(rolling_sharpe),rolling_volatility=[None]+clean(rolling_vol*100),provenance=provenance) + +@router.get('/drawdown-analysis') +async def analyze_drawdowns(user: str = Depends(get_required_user),days: int = Query(365,ge=30,le=3650),min_duration: int = Query(5,ge=1),source: str = Query('cointracking')): + returns,provenance = await _performance(days,user,source) + _,periods = _drawdowns(_equity(returns),min_duration) + return {'summary':{'total_drawdown_periods':len(periods),'avg_drawdown_pct':float(np.mean([p['drawdown_pct'] for p in periods])) if periods else 0, + 'avg_duration_days':float(np.mean([p['duration_days'] for p in periods])) if periods else 0,'max_duration_days':max((p['duration_days'] for p in periods),default=0), + 'recovery_rate':sum(p['is_recovered'] for p in periods)/len(periods) if periods else None},'periods':periods,'provenance':provenance} + +@router.get('/strategy-comparison') +async def compare_strategies(user: str = Depends(get_required_user), strategies: list[str] = Query(['rebalancing','buy_hold','momentum']),days: int = Query(365)): + raise HTTPException(503,detail='Validated strategy return series are unavailable; simulated comparisons are not published') + +@router.get('/risk-metrics') +async def get_risk_metrics(user: str = Depends(get_required_user),days: int = Query(365,ge=30,le=3650),confidence_level: float = Query(.95,gt=0,lt=1),source: str = Query('cointracking')): + returns,provenance = await _performance(days,user,source) + dd,_ = _drawdowns(_equity(returns)) + var = float(returns.quantile(1-confidence_level)*100) + return {'value_at_risk':{f'var_{int(confidence_level*100)}':var,f'cvar_{int(confidence_level*100)}':float(returns[returns*100<=var].mean()*100)}, + 'distribution_metrics':{'skewness':float(returns.skew()) if returns.std()>0 else None,'kurtosis':float(returns.kurtosis()+3) if returns.std()>0 else None}, + 'drawdown_metrics':{'max_drawdown_pct':float(dd.min()*100),'avg_drawdown_pct':float(dd[dd<0].mean()*100) if (dd<0).any() else 0,'time_in_drawdown_pct':float((dd<0).mean()*100)},'provenance':provenance} diff --git a/api/alerts_endpoints.py b/api/alerts_endpoints.py index 274f8836..07fc8d0b 100644 --- a/api/alerts_endpoints.py +++ b/api/alerts_endpoints.py @@ -638,13 +638,15 @@ async def get_health_status( storage_healthy = False # Statut global - overall_status = "healthy" if (scheduler_healthy and storage_healthy) else "degraded" + preview = os.getenv("ML_TEST_PREVIEW") == "1" + overall_status = "healthy" if (storage_healthy and (scheduler_healthy or preview)) else "degraded" return { "status": overall_status, "components": { "scheduler": { - "status": "healthy" if scheduler_healthy else "unhealthy", + "status": "disabled" if preview else "healthy" if scheduler_healthy else "unhealthy", + "reason": "Intentionally disabled in the isolated preview" if preview else None, "is_active": engine.is_scheduler, "last_evaluation_seconds_ago": last_eval_age }, diff --git a/api/coingecko_proxy_router.py b/api/coingecko_proxy_router.py index eb71765d..29e7a726 100644 --- a/api/coingecko_proxy_router.py +++ b/api/coingecko_proxy_router.py @@ -219,7 +219,13 @@ async def _fetch_with_cache_and_fallback( age = (datetime.now() - _cache[cache_key]["timestamp"]).total_seconds() logger.info(f"Using stale cache (age: {age:.1f}s) due to API error {e.response.status_code}") return stale_data - raise HTTPException(status_code=e.response.status_code, detail=f"CoinGecko API error and no cache available") + # Une erreur d'authentification fournisseur ne doit pas déconnecter la session SmartFolio. + upstream_status = e.response.status_code + proxy_status = 502 if upstream_status in (401, 403) else upstream_status + raise HTTPException( + status_code=proxy_status, + detail=f"CoinGecko API error (upstream status {upstream_status}) and no cache available", + ) from e except HTTPException: raise diff --git a/api/crypto_toolbox_endpoints.py b/api/crypto_toolbox_endpoints.py index af74c8fc..c3271c0f 100644 --- a/api/crypto_toolbox_endpoints.py +++ b/api/crypto_toolbox_endpoints.py @@ -101,7 +101,24 @@ # Lifecycle Hooks (called from api/startup.py) # ============================================================================ +_browser_start_lock = asyncio.Lock() + + async def startup_playwright(): + """Serialize browser startup and recover a disconnected driver.""" + async with _browser_start_lock: + if _browser is not None: + if _browser.is_connected(): + return + await shutdown_playwright() + try: + await _startup_playwright_locked() + except Exception: + await shutdown_playwright() + raise + + +async def _startup_playwright_locked(): """ Initialize Playwright and launch browser (called at app startup). @@ -263,7 +280,15 @@ async def _scrape_crypto_toolbox() -> Dict[str, Any]: await page.goto(CRYPTO_TOOLBOX_URL, timeout=15000) # Wait for table instead of networkidle (faster) await page.wait_for_selector("table tbody tr", timeout=10000) - await asyncio.sleep(0.5) # Reduced delay for client-side hydration + # Les lignes existent avant les valeurs: attendre les chiffres hydratés, + # sans accepter les placeholders zéro comme observations. + await page.wait_for_function("""() => { + const cells=[...document.querySelectorAll('table tbody tr')] + .map(row=>row.querySelectorAll('td')).filter(c=>c.length>=3); + const values=cells.map(c=>Number.parseFloat(c[1].innerText.replace(/,/g,''))) + .filter(Number.isFinite); + return values.length>=10 && values.filter(v=>v!==0).length/values.length>.2; + }""", timeout=20000) # Parse table rows rows = await page.locator("table tbody tr").all() diff --git a/api/health_router.py b/api/health_router.py index b7c240fa..00ecb946 100644 --- a/api/health_router.py +++ b/api/health_router.py @@ -2,6 +2,7 @@ Health Router - Health Check and Utility Endpoints Extracted from api/main.py for better organization """ +import os import base64 import logging from datetime import datetime @@ -66,7 +67,6 @@ async def health_detailed(): @router.get("/health/redis") async def health_redis(): """Test Redis connectivity""" - import os try: import redis.asyncio as aioredis redis_url = os.getenv("REDIS_URL", "redis://localhost:6379/0") @@ -151,7 +151,6 @@ async def health_all(): Use this endpoint for monitoring dashboards and alerting systems. """ - import os import asyncio from datetime import datetime @@ -255,7 +254,12 @@ async def health_all(): } # Compute overall status - statuses = [c.get("status") for c in results["components"].values()] + preview = os.getenv("ML_TEST_PREVIEW") == "1" + if preview: + results["environment"] = "isolated_preview" + results["components"]["scheduler"]["reason"] = "Intentionally disabled in the isolated preview" + statuses = [c.get("status") for c in results["components"].values() + if not (preview and c.get("status") == "disabled" and c.get("enabled") is False)] if all(s == "healthy" for s in statuses): results["overall_status"] = "healthy" elif any(s == "unhealthy" for s in statuses): diff --git a/api/ml/model_endpoints.py b/api/ml/model_endpoints.py index 4784ca73..1d6bc0b0 100644 --- a/api/ml/model_endpoints.py +++ b/api/ml/model_endpoints.py @@ -97,8 +97,7 @@ async def load_volatility_models( background_tasks.add_task(_load_all_volatility_background) return { "success": True, - "message": "Loading all volatility models in background", - "estimated_duration_minutes": 2 + "message": "Loading all volatility models in background" } else: results = pipeline_manager.load_all_volatility_models() diff --git a/api/ml/monitoring_endpoints.py b/api/ml/monitoring_endpoints.py index 714ff32d..dbb2b2de 100644 --- a/api/ml/monitoring_endpoints.py +++ b/api/ml/monitoring_endpoints.py @@ -9,7 +9,8 @@ Extrait de unified_ml_endpoints.py pour modularité (Fév 2026). """ -from fastapi import APIRouter, HTTPException, Body +from fastapi import APIRouter, HTTPException, Body, Depends +from api.deps import require_admin_role from typing import Dict, Optional, Any import logging import json @@ -31,183 +32,31 @@ @router.get("/monitoring/health", response_model=MLSystemHealth) async def get_ml_system_health(): - """ - Obtenir l'état de santé global du système ML - """ - try: - gating_system = get_gating_system() - models_status = [] - - model_keys_to_check = set() - - # 1. Modèles avec historique de prédictions (gating) - history_keys = list(gating_system.prediction_history.keys()) - model_keys_to_check.update(history_keys) - logger.info(f"Found {len(history_keys)} models with prediction history") - - # 2. Modèles chargés dans le pipeline - try: - if hasattr(pipeline_manager, 'model_cache') and hasattr(pipeline_manager.model_cache, 'cache'): - cached_keys = list(pipeline_manager.model_cache.cache.keys()) - model_keys_to_check.update(cached_keys) - logger.info(f"Found {len(cached_keys)} models in cache: {cached_keys}") - except Exception as e: - logger.warning(f"Could not access model cache: {e}") - - # 3. Modèles disponibles sur disque - if not model_keys_to_check: - logger.info("No models in cache or history, checking available models on disk") - try: - pipeline_status = pipeline_manager.get_pipeline_status() - vol_symbols = pipeline_status.get('volatility_models', {}).get('available_symbols', []) - logger.info(f"Found {len(vol_symbols)} volatility models on disk: {vol_symbols[:5]}") - - for symbol in vol_symbols[:3]: - model_key = f'volatility_{symbol}' - model_keys_to_check.add(model_key) - logger.info(f"Added model from disk: {model_key}") - - if pipeline_status.get('regime_models', {}).get('model_exists', False): - model_keys_to_check.add('regime_model') - logger.info("Added regime_model from disk") - except Exception as e: - logger.error(f"Error checking models on disk: {e}", exc_info=True) - - logger.info(f"Total models to check: {len(model_keys_to_check)} - {list(model_keys_to_check)}") - - for model_key in model_keys_to_check: - try: - logger.info(f"Processing model: {model_key}") - health_report = gating_system.get_model_health_report(model_key) - logger.debug(f"Health report for {model_key}: {health_report}") - - model_is_loaded = (hasattr(pipeline_manager, 'model_cache') and - hasattr(pipeline_manager.model_cache, 'cache') and - model_key in pipeline_manager.model_cache.cache) - - if "error" in health_report: - health_report = { - "health_score": 0.8, - "error_rate": 0.0, - "avg_confidence": 0.7 if model_is_loaded else 0.5, - "total_predictions_24h": 0, - "last_prediction": None - } - logger.info(f"Created default health report for {model_key} (loaded={model_is_loaded})") - - if "error" not in health_report: - logger.info(f"Creating ModelHealth for {model_key}") - drift_score = None - if model_key in gating_system.prediction_history: - history = gating_system.prediction_history[model_key] - valid_predictions = [ - h['prediction'] for h in history[-30:] - if not h.get('error', False) and 'prediction' in h - ] - - if len(valid_predictions) >= 5: - mean_pred = float(np.mean(valid_predictions)) - std_pred = float(np.std(valid_predictions)) - - if abs(mean_pred) > 1e-6: - cv = std_pred / abs(mean_pred) - drift_score = min(1.0, max(0.0, (cv - 0.05) / 0.25)) - drift_score = round(float(drift_score), 3) - logger.debug(f"Drift score for {model_key}: {drift_score} (CV={cv:.3f}, n={len(valid_predictions)})") - - model_health = ModelHealth( - model_name=model_key.split('_')[0], - version="1.0.0", - is_healthy=health_report.get("health_score", 0.5) > 0.5, - last_prediction=health_report.get("last_prediction"), - error_rate_24h=health_report.get("error_rate", 0.0), - avg_confidence=health_report.get("avg_confidence", 0.7), - drift_score=drift_score - ) - models_status.append(model_health) - logger.info(f"Added ModelHealth for {model_key} to status list") - except Exception as e: - logger.error(f"Error processing model {model_key}: {e}", exc_info=True) - - # Calcul de la santé globale - if models_status: - health_scores = [ - m.avg_confidence * (1 - m.error_rate_24h) - for m in models_status if m.avg_confidence is not None and m.error_rate_24h is not None - ] - overall_health = float(np.mean(health_scores)) if health_scores else 0.5 - else: - overall_health = 0.8 - - total_predictions = 0 - for model_key in model_keys_to_check: - if model_key in gating_system.prediction_history: - report = gating_system.get_model_health_report(model_key) - total_predictions += report.get("total_predictions_24h", 0) - - system_metrics = { - "active_models": len(models_status), - "healthy_models": sum(1 for m in models_status if m.is_healthy), - "total_predictions_24h": total_predictions - } - - return MLSystemHealth( - overall_health=overall_health, - models_status=models_status, - system_metrics=system_metrics - ) - - except Exception as e: - logger.error(f"Failed to get ML system health: {e}") - return MLSystemHealth( - overall_health=0.3, - models_status=[], - system_metrics={"error": str(e)} - ) + from services.ml.reliability import capability_service + catalog = capability_service.catalog() + return MLSystemHealth(overall_health=None, models_status=[ + ModelHealth(model_name=entry["path"], version=entry["sha256"][:16], + is_healthy=None, last_prediction=None, avg_confidence=None, error_rate_24h=None) + for entry in catalog], + system_metrics={"files_present": len(catalog), "loaded_models": sum(e["loaded"] for e in catalog), + "successful_models": sum(e["inference_succeeded"] for e in catalog), + "active_models": sum(e["loaded"] for e in catalog), + "healthy_models": None, "total_predictions_24h": None, + "reason": "No performance health estimate is derived from artifact presence or confidence"}) @router.get("/metrics/{model_name}") @handle_api_errors(fallback={"error": "Metrics unavailable"}, reraise_http_errors=True) async def get_model_metrics(model_name: str, version: Optional[str] = None) -> dict: - """ - Obtenir les métriques pour un modèle spécifique - """ - metrics_file = Path("data/ml_metrics.json") - - if metrics_file.exists(): - with open(metrics_file, 'r') as f: - all_metrics = json.load(f) - - model_data = all_metrics.get(model_name, {}) - if version: - version_data = model_data.get("versions", {}).get(version, {}) - return success_response({"model": model_name, "version": version, "metrics": version_data}) - else: - versions = model_data.get("versions", {}) - if versions: - latest_version = max(versions.keys()) - return success_response({"model": model_name, "version": latest_version, "metrics": versions[latest_version]}) - - gating_system = get_gating_system() - matching_keys = [key for key in gating_system.prediction_history.keys() if model_name in key] - - if matching_keys: - key = matching_keys[0] - health_report = gating_system.get_model_health_report(key) - - return success_response({ - "model": model_name, - "version": "1.0.0", - "metrics": { - "predictions_24h": health_report.get("total_predictions_24h", 0), - "error_rate": health_report.get("error_rate", 0.0), - "avg_confidence": health_report.get("avg_confidence", 0.5), - "acceptance_rate": health_report.get("acceptance_rate", 0.8), - "last_updated": health_report.get("last_prediction", datetime.now()).isoformat() - } - }) - - return error_response(f"No metrics found for model {model_name}", code=400) + from services.ml.reliability import capability_service + try: + market, asset, days = model_name.rsplit("_", 2) + artifact, path = capability_service.artifact(market, asset, int(days.rstrip("d"))) + return success_response({"model": model_name, "version": artifact["code_version"], "availability": "Available", + "metrics": artifact["validation"]["metrics"], "validation_state": "retrospectively_validated"}) + except Exception: + return success_response({"model": model_name, "version": None, "metrics": None, "availability": "Unavailable", + "reason": "No verified confirmation evaluation is registered for this model"}) @router.get("/versions/{model_name}") @@ -233,8 +82,9 @@ async def get_model_versions(model_name: str) -> dict: return success_response({ "model": model_name, - "available_versions": ["1.0.0"], - "total_versions": 1 + "available_versions": [], + "total_versions": 0, + "reason": "No version observation is registered" }) @@ -243,7 +93,8 @@ async def get_model_versions(model_name: str) -> dict: async def update_model_metrics( model_name: str, version: str, - metrics: Dict[str, Any] = Body(...) + metrics: Dict[str, Any] = Body(...), + user: str = Depends(require_admin_role) ) -> dict: """ Mettre à jour les métriques d'un modèle (version spécifique) diff --git a/api/ml/prediction_endpoints.py b/api/ml/prediction_endpoints.py index 0bdd653f..afb23a92 100644 --- a/api/ml/prediction_endpoints.py +++ b/api/ml/prediction_endpoints.py @@ -20,7 +20,7 @@ from services.ml.orchestrator import get_orchestrator, get_ml_predictions from services.ml_pipeline_manager_optimized import optimized_pipeline_manager as pipeline_manager -from api.deps import get_required_user +from api.deps import get_required_user, require_admin_role from api.utils.formatters import success_response, error_response from shared.error_handlers import handle_api_errors, handle_service_errors from .cache_utils import get_ml_cache, cache_get, cache_set @@ -67,84 +67,41 @@ class SentimentResponse(BaseModel): fallback={"predictions": {}, "regime_prediction": None, "volatility_forecast": None, "model_status": {}}, reraise_http_errors=True ) -async def unified_predictions(request: PredictionRequest): - """ - Prédictions ML unifiées - volatilité, régime, corrélations - Support multi-horizon: horizons=[1, 7, 30] pour 1j, 7j, 30j - """ - ml_cache = get_ml_cache() - cache_key = f"predictions_{hash(str(request.dict()))}" - cached_result = cache_get(ml_cache, cache_key, 300) # 5 min cache - if cached_result: - return cached_result - - orchestrator = get_orchestrator() - horizons = request.horizons if request.horizons else [request.horizon_days] - - predictions = await get_ml_predictions(symbols=request.assets) - - enhanced_predictions = {} - if request.include_volatility and len(horizons) > 1: - enhanced_predictions = await _get_multi_horizon_predictions( - request.assets, horizons, request.include_confidence - ) - - final_predictions = predictions.get("predictions", {}) - if enhanced_predictions: - for symbol in request.assets: - if symbol in enhanced_predictions: - if symbol not in final_predictions: - final_predictions[symbol] = {} - final_predictions[symbol].update(enhanced_predictions[symbol]) - - if request.include_confidence: - final_predictions = await _add_confidence_metrics(final_predictions, request.assets) - - result = PredictionResponse( - success=True, - predictions=final_predictions, - regime_prediction=predictions.get("regime") if request.include_regime else None, - volatility_forecast=predictions.get("volatility") if request.include_volatility else None, - model_status=predictions.get("model_status", {}), - timestamp=datetime.now().isoformat() - ) - - cache_set(ml_cache, cache_key, result) - logger.info(f"Unified predictions generated for {len(request.assets)} assets, horizons: {horizons}") - return result +async def unified_predictions(request: PredictionRequest, user: str = Depends(get_required_user), source: str = Query("cointracking")): + from services.ml.reliability import capability_service + from api.schemas.ml_contract import ModelType, Horizon + horizons = request.horizons or [request.horizon_days] + results = {} + for asset in request.assets: + results[asset] = {} + for days in horizons: + h = {1: Horizon.D1, 7: Horizon.D7, 30: Horizon.D30, 90: Horizon.D90}.get(days) + record = await capability_service.result(asset, "crypto", ModelType.VOLATILITY, h) + results[asset][f"{days}d"] = record.model_dump(mode="json") + return PredictionResponse(success=True, predictions=results, + volatility_forecast={"available": any(r["value"] is not None for v in results.values() for r in v.values()), "predictions": results, "user_id": user, "source": source}, + regime_prediction=None, model_status={"reason": "Per-result availability is authoritative"}, timestamp=datetime.now().isoformat()) # ===== VOLATILITY PREDICTIONS ===== @router.get("/volatility/predict/{symbol}") @handle_api_errors(fallback={"volatility_forecast": None}) -async def predict_volatility(symbol: str, horizon_days: int = Query(30, ge=1, le=365)) -> dict: - """ - Prédiction de volatilité pour un asset spécifique - """ - ml_cache = get_ml_cache() - cache_key = f"volatility_{symbol}_{horizon_days}" - cached_result = cache_get(ml_cache, cache_key, 600) # 10 min cache - if cached_result: - return cached_result - - orchestrator = get_orchestrator() - prediction = await orchestrator.predict_volatility(symbol, horizon_days) - - result = success_response({ - "symbol": symbol, - "horizon_days": horizon_days, - "volatility_forecast": prediction, - "timestamp": datetime.now().isoformat() - }) - - cache_set(ml_cache, cache_key, result) - return result +async def predict_volatility(symbol: str, horizon_days: int = Query(30, ge=1, le=365), + user: str = Depends(get_required_user), source: str = Query("cointracking")) -> dict: + from services.ml.reliability import capability_service + from api.schemas.ml_contract import ModelType, Horizon + horizon = {7: Horizon.D7, 30: Horizon.D30}.get(horizon_days) + record = await capability_service.result(symbol, "crypto", ModelType.VOLATILITY, horizon) + return success_response({"symbol": symbol, "horizon_days": horizon_days, + "available": record.value is not None, "reason": record.reason, + "volatility_forecast": record.value, "prediction": record.value, + "result": record.model_dump(mode="json"), "user_id": user, "source": source}) @router.post("/volatility/train-portfolio") @handle_api_errors(fallback={"trainable_assets": 0, "loaded": 0, "results": {}}) -async def alias_train_portfolio(symbols: Optional[List[str]] = Query(None)) -> dict: +async def alias_train_portfolio(symbols: Optional[List[str]] = Query(None), user: str = Depends(require_admin_role)) -> dict: """Alias that preloads requested volatility models instead of training.""" req_symbols = symbols or ["BTC", "ETH"] results = {} @@ -153,7 +110,7 @@ async def alias_train_portfolio(symbols: Optional[List[str]] = Query(None)) -> d loaded = sum(1 for v in results.values() if v) return success_response({ "trainable_assets": len(req_symbols), - "estimated_duration_minutes": 1, + "action": "load_existing_artifacts", "results": results, "loaded": loaded }) @@ -161,12 +118,12 @@ async def alias_train_portfolio(symbols: Optional[List[str]] = Query(None)) -> d @router.post("/volatility/batch-predict") @handle_api_errors(fallback={"predictions": {}}) -async def alias_batch_predict(payload: Dict[str, Any] = Body(default={})) -> dict: +async def alias_batch_predict(payload: Dict[str, Any] = Body(default={}), user: str = Depends(get_required_user), source: str = Query("cointracking")) -> dict: """Alias that forwards to unified /predict.""" assets = payload.get("symbols") or payload.get("assets") or ["BTC", "ETH"] horizons = [1, 7, 30] req = PredictionRequest(assets=assets, horizons=horizons, include_regime=False, include_volatility=True) - return await unified_predictions(req) + return await unified_predictions(req, user, source) # ===== REGIME PREDICTIONS ===== @@ -302,65 +259,11 @@ async def get_symbol_sentiment( days: int = Query(1, ge=1, le=30, description="Number of days for sentiment analysis"), include_breakdown: bool = Query(True, description="Include detailed source breakdown") ): - """ - Get sentiment analysis for a cryptocurrency symbol - """ - logger.debug(f"Getting sentiment analysis for {symbol} over {days} days") - - ml_cache = get_ml_cache() - cache_key = f"sentiment:{symbol}:{days}:{include_breakdown}" - cached_result = cache_get(ml_cache, cache_key, 900) - if cached_result: - logger.debug(f"Returning cached sentiment for {symbol}") - return cached_result - - try: - from services.execution.governance import governance_engine - current_state = await governance_engine.get_current_state() - signals = current_state.signals if current_state else None - except Exception as e: - logger.warning(f"Could not get verified governance sentiment: {e}") - signals = None - - is_available = bool( - signals - and getattr(signals, "available", False) - and isinstance(signals.sentiment, dict) - and signals.sentiment - ) - if not is_available: - result = SentimentResponse( - success=False, - symbol=symbol.upper(), - aggregated_sentiment={ - "available": False, - "reason": "No verified sentiment observation is available", - "analysis_period_days": days - }, - sources_used=[], - metadata={"timestamp": datetime.now().isoformat(), "data_quality": "unavailable"} - ) - cache_set(ml_cache, cache_key, result) - return result - - result = SentimentResponse( - success=True, - symbol=symbol.upper(), - aggregated_sentiment={ - "available": True, - **signals.sentiment, - "analysis_period_days": days - }, - sources_used=list(signals.sources_used), - metadata={ - "timestamp": datetime.now().isoformat(), - "data_quality": "reported_by_governance", - "last_updated": datetime.now().isoformat() - } - ) - - cache_set(ml_cache, cache_key, result) - return result + """No asset sentiment model has an independently validated adapter.""" + return SentimentResponse(success=False, symbol=symbol.upper(), + aggregated_sentiment={"available": False, "value": None, "availability": "Unavailable", + "reason": "No validated asset sentiment model. Fear & Greed is a separate external crypto-market indicator."}, + sources_used=[], metadata={"validation_state": "not_evaluable"}) # ===== CORRELATION ENDPOINT ===== diff --git a/api/ml/registry_endpoints.py b/api/ml/registry_endpoints.py index 8b1de4ff..a548f031 100644 --- a/api/ml/registry_endpoints.py +++ b/api/ml/registry_endpoints.py @@ -9,7 +9,8 @@ Extrait de unified_ml_endpoints.py pour modularité (Fév 2026). """ -from fastapi import APIRouter, HTTPException, Body +from fastapi import APIRouter, HTTPException, Body, Depends +from api.deps import require_admin_role from typing import Dict, Optional, Any import logging @@ -66,11 +67,13 @@ async def get_model_versions_registry(model_name: str) -> dict: for version, manifest in registry.models[model_name].items(): versions_info.append({ "version": version, - "status": manifest.status, + "status": manifest.to_dict()["status"], "created_at": manifest.created_at, "model_type": manifest.model_type, "file_size": manifest.file_size, - "validation_metrics": manifest.validation_metrics, + "validation_metrics": manifest.to_dict()["validation_metrics"], + "availability": manifest.availability, + "validation_reason": manifest.validation_reason, "tags": manifest.tags }) @@ -91,7 +94,8 @@ async def update_model_status( model_name: str, version: str, status: ModelStatus, - reason: Optional[str] = Body(None) + reason: Optional[str] = Body(None), + user: str = Depends(require_admin_role) ) -> dict: """ Mettre à jour le statut d'un modèle @@ -118,7 +122,8 @@ async def update_model_performance_metrics( model_name: str, version: str, validation_metrics: Optional[Dict[str, float]] = Body(None), - test_metrics: Optional[Dict[str, float]] = Body(None) + test_metrics: Optional[Dict[str, float]] = Body(None), + user: str = Depends(require_admin_role) ) -> dict: """ Mettre à jour les métriques de performance d'un modèle diff --git a/api/ml/training_endpoints.py b/api/ml/training_endpoints.py index 70bdb66b..b641735c 100644 --- a/api/ml/training_endpoints.py +++ b/api/ml/training_endpoints.py @@ -8,8 +8,9 @@ Extrait de unified_ml_endpoints.py pour modularité (Fév 2026). """ -from fastapi import APIRouter, BackgroundTasks -from typing import List +from fastapi import APIRouter, BackgroundTasks, Depends +from api.deps import require_admin_role +from typing import List, Literal import logging from datetime import datetime from pydantic import BaseModel @@ -20,7 +21,7 @@ from .model_endpoints import load_regime_model logger = logging.getLogger(__name__) -router = APIRouter(tags=["ML Training"]) +router = APIRouter(tags=["ML Training"], dependencies=[Depends(require_admin_role)]) class TrainingRequest(BaseModel): @@ -29,6 +30,7 @@ class TrainingRequest(BaseModel): lookback_days: int = 730 include_market_indicators: bool = True save_models: bool = True + market: Literal["crypto", "stocks"] = "crypto" @router.post("/train") @@ -48,7 +50,8 @@ async def train_models( request.assets, request.lookback_days, request.include_market_indicators, - request.save_models + request.save_models, + request.market ) # Invalider les caches de prédiction @@ -60,7 +63,7 @@ async def train_models( "success": True, "message": f"Training started for {len(request.assets)} assets", "assets": request.assets, - "estimated_duration_minutes": len(request.assets) * 2, + "action": "evaluate_frozen_volatility_protocol", "background_task": True } @@ -70,24 +73,30 @@ async def _train_models_background( assets: List[str], lookback_days: int, include_market_indicators: bool, - save_models: bool + save_models: bool, + market: Literal["crypto", "stocks"] = "crypto" ): """ Tâche d'entraînement en arrière-plan """ - orchestrator = get_orchestrator() - - await orchestrator.train_models( - assets=assets, - lookback_days=lookback_days, - include_market_indicators=include_market_indicators, - save_models=save_models - ) - - logger.info(f"Background training completed for assets: {assets}") + import asyncio + import json + from services.ml.risk_evaluation import evaluate + from services.ml.reliability import capability_service + reports = [] + for asset in assets: + for horizon in (7, 30): + try: + reports.append(await asyncio.to_thread(evaluate, capability_service, market, asset.upper(), horizon, publish=save_models)) + except Exception as exc: + reports.append({"asset": asset, "horizon": horizon, "state": "not_evaluable", "reason": str(exc), "published": False}) + destination = capability_service.root / "outputs/ml-reliability/admin-evaluation.json" + destination.parent.mkdir(parents=True, exist_ok=True) + destination.write_text(json.dumps(reports, indent=2, allow_nan=False), encoding="utf-8") + logger.info("Explicit ML evaluation completed; conclusions recorded at %s", destination) @router.post("/regime/train") async def alias_regime_train() -> dict: """Alias that loads the regime model.""" - return await load_regime_model() + return {"success": False, "availability": "Unavailable", "reason": "Legacy training alias retired. Use the explicit administrator evaluation workflow."} diff --git a/api/ml/unified_contract_endpoints.py b/api/ml/unified_contract_endpoints.py index e35d1ae0..d0c8423a 100644 --- a/api/ml/unified_contract_endpoints.py +++ b/api/ml/unified_contract_endpoints.py @@ -1,176 +1,77 @@ -""" -ML Unified Contract Endpoints - Prédictions avec contrat unifié +"""Authenticated adapters to the shared, read-only capability service.""" +from fastapi import APIRouter, Depends, Query +from typing import Optional +from datetime import datetime, timezone +from api.deps import get_required_user +from api.schemas.ml_contract import UnifiedMLRequest, UnifiedMLResponse, ModelType, Horizon +from services.ml.reliability import capability_service -Ce module gère: -- Endpoint de prédiction unifié avec gating et incertitude -- Prédiction de volatilité avec contrat unifié - -Extrait de unified_ml_endpoints.py pour modularité (Fév 2026). -""" - -from fastapi import APIRouter, Query -from typing import Dict, Optional, Any -import logging -import numpy as np -from datetime import datetime - -from services.ml.orchestrator import get_orchestrator -from shared.error_handlers import handle_service_errors -from .gating import get_gating_system -from api.schemas.ml_contract import ( - UnifiedMLRequest, UnifiedMLResponse, ModelType, Horizon, - UnifiedPrediction, QualityMetrics, UncertaintyMeasures, - ModelMetadata, create_fallback_response -) - -logger = logging.getLogger(__name__) router = APIRouter(tags=["ML Unified Contract"]) +@router.get("/overview") +async def overview(user: str = Depends(get_required_user), source: str = Query("cointracking", min_length=1, max_length=100), market: str = Query("crypto", pattern="^(crypto|stocks)$"), assets: str = Query("BTC,ETH,SOL", max_length=1000), mode: str = Query("benchmarks", pattern="^(portfolio|benchmarks)$"), limit: int = Query(25, ge=1, le=250), file_key: str | None = Query(None)): + context = None + if mode == "portfolio": + from services.ml.portfolio_context import read_context, select_assets + try: + raw = await read_context(user, source, market, file_key) + symbols, context = select_assets(raw, market, limit) + except (ValueError, FileNotFoundError, OSError) as exc: + symbols = [] + context = dict(availability="Unavailable", reason=str(exc), held_positions=None, selected_assets=0) + else: + symbols = list(dict.fromkeys(a.strip().upper() for a in assets.split(",") if a.strip()))[:50] + if capability_service.refresh_observations: + import asyncio + semaphore=asyncio.Semaphore(4) + async def prepare(asset): + async with semaphore: + try: + await asyncio.to_thread(capability_service.history,market,asset) + except (ValueError,FileNotFoundError,OSError): + pass # The result adapter exposes the exact unavailable reason. + await asyncio.gather(*(prepare(asset) for asset in symbols)) + result = await capability_service.overview(user, source, symbols, market) + result["scope"] = "selected_authenticated_portfolio" if mode == "portfolio" else "explicit_market_universe" + result["portfolio_context"] = context + return result @router.post("/unified/predict", response_model=UnifiedMLResponse) -async def unified_predict(request: UnifiedMLRequest): - """ - Endpoint unifié de prédiction ML avec gating et incertitude - - Supports: volatility, sentiment, risk scoring - """ - start_time = datetime.now() - gating_system = get_gating_system() - - try: - logger.debug(f"Unified prediction request: {request.model_type} for {len(request.assets)} assets") - - predictions = [] - failed_assets = [] - warnings = [] - - for asset in request.assets: - try: - raw_prediction = await _get_raw_prediction( - asset, request.model_type, request.horizon - ) - if raw_prediction is None or not np.isfinite(raw_prediction): - raise ValueError("Verified model output is unavailable") - - model_key = f"{request.model_type.value}_{request.horizon.value if request.horizon else 'default'}" - - gated_prediction, accepted = gating_system.gate_prediction( - asset=asset, - raw_prediction=raw_prediction, - model_key=model_key, - model_type=request.model_type, - context={} - ) - - if not accepted or gated_prediction is None: - failed_assets.append(asset) - warnings.append(f"{asset}: prediction rejected by quality gate") - continue - - if request.include_metadata: - gated_prediction.metadata = ModelMetadata( - name=model_key, - version="1.0.0", - model_type=request.model_type, - horizon=request.horizon - ) - - if gated_prediction.quality.confidence >= request.confidence_threshold: - predictions.append(gated_prediction) - else: - failed_assets.append(asset) - warnings.append(f"{asset}: confidence below threshold") - - except Exception as e: - logger.error(f"Failed to predict for {asset}: {e}") - failed_assets.append(asset) - warnings.append(f"{asset}: {str(e)}") - - processing_time = (datetime.now() - start_time).total_seconds() * 1000 - - aggregated = {} - if predictions: - values = [p.value for p in predictions] - confidences = [p.quality.confidence for p in predictions] - aggregated = { - "avg_prediction": float(np.mean(values)), - "avg_confidence": float(np.mean(confidences)), - "prediction_range": [float(min(values)), float(max(values))] - } - - return UnifiedMLResponse( - success=len(predictions) > 0, - model_type=request.model_type, - horizon=request.horizon, - predictions=predictions, - aggregated=aggregated, - processing_time_ms=processing_time, - warnings=warnings, - failed_assets=failed_assets - ) - - except Exception as e: - logger.error(f"Unified prediction failed: {e}") - return create_fallback_response( - request.model_type, - request.assets, - f"Unified prediction error: {str(e)}" - ) - +async def unified_predict(request: UnifiedMLRequest, user: str = Depends(get_required_user)): + start = datetime.now(timezone.utc) + results = [await capability_service.result(asset, request.market, request.model_type, request.horizon) for asset in request.assets] + capability_service.journal(user, request.source, results) + if not request.include_uncertainty: + for result in results: + result.uncertainty = None + return UnifiedMLResponse(success=True, model_type=request.model_type, horizon=request.horizon, + predictions=results, user_id=user, source=request.source, + failed_assets=[r.asset for r in results if r.value is None], + warnings=[f"{r.asset}: {r.reason}" for r in results if r.value is None], + processing_time_ms=(datetime.now(timezone.utc)-start).total_seconds()*1000) @router.get("/unified/volatility/{symbol}", response_model=UnifiedMLResponse) -async def unified_volatility_predict( - symbol: str, - horizon: Horizon = Query(Horizon.D30, description="Prediction horizon"), - include_uncertainty: bool = Query(True, description="Include uncertainty measures"), - include_metadata: bool = Query(False, description="Include model metadata") -): - """ - Prédiction de volatilité avec contrat unifié - """ - request = UnifiedMLRequest( - assets=[symbol.upper()], - model_type=ModelType.VOLATILITY, - horizon=horizon, - include_uncertainty=include_uncertainty, - include_metadata=include_metadata - ) - - return await unified_predict(request) - +async def unified_volatility_predict(symbol: str, horizon: Horizon = Query(Horizon.D30), + include_uncertainty: bool = Query(True), include_metadata: bool = Query(False), + source: str = Query("cointracking"), market: str = Query("crypto", pattern="^(crypto|stocks)$"), + user: str = Depends(get_required_user)): + return await unified_predict(UnifiedMLRequest(assets=[symbol.upper()], model_type=ModelType.VOLATILITY, + horizon=horizon, market=market, source=source, include_uncertainty=include_uncertainty, + include_metadata=include_metadata), user) async def _get_raw_prediction(asset: str, model_type: ModelType, horizon: Optional[Horizon]) -> Optional[float]: - """ - Obtenir une prédiction brute selon le type de modèle - - Cette fonction fait le bridge avec les services ML existants - """ - orchestrator = get_orchestrator() - - if model_type == ModelType.VOLATILITY: - days_mapping = { - Horizon.H1: 1/24, - Horizon.H4: 4/24, - Horizon.D1: 1, - Horizon.D7: 7, - Horizon.D30: 30, - Horizon.D90: 90 - } - days = days_mapping.get(horizon, 30) - result = await orchestrator.predict_volatility(asset, int(days)) - - if isinstance(result, dict) and 'prediction' in result: - return float(result['prediction']) - elif isinstance(result, (int, float)): - return float(result) - return None + """Compatibility helper; diagnostics never masquerade as forecasts.""" + result = await capability_service.result(asset, "crypto", model_type, horizon) + return result.value if result.nature == "forecast" and isinstance(result.value, (int, float)) else None - elif model_type == ModelType.SENTIMENT: - return None - elif model_type == ModelType.RISK: - return None - - else: - logger.warning(f"Unsupported model type: {model_type}") - return None +@router.get("/cycle-history") +async def cycle_history(user: str = Depends(get_required_user)): + """Verified observed closes for descriptive cycle comparisons; no forecast.""" + import asyncio + from services.ml.cycle_diagnostics import historical_cycle_comparison + try: + close, receipt = await asyncio.to_thread(capability_service.history, "crypto", "BTC") + return historical_cycle_comparison(capability_service.root, close, receipt) + except (ValueError, FileNotFoundError, OSError) as exc: + return {"availability": "Unavailable", "reason": str(exc), "cycles": []} diff --git a/api/ml_bourse_endpoints.py b/api/ml_bourse_endpoints.py index a98f17df..d3f69a23 100644 --- a/api/ml_bourse_endpoints.py +++ b/api/ml_bourse_endpoints.py @@ -85,7 +85,7 @@ def _finite_number(value: Any, default: Optional[float]) -> Optional[float]: class VolatilityForecastResponse(BaseModel): """Response model for volatility forecast""" symbol: str - timestamp: str + timestamp: Optional[str] = None predictions: Dict[str, Any] model_type: str lookback_days: int @@ -103,7 +103,7 @@ class RegimeDetectionResponse(BaseModel): confidence: Optional[float] = None regime_probabilities: Dict[str, float] benchmark: str - timestamp: str + timestamp: Optional[str] = None characteristics: Dict[str, str] model_type: Optional[str] = None model_validation: Optional[Dict[str, Any]] = None @@ -114,7 +114,7 @@ class CorrelationForecastResponse(BaseModel): """Response model for correlation forecast""" symbols: List[str] predictions: Dict[str, Any] - timestamp: str + timestamp: Optional[str] = None horizons: List[int] model_type: str note: Optional[str] = None @@ -123,12 +123,17 @@ class CorrelationForecastResponse(BaseModel): class SignalsResponse(BaseModel): """Response model for ML signals""" symbol: str - timestamp: str - overall_signal: float - confidence: float + timestamp: Optional[str] = None + overall_signal: Optional[float] = None + confidence: Optional[float] = None signals: Dict[str, Dict[str, float]] recommendation: str technical_indicators: Dict[str, float] + availability: str = "Unavailable" + nature: str = "diagnostic" + reason: str = "No verified technical observation" + data_as_of: Optional[str] = None + provider: Optional[str] = None error: Optional[str] = None @@ -166,10 +171,11 @@ async def forecast_volatility( raise HTTPException(status_code=500, detail=f"Internal error: {str(e)}") -@router.get("/api/ml/bourse/regime", response_model=RegimeDetectionResponse) +@router.get("/api/ml/bourse/regime") async def detect_regime( benchmark: str = Query("SPY", description="Market benchmark ticker"), lookback_days: int = Query(7300, ge=60, le=10950, description="Days of history (20 years default to capture 4-5 full market cycles, max 30 years)"), + force_retrain: bool = Query(False, description="Deprecated: reads never train models") ): """ Detect current market regime (Bull/Bear/Consolidation/Distribution). @@ -189,7 +195,7 @@ async def detect_regime( force_retrain=False ) - return RegimeDetectionResponse(**result) + return result except ValueError as e: logger.error(f"Validation error in regime detection: {e}") @@ -199,7 +205,7 @@ async def detect_regime( raise HTTPException(status_code=500, detail=f"Internal error: {str(e)}") -@router.get("/api/ml/bourse/correlation", response_model=CorrelationForecastResponse) +@router.get("/api/ml/bourse/correlation") async def forecast_correlation( symbols: str = Query("AAPL,MSFT,GOOGL", description="Comma-separated stock tickers"), lookback_days: int = Query(365, ge=90, le=1825, description="Days of history"), @@ -333,96 +339,19 @@ async def get_ml_dashboard( @router.post("/api/ml/bourse/train") -async def train_models( - symbols: str = Query("AAPL,MSFT,GOOGL", description="Comma-separated stock tickers"), - lookback_days: int = Query(730, ge=365, le=3650, description="Training data period"), - user: str = Depends(require_admin_role), -) -> dict: - """ - Train ML models on historical stock data. - - This endpoint trains: - - Volatility predictor (LSTM) - - Regime detector (HMM + NN) - - Correlation forecaster (Transformer) - - Training can take several minutes. - - Example: - POST /api/ml/bourse/train?symbols=AAPL,MSFT,GOOGL&lookback_days=730 - """ - try: - symbols_list = [s.strip().upper() for s in symbols.split(',')] - - logger.info(f"Training ML models for {len(symbols_list)} symbols (lookback={lookback_days}d)") - - results = [] - - for symbol in symbols_list: +async def train_models(symbols: str = Query("SPY,QQQ"), lookback_days: int = Query(730, ge=365, le=3650), user: str = Depends(require_admin_role)) -> dict: + import asyncio + from services.ml.risk_evaluation import evaluate + from services.ml.reliability import capability_service + results = [] + for asset in [s.strip().upper() for s in symbols.split(",")][:20]: + for horizon in (7,30): try: - # Train volatility model - vol_result = await stocks_ml_adapter.predict_volatility( - symbol=symbol, - lookback_days=lookback_days, - train_if_missing=True, - ) - if vol_result.get('model_type') != 'LSTM': - raise RuntimeError('Volatility training did not produce a verified model') - results.append({ - 'symbol': symbol, - 'model': 'volatility', - 'status': 'success', - 'model_type': vol_result.get('model_type', 'LSTM') - }) - except Exception as e: - logger.error(f"Failed to train volatility model for {symbol}: {e}") - results.append({ - 'symbol': symbol, - 'model': 'volatility', - 'status': 'failed', - 'error': str(e) - }) - - # Train regime detector (once, uses benchmark) - try: - regime_result = await stocks_ml_adapter.detect_market_regime( - benchmark="SPY", - lookback_days=lookback_days, - force_retrain=True, - ) - if regime_result.get('model_type') != 'causal_neural': - raise RuntimeError('Regime training did not produce a verified model') - results.append({ - 'model': 'regime_detector', - 'status': 'success', - 'current_regime': regime_result.get('current_regime', 'Unknown') - }) - except Exception as e: - logger.error(f"Failed to train regime detector: {e}") - results.append({ - 'model': 'regime_detector', - 'status': 'failed', - 'error': str(e) - }) - - results.append({ - 'model': 'correlation_forecaster', - 'status': 'unavailable', - 'reason': 'Temporal out-of-sample validation is not implemented for this model', - }) - - return { - 'status': 'completed_with_unavailable_models' if any( - item['status'] != 'success' for item in results - ) else 'completed', - 'timestamp': datetime.now().isoformat(), - 'training_results': results, - 'lookback_days': lookback_days - } - - except Exception as e: - logger.error(f"Error training models: {e}") - raise HTTPException(status_code=500, detail=f"Internal error: {str(e)}") + result = await asyncio.to_thread(evaluate, capability_service, "stocks", asset, horizon, publish=True) + except Exception as exc: + result = {"asset": asset, "horizon": horizon, "state": "not_evaluable", "reason": str(exc), "published": False} + results.append(result) + return {"success": True, "training_results": results, "user_id": user, "action": "explicit_frozen_risk_evaluation"} @router.get("/api/ml/bourse/model-info") @@ -599,7 +528,9 @@ async def get_regime_history( "total_samples": total_samples, "regime_distribution": regime_distribution, "regime_id_mapping": regime_id_counts, - "detection_method": "rule_based", # Indicate we use objective criteria + "detection_method": "rule_based", + "nature": "diagnostic", "retrospective": True, + "history_limitation": "Rule labels and minimum-duration smoothing reconstruct history using later observations; not real-time decision evidence", "events": events, "timestamp": datetime.now().isoformat() } @@ -851,277 +782,80 @@ async def get_portfolio_recommendations( async def get_market_opportunities( request: Request, user: str = Depends(get_required_user), - horizon: str = Query("medium", description="Time horizon: short (1-3M), medium (6-12M), long (2-3Y)"), - source: Optional[str] = Query(None, description="Data source: manual_bourse, saxobank_api"), - file_key: Optional[str] = Query(None, description="Saxo CSV file key"), - min_gap_pct: float = Query(5.0, ge=0.0, le=50.0, description="Minimum gap percentage to consider"), - sector_targets: Optional[str] = Query(None, description="Optional JSON object of industry target percentages totaling 100%") + horizon: str = Query("medium", description="Holding horizon; no validated forward-return forecast"), + source: Optional[str] = Query(None, description="Explicit active stock source"), + file_key: Optional[str] = Query(None, description="Selected Saxo CSV file key"), + min_gap_pct: float = Query(5.0, ge=0.0, le=50.0, description="Minimum certain sector deviation in percentage points"), + sector_targets: Optional[str] = Query(None, max_length=3000, description="Industry percentages totaling 100%"), + candidate_sector: str = Query("all", description="Explore sector ETFs or stocks within one economic sector"), ) -> dict: - """ - Get market opportunities outside current portfolio. - - Identifies sector gaps and suggests: - - Underweight/missing sectors - - Top opportunities to buy (stocks + ETFs) - - Positions to trim to fund opportunities - - Portfolio reallocation impact - - Example: - GET /api/bourse/opportunities?user_id=jack&horizon=medium&min_gap_pct=5.0 - """ + import asyncio + from services.ml.bourse.market_snapshot import load_snapshot + from services.ml.bourse.market_analysis import analyze + from services.ml.bourse.opportunity_scanner import parse_sector_targets + from api.utils.formatters import success_response try: - logger.info(f" Market opportunities requested (user={user}, horizon={horizon})") - - # Validate horizon - if horizon not in ["short", "medium", "long"]: - raise HTTPException( - status_code=400, - detail=f"Invalid horizon '{horizon}'. Must be: short, medium, or long" - ) - - from services.ml.bourse.opportunity_scanner import parse_sector_targets - try: - target_allocations = parse_sector_targets(sector_targets) - except ValueError as exc: - raise HTTPException(status_code=400, detail=str(exc)) from exc - - # Import httpx at the beginning (may be used later) - import httpx - - # 1. Get user positions from Manual/API/CSV - if source == "manual_bourse": - # Manual mode: load from Sources V2 - from services.sources import source_registry - from pathlib import Path - - project_root = Path(__file__).parent.parent - manual_source = source_registry.get_source("manual_bourse", user, project_root) - - if not manual_source: - return { - "gaps": [], - "opportunities": [], - "suggested_sales": [], - "impact": {}, - "message": "Manual bourse source not available", - "generated_at": datetime.now().isoformat() - } - - result = await manual_source.get_balances() - # get_balances() returns List[BalanceItem] directly, not a dict - items = result if isinstance(result, list) else [] - - # Transform BalanceItem (dataclass) to positions format - positions = [ - { - "symbol": item.symbol, - "asset_name": item.alias or item.symbol, - "quantity": float(item.amount or 0), - "market_value": float(item.value_usd or 0), - "market_value_usd": float(item.value_usd or 0), - "asset_class": item.asset_class or "EQUITY", - "currency": item.currency or "USD", - "broker": item.location or "Manual" - } - for item in items - ] - - else: - # API or CSV mode: use HTTP endpoints - async with httpx.AsyncClient(timeout=30.0) as client: - if source == "saxobank_api": - # API mode: use api-positions endpoint - positions_url = f"{API_BASE_URL}/api/saxo/api-positions" - pos_response = await client.get( - positions_url, - headers=_forward_authenticated_headers(request, user) - ) - # Handle 401 Unauthorized specifically (Saxo not connected) - if pos_response.status_code == 401: - raise HTTPException( - status_code=401, - detail="Saxo Bank not connected - please connect first via Dashboard" - ) - pos_response.raise_for_status() - positions_data = pos_response.json() - positions = positions_data.get("data", {}).get("positions", []) - else: - # CSV mode: use positions endpoint - positions_url = f"{API_BASE_URL}/api/saxo/positions" - - pos_response = await client.get( - positions_url, - headers=_forward_authenticated_headers(request, user), - params={"file_key": file_key} if file_key else None, - ) - pos_response.raise_for_status() - positions_data = pos_response.json() - positions = positions_data.get("positions", []) - - if not positions: - return { - "gaps": [], - "opportunities": [], - "suggested_sales": [], - "impact": {}, - "message": "No positions found", - "generated_at": datetime.now().isoformat() - } - - # Debug: Log position format - if positions and len(positions) > 0: - logger.debug(f" Sample position keys: {list(positions[0].keys())}") - logger.debug(f" Sample position: {positions[0]}") - - # 2. Scan for opportunities - from services.ml.bourse.opportunity_scanner import OpportunityScanner - scanner = OpportunityScanner() - - scan_result = await scanner.scan_opportunities( - positions=positions, - horizon=horizon, - min_gap_pct=min_gap_pct, - target_allocations=target_allocations, - ) - - gaps = scan_result.get("top_gaps", []) - - # 3. Build opportunities list (for now, use sector ETFs) - from services.ml.bourse.sector_analyzer import SectorAnalyzer - from services.ml.bourse.opportunity_scanner import SECTOR_MAPPING - sector_analyzer = SectorAnalyzer() - - opportunities = [] - held_symbols = { - str(p.get("symbol") or p.get("instrument_id") or p.get("ticker") or "") - .split(":", 1)[0].upper() - for p in positions - } - for gap in gaps: - sector = gap.get("sector") - etf = gap.get("etf") - gap_pct = gap.get("gap_pct", 0) - - # Get top stocks in sector (ETF + top 6 individual stocks with scores) - # Increased from 3 to 6 to include international blue-chips (US + Europe + Asia) - top_stocks = await sector_analyzer.get_top_stocks_in_sector( - sector_etf=etf, - top_n=6, - horizon=horizon, - score_individually=True # Enable individual stock scoring - ) - - # Calculate capital needed based on gap and portfolio size - # Note: Saxo positions use "market_value" field (already in USD) - total_value = sum(p.get("market_value", 0) or p.get("market_value_usd", 0) for p in positions) - capital_needed = (gap_pct / 100) * total_value - - if top_stocks: - for stock in top_stocks: - stock_symbol = str(stock.get("symbol") or "").split(":", 1)[0].upper() - if not stock_symbol or stock_symbol in held_symbols: - continue - if stock.get("type") != "ETF" and stock.get("composite_score") is None: - # A sector score is not evidence for an unpriced stock. - continue - if stock.get("type") != "ETF": - reported_sector = stock.get("reported_sector") - if SECTOR_MAPPING.get(reported_sector) != sector: - # Static candidate lists can drift or contain errors. - continue - # Use individual stock scores if available, otherwise fall back to sector scores - stock_score = _finite_number(stock.get("composite_score"), gap.get("score")) - stock_momentum = _finite_number(stock.get("momentum_score"), gap.get("momentum_score") if stock.get("type") == "ETF" else None) - stock_value = _finite_number(stock.get("value_score"), gap.get("value_score") if stock.get("type") == "ETF" else None) - stock_diversification = _finite_number(stock.get("diversification_score"), None) - stock_confidence = min( - 1.0, - max(0.0, _finite_number(stock.get("confidence"), _finite_number(gap.get("confidence"), 0.0))), - ) - - opportunities.append({ - "symbol": stock.get("symbol"), - "name": stock.get("name", f"{sector} ETF"), - "sector": sector, - "type": stock.get("type", "ETF"), - "score": stock_score, - "confidence": stock_confidence, - "action": "REVIEW", - "horizon": horizon, - "capital_needed": round(capital_needed, 2), - "capital_needed_is_sector_budget": True, - "confidence_kind": "data_coverage", - "score_kind": "historical_risk_adjusted_screen", - "score_components_available": [name for name, value in - (("momentum", stock_momentum), ("value", stock_value), ("diversification", stock_diversification)) - if value is not None], - "rationale": stock.get("rationale", f"{sector} sector gap: {gap_pct:.1f}% underweight"), - "momentum_score": stock_momentum, - "value_score": stock_value, - "diversification_score": stock_diversification - }) - - # Sort opportunities by score (descending) - opportunities.sort(key=lambda x: x.get("score", 0), reverse=True) - - # Limit to top 35 (to show ETF + international stock recommendations) - # Increased from 20 to 35 to accommodate 6 stocks per gap (was 3) - opportunities = opportunities[:35] - - # 4. Detect sales to fund opportunities - from services.ml.bourse.portfolio_gap_detector import PortfolioGapDetector - gap_detector = PortfolioGapDetector() - - # Calculate capital needed per SECTOR (not per stock suggestion) - # Each stock in a gap carries the full gap capital, so deduplicate by sector - seen_sectors = set() - total_capital_needed = 0 - for opp in opportunities: - sector = opp.get("sector") - if sector not in seen_sectors: - seen_sectors.add(sector) - total_capital_needed += opp.get("capital_needed", 0) - - sales_result = await gap_detector.detect_sales( - positions=positions, - opportunities=opportunities, - total_capital_needed=total_capital_needed - ) - - suggested_sales = sales_result.get("suggested_sales", []) - - # 5. Calculate reallocation impact - impact = await gap_detector.calculate_reallocation_impact( - current_positions=positions, - suggested_sales=suggested_sales, - opportunities=opportunities - ) - - return { - "gaps": gaps, - "opportunities": opportunities, - "suggested_sales": suggested_sales, - "impact": impact, - "summary": { - "total_gaps": len(gaps), - "total_opportunities": len(opportunities), - "total_sales": len(suggested_sales), - "capital_needed": total_capital_needed, - "capital_freed": sales_result.get("total_freed", 0), - "sufficient_capital": sales_result.get("sufficient", False) - }, - "horizon": horizon, - "candidate_universe": "curated_stocks_and_sector_etfs", - "horizon_details": scan_result.get("horizon_details"), - "target_source": scan_result.get("target_source"), - "target_allocations": scan_result.get("target_allocations"), - "classification_coverage": scan_result.get("classification_coverage"), - "unclassified_pct": scan_result.get("unclassified_pct"), - "generated_at": datetime.now().isoformat() - } - + targets = parse_sector_targets(sector_targets) + snapshot = await load_snapshot(user, source, file_key, request, API_BASE_URL) + result = await asyncio.wait_for(analyze(snapshot, horizon, candidate_sector, targets, min_gap_pct), 105) + response = success_response(result) + response.headers['Cache-Control'] = 'no-store' + return response + except (ValueError, FileNotFoundError) as exc: + raise HTTPException(status_code=422, detail=str(exc)) from None + except asyncio.TimeoutError: + raise HTTPException(status_code=504, detail="The data checks timed out; no partial allocation conclusion is returned") from None except HTTPException: raise - except Exception as e: - logger.error(f"Error getting market opportunities: {e}", exc_info=True) - raise HTTPException(status_code=500, detail=f"Internal error: {str(e)}") + except Exception: + logger.error("Market Opportunities analysis failed without publishing private payloads") + raise HTTPException(status_code=503, detail="The selected-source analysis is unavailable") from None + + +@router.post("/api/bourse/opportunities/scenario") +async def simulate_market_opportunities(request: Request, user: str = Depends(get_required_user)): + """Read-only arithmetic. No persistence, broker calls, orders, or background work.""" + import asyncio + from services.ml.bourse.market_snapshot import load_snapshot, number + from services.ml.bourse.market_analysis import analyze, simulate, scenario_history, PublicMarketData + from services.ml.bourse.opportunity_scanner import parse_sector_targets + from api.utils.formatters import success_response + try: + body = await request.body() + if len(body) > 16000: + raise ValueError('Scenario request is too large') + params = json.loads(body) + allowed = {'source', 'file_key', 'horizon', 'candidate_sector', 'sector_targets', 'min_gap_pct', 'scenario'} + if not isinstance(params, dict) or set(params) - allowed: + raise ValueError('Invalid scenario request fields') + gap = number(params.get('min_gap_pct', 5), 'Sector deviation threshold') + if not 0 <= gap <= 50: + raise ValueError('Sector deviation threshold must be between 0 and 50') + scenario = params.get('scenario') + if not isinstance(scenario, dict): + raise ValueError('A manual scenario is required') + if 'include_history' in scenario and not isinstance(scenario['include_history'], bool): + raise ValueError('History option must be a boolean') + snapshot = await load_snapshot(user, params.get('source'), params.get('file_key'), request, API_BASE_URL) + async def calculate(): + provider = PublicMarketData() + analysis = await analyze(snapshot, params.get('horizon', 'medium'), params.get('candidate_sector', 'all'), + parse_sector_targets(params.get('sector_targets')), gap, provider) + result = simulate(analysis, scenario) + if scenario.get('include_history'): + result['historical_risk'] = await scenario_history(analysis, result, provider) + # Private holdings are already in the authenticated scan; only scenario results are returned here. + result.pop('positions_after', None) + return dict(context=analysis['context'], snapshot_id=analysis['snapshot_id'], scenario=result) + response = success_response(await asyncio.wait_for(calculate(), 150)) + response.headers['Cache-Control'] = 'no-store' + return response + except (ValueError, FileNotFoundError, TypeError) as exc: + raise HTTPException(status_code=422, detail=str(exc)) from None + except asyncio.TimeoutError: + raise HTTPException(status_code=504, detail='Scenario data checks timed out; no estimated result is returned') from None + except HTTPException: + raise + except Exception: + logger.error('Market Opportunities scenario failed without publishing private payloads') + raise HTTPException(status_code=503, detail='Scenario calculation is unavailable') from None diff --git a/api/ml_crypto_endpoints.py b/api/ml_crypto_endpoints.py index c4c002ad..ba1ef3b3 100644 --- a/api/ml_crypto_endpoints.py +++ b/api/ml_crypto_endpoints.py @@ -6,7 +6,8 @@ - GET /api/ml/crypto/regime-history: Historical regime timeline """ -from fastapi import APIRouter, Query, HTTPException +from fastapi import APIRouter, Query, HTTPException, Depends +from api.deps import get_required_user from typing import Optional, List, Dict, Any import pandas as pd from datetime import datetime @@ -18,7 +19,7 @@ from services.price_history import price_history from services.regime_constants import REGIME_NAMES, smooth_regime_sequence -router = APIRouter() +router = APIRouter(dependencies=[Depends(get_required_user)]) logger = logging.getLogger(__name__) # Simple in-memory cache for regime history (TTL: 4 hours) @@ -103,10 +104,11 @@ async def get_crypto_regime( 'benchmark': symbol, 'lookback_days': lookback_days, 'prediction_date': result['prediction_date'], - 'model_metadata': result['model_metadata'] + 'model_metadata': result['model_metadata'], + 'data_as_of': result.get('data_as_of') } - logger.info(f"Regime detected: {result['regime_name']} (method={result['detection_method']}, confidence={result['confidence']:.2f})") + response_data.update({key: result.get(key) for key in ("nature", "availability", "probability_kind", "hmm_state", "hmm_state_features", "economic_mapping_verified", "rule_diagnostic")}) return success_response(response_data) @@ -167,7 +169,7 @@ async def get_crypto_regime_history( # Load symbol-specific HMM model for fallback model_file = f"{symbol.lower()}_regime_hmm.pkl" if not detector.load_model(model_file): - await detector.train_hmm(symbol=symbol, lookback_days=3650) + return error_response("HMM history unavailable: explicit administrator training is required", code=503) features_scaled = detector.scaler.transform(features_df[detector.feature_columns]) hmm_labels = detector.hmm_model.predict(features_scaled) @@ -204,12 +206,13 @@ async def get_crypto_regime_history( else: # Fallback to HMM hmm_label = int(hmm_labels[i]) - regime_names.append(detector.regime_names[hmm_label]) - regime_ids.append(hmm_label) + regime_names.append(f"State {chr(65+hmm_label)}") + regime_ids.append(4 + hmm_label) # Smooth regime sequence to remove short-lived transitions (<7 days) regime_ids = smooth_regime_sequence(regime_ids, min_duration=7) - regime_names = [REGIME_NAMES[rid] for rid in regime_ids] + label_mapping = {**dict(enumerate(REGIME_NAMES)), **{4+i: f"State {chr(65+i)}" for i in range(detector.num_regimes)}} + regime_names = [label_mapping[rid] for rid in regime_ids] # Format response response_data = { @@ -219,7 +222,9 @@ async def get_crypto_regime_history( 'regime_ids': regime_ids, 'symbol': symbol, 'lookback_days': lookback_days, - 'regime_id_mapping': {i: name for i, name in enumerate(detector.regime_names)}, + 'regime_id_mapping': label_mapping, + 'economic_mapping_verified': False, + 'id_encoding': 'Rule diagnostics 0-3; unmapped HMM states 4-7', 'events': get_btc_events(features_df.index.min(), features_df.index.max()), 'note': 'Hybrid detection (rule-based + HMM fallback) with 7-day minimum duration smoothing.' } @@ -228,6 +233,8 @@ async def get_crypto_regime_history( _regime_history_cache[cache_key] = (response_data, time.time()) logger.debug(f"[Cache STORE] Cached regime history for {cache_key}") + response_data["retrospective"] = True + response_data["history_limitation"] = "Full-sequence HMM and smoothing use later observations; not real-time decision evidence" return success_response(response_data) except Exception as e: @@ -327,249 +334,18 @@ def _detect_regime_rule_based_optimized( @router.get("/regime-forecast") -async def get_crypto_regime_forecast( - symbol: str = Query("BTC", description="Cryptocurrency symbol"), - lookback_days: int = Query(90, ge=30, le=365, description="Context window (days)") -): - """ - Get Bitcoin regime FORECAST with recent context and future predictions. - - FOCUS: Where we're GOING (predictive) rather than where we were (historical). - - Returns: - - Recent 90-day context (for trend visualization) - - Current regime with high confidence (hybrid rules + HMM) - - Transition probabilities (7/30-day forecast) - - Momentum indicators (drawdown/volatility trends) - - Conditional scenarios (if +10%, if -10%) - - Args: - symbol: Crypto symbol (default: BTC) - lookback_days: Context window (30-365 days, default 90) - - Returns: - { - "current_regime": {...}, - "recent_context": { - "dates": [...], # Last 90 days - "prices": [...], - "regimes": [...] - }, - "forecast": { - "transition_probabilities": {...}, - "momentum_indicators": {...}, - "scenarios": [...] - } - } - """ - try: - logger.info(f"GET /api/ml/crypto/regime-forecast - symbol={symbol}, lookback_days={lookback_days}") - - # Get current regime using existing endpoint (already works well!) - detector = BTCRegimeDetector() - current_regime_result = await detector.predict_regime(symbol=symbol, lookback_days=3650) - - # Get recent context for trend visualization (last N days) - history = price_history.get_cached_history(symbol, days=lookback_days) - if history is None or len(history) == 0: - return error_response(f"No historical data available for {symbol}", code=404) - - data = pd.DataFrame(history, columns=['timestamp', 'close']) - data['timestamp'] = pd.to_datetime(data['timestamp'], unit='s') - data.set_index('timestamp', inplace=True) - - # Prepare features for recent context - features_df = await detector.prepare_regime_features(symbol=symbol, lookback_days=lookback_days) - - if len(features_df) == 0: - return error_response("Insufficient data for forecast", code=400) - - # Get recent regime timeline (simplified, just for context visualization) - model_file = f"{symbol.lower()}_regime_hmm.pkl" - if not detector.load_model(model_file): - await detector.train_hmm(symbol=symbol, lookback_days=3650) - - features_scaled = detector.scaler.transform(features_df[detector.feature_columns]) - hmm_predictions = detector.hmm_model.predict(features_scaled) - recent_regimes = [detector.regime_names[int(pred)] for pred in hmm_predictions] - - # Calculate momentum indicators (trend direction) - latest_features = features_df.iloc[-1] - last_30d_features = features_df.tail(30) - - drawdown_trend = "improving" if last_30d_features['drawdown_from_peak'].diff().mean() > 0 else "worsening" - volatility_trend = "decreasing" if last_30d_features['market_volatility'].diff().mean() < 0 else "increasing" - - # Calculate transition probabilities (simplified heuristic) - current_regime_name = current_regime_result['regime_name'] - current_drawdown = latest_features['drawdown_from_peak'] - current_volatility = latest_features['market_volatility'] - current_trend = latest_features.get('trend_30d', 0) - - # Simple scenario-based forecasting - scenarios = [] - - # Scenario 1: If price +10% - new_dd_up = current_drawdown + 0.10 - if new_dd_up > -0.05: - likely_regime_up = "Bull Market" - elif new_dd_up > -0.20: - likely_regime_up = "Expansion" - else: - likely_regime_up = current_regime_name - scenarios.append({ - "scenario": "Price +10%", - "price_change": "+10%", - "likely_regime": likely_regime_up, - "probability": 0.7 if likely_regime_up != current_regime_name else 0.9 - }) - - # Scenario 2: If price -10% - new_dd_down = current_drawdown - 0.10 - if new_dd_down < -0.30: - likely_regime_down = "Bear Market" - elif new_dd_down < -0.10: - likely_regime_down = "Correction" - else: - likely_regime_down = current_regime_name - scenarios.append({ - "scenario": "Price -10%", - "price_change": "-10%", - "likely_regime": likely_regime_down, - "probability": 0.7 if likely_regime_down != current_regime_name else 0.9 - }) - - # Scenario 3: If trend continues - trend_direction = "up" if current_trend > 0 else "down" - scenarios.append({ - "scenario": f"Trend continues ({trend_direction})", - "price_change": f"{current_trend*100:+.1f}% (30d momentum)", - "likely_regime": current_regime_name, - "probability": 0.8 - }) - - # Format response - response_data = { - 'current_regime': { - 'regime': current_regime_result['regime_name'], - 'confidence': current_regime_result['confidence'], - 'method': current_regime_result['detection_method'], - 'reason': current_regime_result.get('rule_reason', 'HMM prediction'), - 'probabilities': current_regime_result.get('regime_probabilities', {}) - }, - 'recent_context': { - 'dates': features_df.index.strftime('%Y-%m-%d').tolist(), - 'prices': data.loc[features_df.index, 'close'].tolist(), - 'regimes': recent_regimes, - 'period_days': lookback_days - }, - 'momentum_indicators': { - 'drawdown_current': float(current_drawdown), - 'drawdown_trend': drawdown_trend, - 'volatility_current': float(current_volatility), - 'volatility_trend': volatility_trend, - 'trend_30d': float(current_trend), - 'trend_direction': trend_direction - }, - 'scenarios': scenarios, - 'symbol': symbol, - 'forecast_date': datetime.now().isoformat() - } - - logger.info(f"Regime forecast generated: {current_regime_name} (method={current_regime_result['detection_method']})") - - return success_response(response_data) - - except ValueError as e: - logger.error(f"ValueError in get_crypto_regime_history: {e}") - return error_response(str(e), code=400) - except Exception as e: - logger.error(f"Error in get_crypto_regime_history: {e}", exc_info=True) - return error_response(f"Failed to build regime timeline: {str(e)}", code=500) +async def get_crypto_regime_forecast(symbol: str = Query("BTC"), lookback_days: int = Query(90, ge=30, le=365)): + return success_response({"available": False, "availability": "Experimental", "nature": "forecast", + "forecast": None, "symbol": symbol, + "reason": "Regime transition and conditional scenario probabilities have no independent forecasting validation. Use current rule diagnostics and HMM latent-state posteriors separately."}) @router.get("/regime/validate") async def validate_regime_detector( symbol: str = Query("BTC", description="Cryptocurrency symbol") ): - """ - Validate regime detector on known Bitcoin bear markets. - - Tests: - - 2014-2015: Mt.Gox crash (-85%) - - 2018: Crypto Winter (-84%) - - 2022: Luna/FTX (-77%) - - Returns validation report with recall metrics. - """ - try: - logger.info(f"GET /api/ml/crypto/regime/validate - symbol={symbol}") - - # Known bear market periods (manual labeling) - known_bear_markets = [ - {'name': '2014-2015 Mt.Gox', 'start': '2014-01-01', 'end': '2015-01-14', 'max_dd': -0.85}, - {'name': '2018 Crypto Winter', 'start': '2018-01-01', 'end': '2018-12-15', 'max_dd': -0.84}, - {'name': '2022 Luna/FTX', 'start': '2022-05-01', 'end': '2022-11-21', 'max_dd': -0.77} - ] - - detector = BTCRegimeDetector() - - # Validate each bear market - results = [] - for bear in known_bear_markets: - try: - # Get data for this period - start = pd.to_datetime(bear['start']) - end = pd.to_datetime(bear['end']) - days = (end - start).days - - # Predict regime for this period - regime = await detector.predict_regime(symbol=symbol, lookback_days=days + 365) - - detected_as_bear = regime['regime_name'] == 'Bear Market' - confidence = regime['confidence'] - - results.append({ - 'period': bear['name'], - 'expected': 'Bear Market', - 'detected': regime['regime_name'], - 'correct': detected_as_bear, - 'confidence': confidence, - 'method': regime['detection_method'], - 'max_drawdown': bear['max_dd'] - }) - - logger.info(f"{bear['name']}: Detected as {regime['regime_name']} ({'OK' if detected_as_bear else 'Error'})") - - except Exception as e: - logger.error(f"Failed to validate {bear['name']}: {e}") - results.append({ - 'period': bear['name'], - 'expected': 'Bear Market', - 'detected': 'Error', - 'correct': False, - 'error': str(e) - }) - - # Calculate metrics - total = len(results) - correct = sum(1 for r in results if r.get('correct')) - recall = correct / total if total > 0 else 0 - - validation_report = { - 'validation_date': datetime.now().isoformat(), - 'symbol': symbol, - 'total_tests': total, - 'correct_detections': correct, - 'bear_market_recall': recall, - 'results': results, - 'status': 'PASS' if recall >= 0.90 else 'FAIL' - } + """Legacy retrospective validation is unavailable without dated inference.""" + return success_response({"symbol": symbol, "availability": "Unavailable", "validation_state": "not_evaluable", + "bear_market_recall": None, "results": [], "status": "Unavailable", + "reason": "The legacy test queried current observations for historical events. It cannot establish historical recall or real-time decision quality."}) - logger.info(f"Validation complete: {correct}/{total} bear markets detected (recall={recall:.1%})") - - return success_response(validation_report) - - except Exception as e: - logger.error(f"Error in validate_regime_detector: {e}", exc_info=True) - return error_response(f"Validation failed: {str(e)}", code=500) diff --git a/api/portfolio_optimization_endpoints.py b/api/portfolio_optimization_endpoints.py index 035805c2..41526075 100644 --- a/api/portfolio_optimization_endpoints.py +++ b/api/portfolio_optimization_endpoints.py @@ -19,7 +19,8 @@ ) from services.price_history import get_cached_history from services.price_utils import price_history_to_dataframe, validate_price_data -from connectors import cointracking as ct_file +from api.deps import get_required_user +from api.unified_data import get_unified_filtered_balances from services.pricing import FIAT_STABLE_FIXED import re @@ -91,7 +92,8 @@ async def optimize_portfolio( request: OptimizationRequest, source: str = Query("cointracking", description="Data source"), min_usd: float = Query(100, description="Minimum USD value to include"), - min_history_days: int = Query(365, description="Minimum history days required") + min_history_days: int = Query(365, description="Minimum history days required"), + user: str = Depends(get_required_user) ): """ Optimize portfolio allocation using advanced Markowitz optimization @@ -134,80 +136,8 @@ async def optimize_portfolio( optimizer = PortfolioOptimizer() - # Get current portfolio using the same logic as /balances/current - if source == "stub": - balances_response = { - "source_used": "stub", - "items": [ - {"symbol": "BTC", "value_usd": 105000.0, "amount": 2.5, "location": "Kraken"}, - {"symbol": "ETH", "value_usd": 47250.0, "amount": 15.75, "location": "Binance"}, - {"symbol": "USDC", "value_usd": 25000.0, "amount": 25000.0, "location": "Coinbase"}, - {"symbol": "SOL", "value_usd": 23400.0, "amount": 180.0, "location": "Phantom"}, - {"symbol": "AVAX", "value_usd": 13500.0, "amount": 450.0, "location": "Ledger"}, - ] - } - elif source == "cointracking_api": - # Try to use CoinTracking API data via unified connector - try: - result = await ct_file.get_unified_balances_by_exchange("cointracking_api") - items = [] - if 'detailed_holdings' in result: - for exchange, holdings in (result.get('detailed_holdings') or {}).items(): - for holding in holdings: - if holding.get('value_usd', 0) >= min_usd: - items.append(holding) - elif 'items' in result: - for item in result.get('items') or []: - if item.get('value_usd', 0) >= min_usd: - items.append(item) - balances_response = {"source_used": "cointracking_api", "items": items} - except Exception as e: - logger.warning(f"CoinTracking API failed: {e}, falling back to CSV") - # Fallback to CSV data - try: - result = ct_file.get_balances_by_exchange_from_csv() - items = [] - if 'detailed_holdings' in result: - for exchange, holdings in result['detailed_holdings'].items(): - for holding in holdings: - if holding.get('value_usd', 0) >= min_usd: - items.append(holding) - balances_response = {"source_used": "cointracking_csv_fallback", "items": items} - except Exception as e: - logger.warning(f"CSV fallback failed: {e}, using stub data") - # Final fallback to stub - balances_response = { - "source_used": "stub_fallback", - "items": [ - {"symbol": "BTC", "value_usd": 50000.0, "amount": 1.0, "location": "Kraken"}, - {"symbol": "ETH", "value_usd": 30000.0, "amount": 10.0, "location": "Binance"}, - {"symbol": "SOL", "value_usd": 10000.0, "amount": 100.0, "location": "Phantom"} - ] - } - else: - # Default cointracking = CSV data - try: - result = ct_file.get_balances_by_exchange_from_csv() - # Convert detailed_holdings to flat items list - items = [] - if 'detailed_holdings' in result: - for exchange, holdings in result['detailed_holdings'].items(): - for holding in holdings: - if holding.get('value_usd', 0) >= min_usd: - items.append(holding) - balances_response = {"source_used": "cointracking", "items": items} - except Exception as e: - logger.warning(f"Default CSV balances failed: {e}, using stub data") - # Fallback to stub data - balances_response = { - "source_used": "stub", - "items": [ - {"symbol": "BTC", "value_usd": 50000.0, "amount": 1.0, "location": "Kraken"}, - {"symbol": "ETH", "value_usd": 30000.0, "amount": 10.0, "location": "Binance"}, - {"symbol": "SOL", "value_usd": 10000.0, "amount": 100.0, "location": "Phantom"} - ] - } - + balances_response = await get_unified_filtered_balances(user_id=user, source=source, min_usd=min_usd) + if not balances_response.get("items"): raise HTTPException(status_code=400, detail="No portfolio data found") @@ -470,6 +400,8 @@ def canonical_symbol(sym: str) -> str: optimization_details=optimization_details ) + except HTTPException: + raise except Exception as e: logger.error(f"Portfolio optimization failed: {str(e)}", exc_info=True) raise HTTPException(status_code=500, detail=f"Optimization failed: {str(e)}") @@ -479,30 +411,11 @@ async def analyze_portfolio( source: str = Query("cointracking", description="Data source: cointracking|stub"), min_usd: float = Query(50, description="Ignore holdings below this value for stats"), target_assets: int = Query(30, description="Desired number of assets to optimize"), + user: str = Depends(get_required_user), ): """Analyze current portfolio and suggest optimization parameters.""" try: - # Load balances (CSV or stub) - if source == "stub": - balances_response = { - "source_used": "stub", - "items": [ - {"symbol": "BTC", "value_usd": 105000.0, "amount": 2.5, "location": "Kraken"}, - {"symbol": "ETH", "value_usd": 47250.0, "amount": 15.75, "location": "Binance"}, - {"symbol": "USDC", "value_usd": 25000.0, "amount": 25000.0, "location": "Coinbase"}, - {"symbol": "SOL", "value_usd": 23400.0, "amount": 180.0, "location": "Phantom"}, - {"symbol": "AVAX", "value_usd": 13500.0, "amount": 450.0, "location": "Ledger"}, - ] - } - else: - result = ct_file.get_balances_by_exchange_from_csv() - items = [] - if 'detailed_holdings' in result: - for _, holdings in result['detailed_holdings'].items(): - for holding in holdings: - if holding.get('value_usd', 0) >= min_usd: - items.append(holding) - balances_response = {"source_used": "cointracking", "items": items} + balances_response = await get_unified_filtered_balances(user_id=user, source=source, min_usd=min_usd) items = balances_response.get("items") or [] if not items: @@ -817,7 +730,8 @@ async def optimize_portfolio_advanced( source: str = Query("cointracking", description="Data source"), min_usd: float = Query(100, description="Minimum USD value to include"), min_history_days: int = Query(365, description="Minimum history days required"), - risk_free_rate: float = Query(0.02, description="Risk-free rate for Sharpe calculation") + risk_free_rate: float = Query(0.02, description="Risk-free rate for Sharpe calculation"), + user: str = Depends(get_required_user) ): """ Advanced portfolio optimization with sophisticated algorithms: @@ -852,47 +766,7 @@ async def optimize_portfolio_advanced( optimizer = PortfolioOptimizer() optimizer.risk_free_rate = risk_free_rate - # Get portfolio data (same logic as standard optimization) - if source == "stub": - balances_response = { - "source_used": "stub", - "items": [ - {"symbol": "BTC", "value_usd": 105000.0, "amount": 2.5, "location": "Kraken"}, - {"symbol": "ETH", "value_usd": 47250.0, "amount": 15.75, "location": "Binance"}, - {"symbol": "USDC", "value_usd": 25000.0, "amount": 25000.0, "location": "Coinbase"}, - {"symbol": "SOL", "value_usd": 23400.0, "amount": 180.0, "location": "Phantom"}, - {"symbol": "AVAX", "value_usd": 13500.0, "amount": 450.0, "location": "Ledger"}, - ] - } - elif source == "cointracking_api": - try: - result = await ct_file.get_unified_balances_by_exchange("cointracking_api") - items = [] - if 'detailed_holdings' in result: - for exchange, holdings in (result.get('detailed_holdings') or {}).items(): - for holding in holdings: - if holding.get('value_usd', 0) >= min_usd: - items.append(holding) - balances_response = {"source_used": "cointracking_api", "items": items} - except Exception as e: - logger.warning(f"CoinTracking API failed: {e}, falling back to CSV") - result = ct_file.get_balances_by_exchange_from_csv() - items = [] - if 'detailed_holdings' in result: - for exchange, holdings in result['detailed_holdings'].items(): - for holding in holdings: - if holding.get('value_usd', 0) >= min_usd: - items.append(holding) - balances_response = {"source_used": "cointracking_csv_fallback", "items": items} - else: - result = ct_file.get_balances_by_exchange_from_csv() - items = [] - if 'detailed_holdings' in result: - for exchange, holdings in result['detailed_holdings'].items(): - for holding in holdings: - if holding.get('value_usd', 0) >= min_usd: - items.append(holding) - balances_response = {"source_used": "cointracking", "items": items} + balances_response = await get_unified_filtered_balances(user_id=user, source=source, min_usd=min_usd) if not balances_response.get("items"): raise HTTPException(status_code=400, detail="No portfolio data found") @@ -1133,40 +1007,6 @@ def safe_round(value, decimals=4): raise HTTPException(status_code=500, detail=f"Advanced optimization failed: {str(e)}") @router.post("/backtest") -async def backtest_optimization( - request: OptimizationRequest, - test_periods: int = Query(12, description="Number of monthly rebalancing periods to test"), - source: str = Query("cointracking"), - min_usd: float = Query(100) -) -> dict: - """ - Backtest optimization strategy over historical periods - """ - - try: - # This would implement rolling optimization backtest - # For now, return a placeholder structure - - results = { - "success": True, - "backtest_summary": { - "test_periods": test_periods, - "total_return": 0.15, # 15% annual - "volatility": 0.28, # 28% annual - "sharpe_ratio": 0.54, - "max_drawdown": -0.35, # -35% - "win_rate": 0.67, # 67% winning months - "average_rebalancing_cost": 0.002 # 0.2% per rebalance - }, - "period_returns": [ - {"period": i, "return": np.random.normal(0.01, 0.08)} - for i in range(test_periods) - ], - "note": "Backtesting implementation in progress. This is sample data." - } - - return results - - except Exception as e: - logger.error(f"Backtesting failed: {str(e)}") - raise HTTPException(status_code=500, detail=f"Backtesting failed: {str(e)}") +async def backtest_optimization(request: OptimizationRequest, user: str = Depends(get_required_user)) -> dict: + """An unimplemented backtest must never return simulated performance.""" + raise HTTPException(status_code=501, detail="Optimization backtesting is unavailable: no historical implementation has been validated") diff --git a/api/risk_bourse_endpoints.py b/api/risk_bourse_endpoints.py index 7e57e340..46fd553e 100644 --- a/api/risk_bourse_endpoints.py +++ b/api/risk_bourse_endpoints.py @@ -871,7 +871,8 @@ async def get_dividend_analysis( dividends = None try: import yfinance as yf - ticker_obj = yf.Ticker(ticker) + from services.ml.portfolio_context import stock_symbol + ticker_obj = yf.Ticker(stock_symbol(ticker)) dividends = ticker_obj.dividends except Exception as e: logger.warning(f"Could not fetch dividends for {ticker}: {e}") diff --git a/api/risk_endpoints.py b/api/risk_endpoints.py index 34dbe3b4..2ee6158d 100644 --- a/api/risk_endpoints.py +++ b/api/risk_endpoints.py @@ -30,6 +30,11 @@ _risk_cache = {} +def risk_dashboard_cache_key(**context) -> str: + """Separate users, sources and every calculation window/coverage option.""" + return repr(("risk_dashboard_v3", tuple(sorted(context.items())))) + + @router.get("/portfolio-reference") async def get_portfolio_reference( source: str = Query(..., description="Explicit portfolio data source"), @@ -937,7 +942,7 @@ async def get_risk_dashboard( # Check cache (TTL: 30 min = 1800 seconds, optimized per CACHE_TTL_OPTIMIZATION.md) # FIX: Include _csv_hint in cache key to invalidate when CSV changes (Oct 2025) csv_hint_part = f":{_csv_hint}" if _csv_hint else "" - cache_key = f"risk_dashboard:{user}:{source}:{min_usd}:{risk_version}{csv_hint_part}" + cache_key = risk_dashboard_cache_key(user=user, source=source, min_usd=min_usd, risk_version=risk_version, csv_hint=csv_hint_part, price_history_days=price_history_days, lookback_days=lookback_days, use_dual_window=use_dual_window, min_history_days=min_history_days, min_coverage_pct=min_coverage_pct, min_asset_count=min_asset_count) logger.info(f" Risk dashboard request: user={user}, source={source}, csv_hint={_csv_hint}, cache_key={cache_key}") diff --git a/api/schemas/ml_contract.py b/api/schemas/ml_contract.py index c48e858f..7ddf5fde 100644 --- a/api/schemas/ml_contract.py +++ b/api/schemas/ml_contract.py @@ -1,213 +1,259 @@ -""" -Contrat prédictif unifié pour les modèles ML -Schémas standardisés pour entrées/sorties ML avec gestion d'incertitude et métadonnées -""" - -from pydantic import BaseModel, Field -from typing import Dict, List, Optional, Any, Union, Literal -from datetime import datetime -from enum import Enum - - -# === TYPES DE BASE === - -class ModelType(str, Enum): - """Types de modèles supportés""" - VOLATILITY = "volatility" - SENTIMENT = "sentiment" - REGIME = "regime" - CORRELATION = "correlation" - RISK = "risk" - BLENDED = "blended" - - -class Horizon(str, Enum): - """Horizons temporels standardisés""" - H1 = "1h" - H4 = "4h" - D1 = "1d" - D7 = "7d" - D30 = "30d" - D90 = "90d" - - -class ConfidenceLevel(str, Enum): - """Confidence levels for intervals.""" - LOW = "80" - MEDIUM = "90" - HIGH = "95" - VERY_HIGH = "99" - - -# === METADATA ET QUALITE === - -class ModelMetadata(BaseModel): - """Metadata for the model used.""" - model_config = {"protected_namespaces": ()} - - name: str = Field(description="Model name") - version: str = Field(description="Model version (semver)") - trained_at: Optional[datetime] = Field(None, description="Training date") - features_used: Optional[List[str]] = Field(None, description="Features used") - model_type: ModelType = Field(description="Model type") - horizon: Optional[Horizon] = Field(None, description="Predicted horizon") - - -class UncertaintyMeasures(BaseModel): - """Mesures d'incertitude standardisées""" - std: Optional[float] = Field(None, description="Prediction standard deviation") - lower_bound: Optional[float] = Field(None, description="Lower bound (PI)") - upper_bound: Optional[float] = Field(None, description="Upper bound (PI)") - confidence_level: Optional[ConfidenceLevel] = Field(None, description="Confidence level") - calibration_score: Optional[float] = Field(None, ge=0, le=1, description="Score de calibration [0,1]") - - -class QualityMetrics(BaseModel): - """Prediction quality metrics.""" - model_config = {"protected_namespaces": ()} - - confidence: float = Field(ge=0, le=1, description="Overall confidence [0,1]") - data_freshness: Optional[float] = Field(None, description="Data freshness (hours)") - feature_coverage: Optional[float] = Field(None, ge=0, le=1, description="Couverture features [0,1]") - model_health: Optional[float] = Field(None, ge=0, le=1, description="Model health [0,1]") - - -# === REQUETES UNIFIEES === - -class UnifiedMLRequest(BaseModel): - """Unified ML request.""" - model_config = {"protected_namespaces": ()} - - assets: List[str] = Field(max_length=50, description="Assets to analyze") - model_type: ModelType = Field(description="Requested prediction type") - horizon: Optional[Horizon] = Field(None, description="Horizon temporel") - - # Options de qualité - include_uncertainty: bool = Field(False, description="Include uncertainty measures") - include_metadata: bool = Field(False, description="Include model metadata") - confidence_threshold: float = Field(0.5, ge=0, le=1, description="Minimum confidence threshold") - - # Paramètres contextuels - context: Optional[Dict[str, Any]] = Field(None, description="Additional context") - cache_ttl: Optional[int] = Field(300, description="TTL cache en secondes") - - -class BatchMLRequest(BaseModel): - """Requête ML batch pour multiple modèles/horizons""" - assets: List[str] = Field(max_length=20, description="Assets to analyze") - requests: List[Dict[str, Any]] = Field(max_length=10, description="Multiple requests") - global_options: Optional[Dict[str, Any]] = Field(None, description="Options globales") - - -# === REPONSES UNIFIEES === - -class UnifiedPrediction(BaseModel): - """Single prediction with uncertainty.""" - asset: str = Field(description="Target asset") - value: float = Field(description="Predicted value") - - # Incertitude (optionnel) - uncertainty: Optional[UncertaintyMeasures] = Field(None, description="Mesures d'incertitude") - - # Qualité - quality: QualityMetrics = Field(description="Quality metrics") - - # Métadonnées (optionnel) - metadata: Optional[ModelMetadata] = Field(None, description="Model metadata") - - -class UnifiedMLResponse(BaseModel): - """Réponse ML unifiée""" - model_config = {"protected_namespaces": ()} - - success: bool = Field(True, description="Success status") - model_type: ModelType = Field(description="Model type used") - horizon: Optional[Horizon] = Field(None, description="Predicted horizon") - - # Données principales - predictions: List[UnifiedPrediction] = Field(description="Predictions per asset") - - # Agrégations (optionnel) - aggregated: Optional[Dict[str, float]] = Field(None, description="Aggregated metrics") - - # Contexte global - processed_at: datetime = Field(default_factory=datetime.now, description="Timestamp traitement") - cache_hit: bool = Field(False, description="Result served from cache") - processing_time_ms: Optional[float] = Field(None, description="Temps de traitement") - - # Gestion d'erreurs - warnings: List[str] = Field(default_factory=list, description="Avertissements") - failed_assets: List[str] = Field(default_factory=list, description="Failed assets") - - -class BatchMLResponse(BaseModel): - """Réponse ML batch""" - success: bool = Field(True, description="Global success") - responses: Dict[str, UnifiedMLResponse] = Field(description="Responses by request") - global_metadata: Dict[str, Any] = Field(default_factory=dict, description="Global metadata") - total_processing_time_ms: Optional[float] = Field(None, description="Total processing time") - - -# === SCHEMAS SPECIFIQUES === - -class VolatilityPrediction(UnifiedPrediction): - """Prédiction de volatilité avec spécificités""" - annualized_vol: Optional[float] = Field(None, description="Annualized volatility") - regime_context: Optional[str] = Field(None, description="Regime context") - - -class SentimentPrediction(UnifiedPrediction): - """Prédiction de sentiment avec détails""" - sentiment_breakdown: Optional[Dict[str, float]] = Field(None, description="Breakdown by source") - fear_greed_index: Optional[float] = Field(None, ge=0, le=100, description="Indice Fear & Greed") - - -class RiskScorePrediction(UnifiedPrediction): - """Score de risque avec composantes""" - components: Optional[Dict[str, float]] = Field(None, description="Composantes du score") - risk_category: Optional[str] = Field(None, description="Risk category") - - -# === SCHEMAS DE MONITORING === - -class ModelHealth(BaseModel): - """Model health.""" - model_config = {"protected_namespaces": ()} - - model_name: str = Field(description="Model name") - version: str = Field(description="Version") - is_healthy: bool = Field(description="Health status") - last_prediction: Optional[datetime] = Field(None, description="Last prediction") - error_rate_24h: Optional[float] = Field(None, description="24-hour error rate") - avg_confidence: Optional[float] = Field(None, description="Average confidence") - drift_score: Optional[float] = Field(None, description="Score de drift") - - -class MLSystemHealth(BaseModel): - """Overall ML system health.""" - overall_health: float = Field(ge=0, le=1, description="Overall health [0,1]") - models_status: List[ModelHealth] = Field(description="Status by model") - system_metrics: Dict[str, Any] = Field(default_factory=dict, description="System metrics") - last_check: datetime = Field(default_factory=datetime.now, description="Last check") - - -# === UTILITY FUNCTIONS === - -def create_fallback_response( - model_type: ModelType, - assets: List[str], - error_msg: str = "Model unavailable" -) -> UnifiedMLResponse: - """Create an explicit unavailable response without synthetic predictions.""" - return UnifiedMLResponse( - success=False, - model_type=model_type, - predictions=[], - warnings=[error_msg], - failed_assets=assets - ) - - -def validate_prediction_quality(prediction: UnifiedPrediction, min_confidence: float = 0.3) -> bool: - """Valider la qualité d'une prédiction""" - return prediction.quality.confidence >= min_confidence +""" +Contrat prédictif unifié pour les modèles ML +Schémas standardisés pour entrées/sorties ML avec gestion d'incertitude et métadonnées +""" + +from pydantic import BaseModel, Field +from typing import Dict, List, Optional, Any, Union, Literal +from datetime import datetime +from enum import Enum + + +# === TYPES DE BASE === + +class ModelType(str, Enum): + """Types de modèles supportés""" + VOLATILITY = "volatility" + SENTIMENT = "sentiment" + REGIME = "regime" + CORRELATION = "correlation" + RISK = "risk" + BLENDED = "blended" + + +class Horizon(str, Enum): + """Horizons temporels standardisés""" + H1 = "1h" + H4 = "4h" + D1 = "1d" + D7 = "7d" + D30 = "30d" + D90 = "90d" + + +class Availability(str, Enum): + AVAILABLE = "Available" + PARTIAL = "Partial" + UNAVAILABLE = "Unavailable" + EXPERIMENTAL = "Experimental" + REJECTED = "Rejected" + + +class Provenance(BaseModel): + provider: Optional[str] = None + dataset_id: Optional[str] = None + evaluation_dataset_id: Optional[str] = None + observation_sha256: Optional[str] = None + code_version: Optional[str] = None + artifact_sha256: Optional[str] = None + training_start: Optional[datetime] = None + training_end: Optional[datetime] = None + adjustment_policy: Optional[str] = None + method: Optional[str] = None + + +class ValidationStatus(BaseModel): + state: Literal["descriptive", "retrospectively_validated", "unvalidated", "rejected", "not_evaluable"] = "not_evaluable" + reason: str + protocol_id: Optional[str] = None + evaluated_at: Optional[datetime] = None + metrics: Dict[str, Any] = Field(default_factory=dict) + + +class ConfidenceLevel(str, Enum): + """Confidence levels for intervals.""" + LOW = "80" + MEDIUM = "90" + HIGH = "95" + VERY_HIGH = "99" + + +# === METADATA ET QUALITE === + +class ModelMetadata(BaseModel): + """Metadata for the model used.""" + model_config = {"protected_namespaces": ()} + + name: str = Field(description="Model name") + version: str = Field(description="Model version (semver)") + trained_at: Optional[datetime] = Field(None, description="Training date") + features_used: Optional[List[str]] = Field(None, description="Features used") + model_type: ModelType = Field(description="Model type") + horizon: Optional[Horizon] = Field(None, description="Predicted horizon") + + +class UncertaintyMeasures(BaseModel): + """Uncertainty measures standardisées""" + std: Optional[float] = Field(None, description="Prediction standard deviation") + lower_bound: Optional[float] = Field(None, description="Lower bound (PI)") + upper_bound: Optional[float] = Field(None, description="Upper bound (PI)") + confidence_level: Optional[ConfidenceLevel] = Field(None, description="Confidence level") + nominal_coverage: Optional[float] = Field(None, ge=0, le=1, description="Nominal prediction interval coverage") + confirmation_coverage: Optional[float] = Field(None, ge=0, le=1, description="Observed coverage on reserved confirmation data") + calibration_score: Optional[float] = Field(None, ge=0, le=1, description="Calibration score [0,1]") + + +class QualityMetrics(BaseModel): + """Prediction quality metrics.""" + model_config = {"protected_namespaces": ()} + + confidence: Optional[float] = Field(None, ge=0, le=1, description="Evaluated confidence [0,1]; absent when not evaluated") + data_freshness: Optional[float] = Field(None, description="Data freshness (hours)") + feature_coverage: Optional[float] = Field(None, ge=0, le=1, description="Feature coverage [0,1]") + model_health: Optional[float] = Field(None, ge=0, le=1, description="Model health [0,1]") + + +# === REQUETES UNIFIEES === + +class UnifiedMLRequest(BaseModel): + """Unified ML request.""" + model_config = {"protected_namespaces": ()} + + assets: List[str] = Field(max_length=50, description="Assets to analyze") + model_type: ModelType = Field(description="Requested prediction type") + horizon: Optional[Horizon] = Field(None, description="Time horizon") + market: Literal["crypto", "stocks"] = "crypto" + source: str = Field("cointracking", min_length=1, max_length=100, description="Selected portfolio source") + + # Options de qualité + include_uncertainty: bool = Field(False, description="Include uncertainty measures") + include_metadata: bool = Field(False, description="Include model metadata") + confidence_threshold: float = Field(0.5, ge=0, le=1, description="Minimum confidence threshold") + + # Paramètres contextuels + context: Optional[Dict[str, Any]] = Field(None, description="Additional context") + cache_ttl: Optional[int] = Field(300, description="Cache TTL in seconds") + + +class BatchMLRequest(BaseModel): + """Requête ML batch pour multiple modèles/horizons""" + assets: List[str] = Field(max_length=20, description="Assets to analyze") + requests: List[Dict[str, Any]] = Field(max_length=10, description="Multiple requests") + global_options: Optional[Dict[str, Any]] = Field(None, description="Global options") + + +# === REPONSES UNIFIEES === + +class UnifiedPrediction(BaseModel): + """Single prediction with uncertainty.""" + asset: str = Field(description="Target asset") + value: Optional[Union[float, str, Dict[str, Any]]] = Field(None, description="Observed or predicted value; null when unavailable") + market: Literal["crypto", "stocks"] = "crypto" + nature: Literal["diagnostic", "forecast"] = "forecast" + target: str = "unspecified" + horizon: Optional[Horizon] = None + unit: Optional[str] = None + availability: Availability = Availability.UNAVAILABLE + reason: str = "No verified result is available" + data_as_of: Optional[datetime] = None + target_date: Optional[datetime] = None + validation: ValidationStatus = Field(default_factory=lambda: ValidationStatus(reason="No evaluation record")) + provenance: Provenance = Field(default_factory=Provenance) + + # Incertitude (optionnel) + uncertainty: Optional[UncertaintyMeasures] = Field(None, description="Uncertainty measures") + + # Qualité + quality: QualityMetrics = Field(default_factory=QualityMetrics, description="Measured quality metrics") + + # Métadonnées (optionnel) + metadata: Optional[ModelMetadata] = Field(None, description="Model metadata") + + +class UnifiedMLResponse(BaseModel): + """Réponse ML unifiée""" + model_config = {"protected_namespaces": ()} + + success: bool = Field(True, description="Success status") + model_type: ModelType = Field(description="Model type used") + horizon: Optional[Horizon] = Field(None, description="Predicted horizon") + + # Données principales + predictions: List[UnifiedPrediction] = Field(description="Predictions per asset") + + # Agrégations (optionnel) + aggregated: Optional[Dict[str, Any]] = Field(None, description="Aggregated metrics") + user_id: Optional[str] = None + source: Optional[str] = None + + # Contexte global + processed_at: datetime = Field(default_factory=datetime.now, description="Processing timestamp") + cache_hit: bool = Field(False, description="Result served from cache") + processing_time_ms: Optional[float] = Field(None, description="Processing time") + + # Gestion d'erreurs + warnings: List[str] = Field(default_factory=list, description="Warnings") + failed_assets: List[str] = Field(default_factory=list, description="Failed assets") + + +class BatchMLResponse(BaseModel): + """Réponse ML batch""" + success: bool = Field(True, description="Global success") + responses: Dict[str, UnifiedMLResponse] = Field(description="Responses by request") + global_metadata: Dict[str, Any] = Field(default_factory=dict, description="Global metadata") + total_processing_time_ms: Optional[float] = Field(None, description="Total processing time") + + +# === SCHEMAS SPECIFIQUES === + +class VolatilityPrediction(UnifiedPrediction): + """Prédiction de volatilité avec spécificités""" + annualized_vol: Optional[float] = Field(None, description="Annualized volatility") + regime_context: Optional[str] = Field(None, description="Regime context") + + +class SentimentPrediction(UnifiedPrediction): + """Prédiction de sentiment avec détails""" + sentiment_breakdown: Optional[Dict[str, float]] = Field(None, description="Breakdown by source") + fear_greed_index: Optional[float] = Field(None, ge=0, le=100, description="External Fear & Greed index") + + +class RiskScorePrediction(UnifiedPrediction): + """Score de risque avec composantes""" + components: Optional[Dict[str, float]] = Field(None, description="Score components") + risk_category: Optional[str] = Field(None, description="Risk category") + + +# === SCHEMAS DE MONITORING === + +class ModelHealth(BaseModel): + """Model health.""" + model_config = {"protected_namespaces": ()} + + model_name: str = Field(description="Model name") + version: str = Field(description="Version") + is_healthy: Optional[bool] = Field(None, description="Measured health status; unknown without inference evidence") + last_prediction: Optional[datetime] = Field(None, description="Last prediction") + error_rate_24h: Optional[float] = Field(None, description="24-hour error rate") + avg_confidence: Optional[float] = Field(None, description="Average confidence") + drift_score: Optional[float] = Field(None, description="Measured drift score") + + +class MLSystemHealth(BaseModel): + """Overall ML system health.""" + overall_health: Optional[float] = Field(None, ge=0, le=1, description="Measured overall health [0,1]") + models_status: List[ModelHealth] = Field(description="Status by model") + system_metrics: Dict[str, Any] = Field(default_factory=dict, description="System metrics") + last_check: datetime = Field(default_factory=datetime.now, description="Last check") + + +# === UTILITY FUNCTIONS === + +def create_fallback_response( + model_type: ModelType, + assets: List[str], + error_msg: str = "Model unavailable" +) -> UnifiedMLResponse: + """Create an explicit unavailable response without synthetic predictions.""" + return UnifiedMLResponse( + success=False, + model_type=model_type, + predictions=[], + warnings=[error_msg], + failed_assets=assets + ) + + +def validate_prediction_quality(prediction: UnifiedPrediction, min_confidence: float = 0.3) -> bool: + """Valider la qualité d'une prédiction""" + return prediction.quality.confidence is not None and prediction.quality.confidence >= min_confidence diff --git a/api/startup.py b/api/startup.py index 8023277a..8acadd45 100644 --- a/api/startup.py +++ b/api/startup.py @@ -18,29 +18,11 @@ async def initialize_ml_models(): - """ - Initialize ML models for Governance Engine. - - Returns: - int: Number of models successfully initialized - """ - try: - from services.ml.orchestrator import get_orchestrator - orchestrator = get_orchestrator() - - # Force models to ready status - models_initialized = 0 - for model_type in ['volatility', 'regime', 'correlation', 'sentiment', 'rebalancing']: - if model_type in orchestrator.model_status: - orchestrator.model_status[model_type] = 'ready' - models_initialized += 1 - - logger.info(f" {models_initialized} ML models forced to ready status") - return models_initialized - - except Exception as ml_error: - logger.error(f" ML initialization failed: {ml_error}") - return 0 + """Report loaded artifacts without forcing any readiness or training.""" + from services.ml.reliability import capability_service + count = sum(entry["loaded"] for entry in capability_service.catalog()) + logger.info("ML artifacts actually loaded: %s; inference remains on demand", count) + return count async def initialize_governance_engine(): @@ -187,39 +169,9 @@ async def initialize_task_scheduler(): async def initialize_ml_auto_trainer(): - """ - Initialize ML Auto-Trainer for automatic periodic model retraining. - - Returns: - bool: True if auto-trainer started successfully - - Note: - - Trains regime models daily (3am) - - Trains volatility models daily (midnight) - - Uses MLTrainingScheduler rules to check if retraining is needed - - Integrates with TrainingExecutor for background jobs - """ - try: - import os - from services.ml.auto_trainer import ml_auto_trainer - - # Check if auto-training is enabled (default: enabled) - auto_train_enabled = os.getenv("ML_AUTO_TRAIN", "1").strip() == "1" - - if not auto_train_enabled: - logger.info(" ML Auto-Trainer disabled (ML_AUTO_TRAIN != 1)") - return False - - # Start the scheduler - ml_auto_trainer.start() - - logger.info(" ML Auto-Trainer initialized successfully") - return True - - except Exception as e: - logger.warning(f" ML Auto-Trainer initialization failed (non-blocking): {e}") - logger.info(" Models will only train manually via Admin Dashboard") - return False + """Training is restricted to explicit administrator evaluations.""" + logger.info("ML auto-training disabled: use explicit administrator actions") + return False async def background_startup_tasks(): @@ -236,34 +188,33 @@ async def background_startup_tasks(): # Initialize ML models models_count = await initialize_ml_models() - if models_count > 0: - # Initialize Governance Engine - governance_ok = await initialize_governance_engine() + # Initialize Governance Engine + governance_ok = await initialize_governance_engine() - # Initialize Alert Engine - alerts_ok = await initialize_alert_engine() + # Initialize Alert Engine + alerts_ok = await initialize_alert_engine() - # Initialize Playwright (optional, for crypto-toolbox scraping) - # Note: Browser not launched unless router enabled in api/main.py - playwright_ok = await initialize_playwright_browser() + # Initialize Playwright (optional, for crypto-toolbox scraping) + # Note: Browser not launched unless router enabled in api/main.py + playwright_ok = await initialize_playwright_browser() - # Initialize Task Scheduler (optional, periodic tasks) - # Note: Only starts if RUN_SCHEDULER=1 - scheduler_ok = await initialize_task_scheduler() + # Initialize Task Scheduler (optional, periodic tasks) + # Note: Only starts if RUN_SCHEDULER=1 + scheduler_ok = await initialize_task_scheduler() - # Initialize ML Auto-Trainer (optional, automatic model retraining) - # Note: Only starts if ML_AUTO_TRAIN=1 (default: enabled) - ml_auto_trainer_ok = await initialize_ml_auto_trainer() + # Initialize ML Auto-Trainer (optional, automatic model retraining) + # Note: Only starts if ML_AUTO_TRAIN=1 (default: enabled) + ml_auto_trainer_ok = await initialize_ml_auto_trainer() - logger.info( - f" Startup complete: " - f"ML={models_count} models, " - f"Governance={'OK' if governance_ok else 'Warning'}, " - f"Alerts={'OK' if alerts_ok else 'Warning'}, " - f"Playwright={'OK' if playwright_ok else 'Next'}, " - f"Scheduler={'OK' if scheduler_ok else 'Paused'}, " - f"ML-AutoTrain={'OK' if ml_auto_trainer_ok else 'Paused'}" - ) + logger.info( + f" Startup complete: " + f"ML={models_count} models, " + f"Governance={'OK' if governance_ok else 'Warning'}, " + f"Alerts={'OK' if alerts_ok else 'Warning'}, " + f"Playwright={'OK' if playwright_ok else 'Next'}, " + f"Scheduler={'OK' if scheduler_ok else 'Paused'}, " + f"ML-AutoTrain={'OK' if ml_auto_trainer_ok else 'Paused'}" + ) except Exception as e: logger.info(f" Background loading failed, models will load on demand: {e}") diff --git a/api/unified_ml_endpoints.py b/api/unified_ml_endpoints.py index bc3d6c17..63f09832 100644 --- a/api/unified_ml_endpoints.py +++ b/api/unified_ml_endpoints.py @@ -14,7 +14,8 @@ Refactoré en modules (Fév 2026): 1728L -> ~50L orchestrateur. """ -from fastapi import APIRouter +from fastapi import APIRouter, Depends +from api.deps import get_required_user import logging # Import des sous-routers modulaires @@ -33,7 +34,7 @@ logger = logging.getLogger(__name__) # Router principal qui agrège tous les sous-routers -router = APIRouter(prefix="/api/ml", tags=["Machine Learning"]) +router = APIRouter(prefix="/api/ml", tags=["Machine Learning"], dependencies=[Depends(get_required_user)]) # Inclure tous les sous-routers router.include_router(model_router) diff --git a/config/ml_capability_registry.json b/config/ml_capability_registry.json new file mode 100644 index 00000000..452a7dba --- /dev/null +++ b/config/ml_capability_registry.json @@ -0,0 +1,30 @@ +{ + "schema_version": 1, + "research_reference": "CRYPTO_FORECAST_RESEARCH_STATUS_2026-09-13.md", + "capabilities": { + "direction": { + "availability": "Rejected", + "validation_state": "rejected", + "nature": "forecast", + "reason": "Existing causal crypto research failed its economic gates. Directional, funding and hurdle experiments remain closed.", + "reactivation_allowed": false + }, + "alerts": { + "availability": "Rejected", + "validation_state": "rejected", + "reason": "ML alert predictor is disabled; reactivation is outside this implementation.", + "reactivation_allowed": false + }, + "correlation_transformer": { + "availability": "Experimental", + "validation_state": "unvalidated", + "reason": "No independent comparison with historical correlation references. Retraining cannot certify forecasting quality." + }, + "cycle": { + "availability": "Experimental", + "validation_state": "descriptive", + "nature": "diagnostic", + "reason": "Heuristic market-cycle diagnostic; not a validated forecast." + } + } +} diff --git a/data/ml_verified/crypto/ADA.csv b/data/ml_verified/crypto/ADA.csv new file mode 100644 index 00000000..ffecb371 --- /dev/null +++ b/data/ml_verified/crypto/ADA.csv @@ -0,0 +1,3089 @@ +date,close +2018-04-17,0.2426 +2018-04-18,0.262 +2018-04-19,0.27004 +2018-04-20,0.30108 +2018-04-21,0.28668 +2018-04-22,0.2816 +2018-04-23,0.28788 +2018-04-24,0.30853 +2018-04-25,0.2687 +2018-04-26,0.29326 +2018-04-27,0.28445 +2018-04-28,0.35676 +2018-04-29,0.36254 +2018-04-30,0.34145 +2018-05-01,0.35504 +2018-05-02,0.37199 +2018-05-03,0.36716 +2018-05-04,0.358 +2018-05-05,0.36502 +2018-05-06,0.34654 +2018-05-07,0.33046 +2018-05-08,0.32213 +2018-05-09,0.31884 +2018-05-10,0.29121 +2018-05-11,0.25573 +2018-05-12,0.2655 +2018-05-13,0.27718 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b/data/ml_verified/crypto/LINK.json new file mode 100644 index 00000000..c3639d36 --- /dev/null +++ b/data/ml_verified/crypto/LINK.json @@ -0,0 +1,12 @@ +{ + "provider": "binance_spot_public_market_data", + "research_artifact_id": "crypto-forecast-history-acquisition-v1-7d67c83f07ff241e", + "acquisition_manifest_sha256": "a53c49b4d0ad95a80126c0db07eaa0eb6125df74c14c8f22d7f3e91e00b1be63", + "original_ohlcv_sha256": "62da657a5300626d7361d20b375a573403a1c043dcd85b568d3bda62164f5bdd", + "adjustment_policy": "Unadjusted Binance Spot USDT daily closes", + "file": "LINK.csv", + "sha256": "b93c2c5e76e38634e50670ccd6d8b2d9fcb66e0035378f13a9c39182dc259047", + "dataset_id": "verified-daily-crypto-LINK-b93c2c5e76e38634", + "fetched_at": "2026-09-30T13:29:06.089680+00:00", + "refresh_raw_response_sha256": "0bd065eaabe323b86f7978cc9eb6b19a6f35d526f385a3c44ce638dc18bdf563" +} \ No newline at end of file diff --git a/data/ml_verified/crypto/LTC.csv b/data/ml_verified/crypto/LTC.csv new file mode 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"518f2254f5acb75b42a9d8bb9deb3a67a029ed92216326a2fec3143eb01a53f3", + "adjustment_policy": "Unadjusted Binance Spot USDT daily closes", + "file": "XRP.csv", + "sha256": "da324441ae0b4bbb2cf324c6e2eb97daa9873bbcc58ac44bc3917ac1e074d4f6", + "dataset_id": "verified-daily-crypto-XRP-da324441ae0b4bbb", + "fetched_at": "2026-09-30T13:29:09.681890+00:00", + "refresh_raw_response_sha256": "968b429545ea79177245fe6f0490e18b8a31f129eb424e7bfa411b13424f99e4" +} \ No newline at end of file diff --git a/data/ml_verified/cycles/BTC.csv b/data/ml_verified/cycles/BTC.csv new file mode 100644 index 00000000..5625a35d --- /dev/null +++ b/data/ml_verified/cycles/BTC.csv @@ -0,0 +1,5790 @@ +date,close +2010-07-18,0.08584 +2010-07-19,0.0808 +2010-07-20,0.0747357288135593 +2010-07-21,0.0791928626534191 +2010-07-22,0.0584697603740503 +2010-07-23,0.0605928696668615 +2010-07-24,0.05454 +2010-07-25,0.050540618351841 +2010-07-26,0.056 +2010-07-27,0.058622016364699 +2010-07-28,0.0589110987726476 +2010-07-29,0.0699 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Retrospective historical cycle comparison only; not used to train or infer volatility forecasts.", + "license": "CC BY-NC 4.0", + "attribution": "Coin Metrics Community https://github.com/coinmetrics/data" +} \ No newline at end of file diff --git a/data/ml_verified/stocks/AAPL.csv b/data/ml_verified/stocks/AAPL.csv new file mode 100644 index 00000000..2259803d --- /dev/null +++ b/data/ml_verified/stocks/AAPL.csv @@ -0,0 +1,5218 @@ +date,close +2006-01-03,2.2357442378997803 +2006-01-04,2.2423250675201416 +2006-01-05,2.2246787548065186 +2006-01-06,2.2821052074432373 +2006-01-09,2.274627923965454 +2006-01-10,2.418492555618286 +2006-01-11,2.5094189643859863 +2006-01-12,2.521082639694214 +2006-01-13,2.5599658489227295 +2006-01-17,2.5336451530456543 +2006-01-18,2.4672446250915527 +2006-01-19,2.3640573024749756 +2006-01-20,2.2758243083953857 +2006-01-23,2.3230817317962646 +2006-01-24,2.2743279933929443 +2006-01-25,2.219295024871826 +2006-01-26,2.163362741470337 +2006-01-27,2.154390335083008 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a/data/ml_verified/stocks/IWDA.AS.json b/data/ml_verified/stocks/IWDA.AS.json new file mode 100644 index 00000000..b877fb1c --- /dev/null +++ b/data/ml_verified/stocks/IWDA.AS.json @@ -0,0 +1,18 @@ +{ + "provider": "Yahoo Finance via yfinance", + "yfinance_version": "1.0", + "adjustment_policy": "auto_adjust=True: split/dividend-adjusted daily closes; current vendor revisions, not a point-in-time corporate-action archive", + "exchange_calendar": "XAMS", + "fetched_at": "2026-09-30T13:29:07.946692+00:00", + "file": "IWDA.AS.csv", + "sha256": "f38ea1d4f9d02c24bb5ee6fa2ea8806e6f4b08bdfd7e9f3e93b974f81f2f02a9", + "dataset_id": "verified-daily-stocks-IWDA.AS-f38ea1d4f9d02c24", + "original_provider_sha256": "0ddc5cd83671a7d468b8064633f71355bbab4946e5d6c75b4c9e7befa9d695e8", + "raw_archive": "raw\\0ddc5cd83671a7d468b8064633f71355bbab4946e5d6c75b4c9e7befa9d695e8\\IWDA.AS.csv", + "observation_filter": { + "excluded_dates": [ + "2026-09-29" + ], + "policy": "Exclude non-session/incomplete dates and trailing invalid provider rows; never fill an interior missing close" + } +} \ No newline at end of file diff --git a/data/ml_verified/stocks/KO.csv b/data/ml_verified/stocks/KO.csv new file mode 100644 index 00000000..184dca9f --- /dev/null +++ b/data/ml_verified/stocks/KO.csv @@ -0,0 +1,5218 @@ +date,close +2006-01-03,10.918529510498047 +2006-01-04,10.89716911315918 +2006-01-05,10.95055866241455 +2006-01-06,11.054677963256836 +2006-01-09,11.108064651489258 +2006-01-10,11.118744850158691 +2006-01-11,11.124085426330566 +2006-01-12,11.062684059143066 +2006-01-13,11.02797794342041 +2006-01-17,10.953230857849121 +2006-01-18,10.89716911315918 +2006-01-19,10.926535606384277 +2006-01-20,10.702289581298828 +2006-01-23,10.811747550964355 +2006-01-24,10.918529510498047 +2006-01-25,10.982598304748535 +2006-01-26,11.073362350463867 +2006-01-27,11.204174995422363 +2006-01-30,11.145444869995117 +2006-01-31,11.046662330627441 +2006-02-01,11.07870101928711 +2006-02-02,11.006624221801758 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b/data/ml_verified/stocks/META.json new file mode 100644 index 00000000..6c4ea7a4 --- /dev/null +++ b/data/ml_verified/stocks/META.json @@ -0,0 +1,10 @@ +{ + "provider": "Yahoo Finance via yfinance", + "yfinance_version": "1.0", + "adjustment_policy": "auto_adjust=True: split/dividend-adjusted daily closes; current vendor revisions, not a point-in-time corporate-action archive", + "exchange_calendar": "XNYS", + "fetched_at": "2026-09-30T13:29:01.967239+00:00", + "file": "META.csv", + "sha256": "a496f00e3cd164f3f8a3f7fadc9d26793773523345db8fd62ea9310fe270206c", + "dataset_id": "verified-daily-stocks-META-a496f00e3cd164f3" +} \ No newline at end of file diff --git a/data/ml_verified/stocks/MSFT.csv b/data/ml_verified/stocks/MSFT.csv new file mode 100644 index 00000000..1c5bd8d5 --- /dev/null +++ b/data/ml_verified/stocks/MSFT.csv @@ -0,0 +1,5218 @@ +date,close +2006-01-03,18.597070693969727 +2006-01-04,18.687156677246094 +2006-01-05,18.701005935668945 +2006-01-06,18.645578384399414 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a point-in-time corporate-action archive", + "exchange_calendar": "XNYS", + "fetched_at": "2026-09-30T13:29:02.892631+00:00", + "file": "PLTR.csv", + "sha256": "c1be2854367e81846c55bd8ceeb0f4865f4e2315e36a8a7b988d285171921a6d", + "dataset_id": "verified-daily-stocks-PLTR-c1be2854367e8184" +} \ No newline at end of file diff --git a/data/ml_verified/stocks/QQQ.csv b/data/ml_verified/stocks/QQQ.csv new file mode 100644 index 00000000..7a6fafb6 --- /dev/null +++ b/data/ml_verified/stocks/QQQ.csv @@ -0,0 +1,5218 @@ +date,close +2006-01-03,35.237037658691406 +2006-01-04,35.60383224487305 +2006-01-05,35.75737380981445 +2006-01-06,36.40564727783203 +2006-01-09,36.550636291503906 +2006-01-10,36.57624435424805 +2006-01-11,36.85773468017578 +2006-01-12,36.678585052490234 +2006-01-13,36.66154098510742 +2006-01-17,36.42270278930664 +2006-01-18,36.00471115112305 +2006-01-19,36.26915740966797 +2006-01-20,35.185855865478516 +2006-01-23,35.19438934326172 +2006-01-24,35.347923278808594 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b/data/ml_verified/stocks/UETW.DE.json new file mode 100644 index 00000000..06a2e6e4 --- /dev/null +++ b/data/ml_verified/stocks/UETW.DE.json @@ -0,0 +1,10 @@ +{ + "provider": "Yahoo Finance via yfinance", + "yfinance_version": "1.0", + "adjustment_policy": "auto_adjust=True: split/dividend-adjusted daily closes; current vendor revisions, not a point-in-time corporate-action archive", + "exchange_calendar": "XFRA", + "fetched_at": "2026-09-30T13:29:29.667733+00:00", + "file": "UETW.DE.csv", + "sha256": "0175b2736b9a6f7ee06828f195c52bac7f2034e5fd4545ad1e262b747d031c8f", + "dataset_id": "verified-daily-stocks-UETW.DE-0175b2736b9a6f7e" +} \ No newline at end of file diff --git a/data/ml_verified/stocks/VGK.csv b/data/ml_verified/stocks/VGK.csv new file mode 100644 index 00000000..7cae5030 --- /dev/null +++ b/data/ml_verified/stocks/VGK.csv @@ -0,0 +1,5218 @@ +date,close +2006-01-03,25.76180648803711 +2006-01-04,26.007781982421875 +2006-01-05,25.927364349365234 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b/data/ml_verified/stocks/VWO.json new file mode 100644 index 00000000..ef01150b --- /dev/null +++ b/data/ml_verified/stocks/VWO.json @@ -0,0 +1,10 @@ +{ + "provider": "Yahoo Finance via yfinance", + "yfinance_version": "1.0", + "adjustment_policy": "auto_adjust=True: split/dividend-adjusted daily closes; current vendor revisions, not a point-in-time corporate-action archive", + "exchange_calendar": "XNYS", + "fetched_at": "2026-09-30T13:29:18.064165+00:00", + "file": "VWO.csv", + "sha256": "46a14a42c91acfc0664f08de7c7313a9704da3161499252a4a2ef4fa803c4196", + "dataset_id": "verified-daily-stocks-VWO-46a14a42c91acfc0" +} \ No newline at end of file diff --git a/data/ml_verified/stocks/WFRD.csv b/data/ml_verified/stocks/WFRD.csv new file mode 100644 index 00000000..552b2a60 --- /dev/null +++ b/data/ml_verified/stocks/WFRD.csv @@ -0,0 +1,1442 @@ +date,close +2021-01-04,5.565280914306641 +2021-01-05,5.72041130065918 +2021-01-06,5.938562870025635 +2021-01-07,6.185801029205322 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+2026-09-18,106.5999984741211 +2026-09-21,111.68000030517578 +2026-09-22,113.63999938964844 +2026-09-23,113.0999984741211 +2026-09-24,111.77999877929688 +2026-09-25,114.13999938964844 +2026-09-28,111.77999877929688 +2026-09-29, diff --git a/data/ml_verified/stocks/raw/dd475a3eb04d1bc897449193ca829aa7b4cdda404a6cf8bb47e8e5c4f9d70822/SMH.L.json b/data/ml_verified/stocks/raw/dd475a3eb04d1bc897449193ca829aa7b4cdda404a6cf8bb47e8e5c4f9d70822/SMH.L.json new file mode 100644 index 00000000..cef4bb98 --- /dev/null +++ b/data/ml_verified/stocks/raw/dd475a3eb04d1bc897449193ca829aa7b4cdda404a6cf8bb47e8e5c4f9d70822/SMH.L.json @@ -0,0 +1,10 @@ +{ + "provider": "Yahoo Finance via yfinance", + "yfinance_version": "1.0", + "adjustment_policy": "auto_adjust=True: split/dividend-adjusted daily closes; current vendor revisions, not a point-in-time corporate-action archive", + "exchange_calendar": "XLON", + "fetched_at": "2026-09-30T13:29:19.038483+00:00", + "file": "SMH.L.csv", + "sha256": "dd475a3eb04d1bc897449193ca829aa7b4cdda404a6cf8bb47e8e5c4f9d70822", + "dataset_id": "verified-daily-stocks-SMH.L-dd475a3eb04d1bc8" +} \ No newline at end of file diff --git a/deploy/ml-production/release_robot2.py b/deploy/ml-production/release_robot2.py new file mode 100644 index 00000000..db4b6773 --- /dev/null +++ b/deploy/ml-production/release_robot2.py @@ -0,0 +1,278 @@ +"""Bounded production delivery on robot2. Private state never leaves this host.""" +from __future__ import annotations + +import argparse +import hashlib +import http.client +import json +import os +from pathlib import Path +import secrets +import shutil +import socket +import subprocess +import time +import urllib.request +from zipfile import ZipFile + +PROD = Path('/home/jack/smartfolio') +ROOT = Path('/home/jack/smartfolio-releases/ml-reliability-20261002') +OLD = 'sha256:0c724b8b8ff87d73885bf0e109276c7b1da2b211f3c9c497b33f9bcda544f2aa' +START = '2026-09-28T09:01:59.068663028Z' +BASE = '806338b522e89647c3f49260e0f418dbea1da761' +NAME = 'smartfolio-api' +BACKUP = 'smartfolio-api-before-ml-reliability-20261002' +CHECK = 'smartfolio-ml-production-check-20261002' +IMAGE = 'smartfolio-prod:ml-reliability-20261002' +BASE_TAG = 'smartfolio-prod-base:806338-20261002' + + +def run(args): + return subprocess.check_output(args, stderr=subprocess.PIPE) + + +def inspect(name): + return json.loads(run(['docker', 'inspect', name]))[0] + + +def sha(content): + return hashlib.sha256(content).hexdigest() + + +def write_private(path, value): + path.parent.mkdir(parents=True, exist_ok=True, mode=0o700) + fd = os.open(path, os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0o600) + with os.fdopen(fd, 'w') as out: + json.dump(value, out) + + +def emit(value, filename=None): + if filename: + (ROOT / filename).write_text(json.dumps(value, indent=2)) + print(json.dumps(value), flush=True) + + +def invariant(): + assert run(['hostname']).decode().strip() == 'robot2', 'Unexpected host' + assert ROOT.resolve().is_relative_to(Path('/home/jack/smartfolio-releases').resolve()) + current = inspect(NAME) + assert current['Image'] == OLD and current['State']['StartedAt'] == START and current['State']['Running'], 'Production baseline changed' + assert run(['git', '-C', str(PROD), 'rev-parse', 'HEAD']).decode().strip() == BASE, 'Production checkout changed' + return current + + +def verified_package(path, expected): + assert sha(path.read_bytes()) == expected, 'Package digest mismatch' + with ZipFile(path) as z: + manifest = json.loads(z.read('manifest.json')) + assert manifest['base_commit'] == BASE and manifest['private_data_included'] is False + for row in manifest['entries']: + name = row['path'] + assert not any(x in name for x in ('data/users/', '.env', 'private/')), 'Private entry rejected' + assert sha(z.read(name)) == row['sha256'], 'Entry digest mismatch' + return manifest + + +def build(args): + invariant() + assert shutil.disk_usage(PROD).free > 15 * 1024**3, 'Insufficient free disk' + ROOT.mkdir(parents=True, exist_ok=True, mode=0o700) + manifest = verified_package(args.package, args.sha) + context = ROOT / 'context' + context.mkdir(exist_ok=True) + with ZipFile(args.package) as z: + for row in manifest['entries']: + if not row['path'].startswith(('code/', 'public/models/validated_risk/', 'public/data/ml_verified/')): + continue + target = (context / row['path']).resolve() + assert target.is_relative_to(context.resolve()) + target.parent.mkdir(parents=True, exist_ok=True) + target.write_bytes(z.read(row['path'])) + (context / 'Dockerfile').write_text('''ARG BASE_IMAGE +FROM ${BASE_IMAGE} +USER root +WORKDIR /app +COPY code/ /app/ +RUN pip install --no-cache-dir "exchange-calendars==4.13.2" && pip check +COPY public/models/validated_risk/ /app/models/validated_risk/ +ENV PYTHONDONTWRITEBYTECODE=1 ML_AUTO_TRAIN=0 +LABEL smartfolio.release="ml-reliability-20261002" +''') + subprocess.run(['docker', 'tag', OLD, BASE_TAG], check=True) + with (ROOT / 'build.log').open('wb') as log: + p = subprocess.run(['docker', 'build', '--build-arg', 'BASE_IMAGE='+BASE_TAG, + '--label', 'org.opencontainers.image.revision='+args.commit, + '--label', 'smartfolio.package.sha256='+args.sha, + '-t', IMAGE, str(context)], stdout=log, stderr=subprocess.STDOUT) + assert p.returncode == 0, 'Build failed; inspect public build.log on robot2' + code = '''import pathlib,json +from services.ml.reliability import code_version +p=pathlib.Path('/app') +assert not (p/'data/users/jack').exists() and not (p/'.env').exists() +assert not (p/'preview_app.py').exists() and not (p/'preview_guard.py').exists() +assert not (p/'static/preview-banner.js').exists() +registry=json.loads((p/'models/validated_risk/registry.json').read_text()) +assert len(registry)==62 +assert all(json.loads((p/'models/validated_risk'/k).read_text())['code_version']==code_version() for k in registry) +print(json.dumps({'private_account_not_baked':True,'preview_runtime_absent':True,'artifacts':len(registry),'code_fingerprints_match':True})) +''' + subprocess.run(['docker', 'run', '--rm', '--network', 'none', '--entrypoint', 'python', IMAGE, '-B', '-c', code], check=True) + invariant() + record = dict(package_sha256=args.sha, commit=args.commit, image=inspect(IMAGE)['Id'], entries=len(manifest['entries']), production_unchanged=True) + emit(record, 'build-result.json') + + +class DockerHTTP(http.client.HTTPConnection): + def __init__(self): + super().__init__('localhost') + + def connect(self): + self.sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) + self.sock.connect('/var/run/docker.sock') + + +def api(method, path, payload=None): + conn = DockerHTTP() + conn.request(method, path, json.dumps(payload).encode() if payload is not None else None, {'Content-Type': 'application/json'}) + response = conn.getresponse() + raw = response.read() + conn.close() + assert response.status < 300, 'Docker operation failed: '+str(response.status) + return json.loads(raw) if raw else {} + + +def config_from(old): + keys = ('User', 'Env', 'Cmd', 'Healthcheck', 'WorkingDir', 'Entrypoint', 'StopSignal', 'StopTimeout', 'Shell', 'Labels', 'Tty', 'OpenStdin', 'StdinOnce', 'ExposedPorts', 'Volumes') + config = {k: v for k, v in old['Config'].items() if k in keys} + config['Image'] = IMAGE + config['Env'] = [x for x in config['Env'] if not x.startswith(('ML_AUTO_TRAIN=', 'ML_PORTFOLIO_SNAPSHOT='))] + ['ML_AUTO_TRAIN=0'] + config['Labels'] = {**(config.get('Labels') or {}), **inspect(IMAGE)['Config']['Labels']} + config['HostConfig'] = json.loads(json.dumps(old['HostConfig'])) + config['NetworkingConfig'] = {'EndpointsConfig': {n: {'Aliases': [NAME]} for n in old['NetworkSettings']['Networks']}} + return config + + +def ready(port): + try: + host = '127.0.0.1' if port == 8084 else '192.168.1.200' + return urllib.request.urlopen('http://'+host+':'+str(port)+'/healthz', timeout=3).status == 200 + except Exception: + return False + + +def wait_ready(name, port): + deadline = time.monotonic()+150 + while time.monotonic() < deadline: + if ready(port): + return + assert inspect(name)['State']['Running'], 'Candidate container stopped' + time.sleep(1) + raise RuntimeError('Readiness timeout') + + +def preflight(args): + old = invariant() + assert args.commit == inspect(IMAGE)['Config']['Labels']['org.opencontainers.image.revision'] + assert not (ROOT / 'preflight').exists(), 'Preflight already exists; preserve its results' + folder = ROOT / 'preflight' + folder.mkdir(mode=0o700) + assert not any(p.is_symlink() for p in (PROD / 'data').rglob('*')), 'Reject symlinked data for write-path validation' + shutil.copytree(PROD / 'data', folder / 'data') + (folder / 'logs').mkdir() + (folder / 'cache').mkdir() + assert not (folder / 'data/ml_verified').exists(), 'Public dataset target already exists' + shutil.copytree(ROOT / 'context/public/data/ml_verified', folder / 'data/ml_verified') + config = config_from(old) + env = dict(x.split('=', 1) for x in config['Env'] if '=' in x) + env.update(RUN_SCHEDULER='0', ML_AUTO_TRAIN='0', ML_INFERENCE_JOURNAL='0', + JWT_SECRET_KEY=secrets.token_urlsafe(48), REDIS_URL='redis://smartfolio-ml-preview-redis:6379/3', + ALLOWED_HOSTS='192.168.1.200,localhost,127.0.0.1,testserver', API_BASE_URL='http://127.0.0.1:8084') + config['Env'] = [k+'='+v for k, v in env.items()] + host = config['HostConfig'] + host['Binds'] = [str(folder/'data')+':/app/data:rw', str(folder/'logs')+':/app/logs:rw', str(folder/'cache')+':/app/cache:rw'] + host['PortBindings'] = {'8080/tcp': [{'HostIp': '127.0.0.1', 'HostPort': '8084'}]} + host['RestartPolicy'] = {'Name': 'no', 'MaximumRetryCount': 0} + host['Memory'] = 3 * 1024**3 + host['NanoCpus'] = 2_000_000_000 + config['Labels'] = {'smartfolio.preflight': 'ml-reliability-20261002'} + config['NetworkingConfig'] = {'EndpointsConfig': {'smartfolio-ml-preview-network': {'Aliases': [CHECK]}}} + created = api('POST', '/containers/create?name='+CHECK, config) + api('POST', '/containers/'+created['Id']+'/start') + wait_ready(CHECK, 8084) + invariant() + emit({'preflight_ready': True, 'private_copy_stays_on_robot2': True, 'scheduler_disabled': True, 'auto_training_disabled': True, 'production_unchanged': True}) + + +def stop_preflight(args): + d = inspect(CHECK) + assert d['Config'].get('Labels', {}).get('smartfolio.preflight') == 'ml-reliability-20261002' + subprocess.run(['docker', 'stop', CHECK], check=True, stdout=subprocess.DEVNULL) + emit({'preflight_stopped': True}) + + +def switch(args): + old = invariant() + build_record = json.loads((ROOT / 'build-result.json').read_text()) + assert args.commit == build_record['commit'] and inspect(IMAGE)['Id'] == build_record['image'] + assert json.loads((ROOT / 'preflight-validation.json').read_text())['all_passed'] is True, 'Preflight validation required' + names = run(['docker', 'ps', '-a', '--format', '{{.Names}}']).decode().splitlines() + assert BACKUP not in names, 'Rollback container already exists' + private = ROOT / 'private-backup' + private.mkdir(mode=0o700) + write_private(private / 'container.json', old) + config = config_from(old) + subprocess.run(['docker', 'stop', NAME], check=True, stdout=subprocess.DEVNULL) + renamed = False + try: + shutil.copytree(PROD / 'data', private / 'data', symlinks=True) + assert not (PROD / 'data/ml_verified').exists(), 'Public dataset target already exists' + temp = PROD / 'data/ml_verified.delivery-20261002' + assert not temp.exists() + shutil.copytree(ROOT / 'context/public/data/ml_verified', temp) + temp.rename(PROD / 'data/ml_verified') + subprocess.run(['docker', 'rename', NAME, BACKUP], check=True) + renamed = True + created = api('POST', '/containers/create?name='+NAME, config) + api('POST', '/containers/'+created['Id']+'/start') + wait_ready(NAME, 8080) + current = inspect(NAME) + assert current['HostConfig']['PortBindings'] == old['HostConfig']['PortBindings'] + assert {(m['Source'], m['Destination'], m['RW']) for m in current['Mounts']} == {(m['Source'], m['Destination'], m['RW']) for m in old['Mounts']} + a = dict(x.split('=', 1) for x in old['Config']['Env'] if '=' in x) + b = dict(x.split('=', 1) for x in current['Config']['Env'] if '=' in x) + assert {k: v for k, v in a.items() if k not in ('ML_AUTO_TRAIN', 'ML_PORTFOLIO_SNAPSHOT')} == {k: v for k, v in b.items() if k not in ('ML_AUTO_TRAIN', 'ML_PORTFOLIO_SNAPSHOT')} + assert b['ML_AUTO_TRAIN'] == '0' and b.get('RUN_SCHEDULER') == a.get('RUN_SCHEDULER') + emit({'status': 'production_ready', 'image': current['Image'], 'commit': args.commit, 'started': current['State']['StartedAt'], 'rollback_container': BACKUP, 'private_backup_local_only': True, 'mounts_preserved': True, 'scheduler_preserved': True, 'auto_training_disabled': True}, 'deployment-result.json') + except Exception: + if renamed: + rollback(args) + else: + subprocess.run(['docker', 'start', NAME], check=True, stdout=subprocess.DEVNULL) + raise + + +def rollback(args): + backup = inspect(BACKUP) + assert backup['Image'] == OLD and not backup['State']['Running'] + names = run(['docker', 'ps', '-a', '--format', '{{.Names}}']).decode().splitlines() + if NAME in names: + current = inspect(NAME) + assert current['Config'].get('Labels', {}).get('smartfolio.release') == 'ml-reliability-20261002' + subprocess.run(['docker', 'stop', NAME], check=True, stdout=subprocess.DEVNULL) + failed = NAME+'-failed-ml-reliability-20261002' + assert failed not in names + subprocess.run(['docker', 'rename', NAME, failed], check=True) + subprocess.run(['docker', 'rename', BACKUP, NAME], check=True) + subprocess.run(['docker', 'start', NAME], check=True, stdout=subprocess.DEVNULL) + wait_ready(NAME, 8080) + emit({'status': 'rolled_back', 'private_data_not_overwritten': True}) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('action', choices=['build', 'preflight', 'stop-preflight', 'switch', 'rollback']) + parser.add_argument('--package', type=Path) + parser.add_argument('--sha') + parser.add_argument('--commit') + arguments = parser.parse_args() + {'build': build, 'preflight': preflight, 'stop-preflight': stop_preflight, 'switch': switch, 'rollback': rollback}[arguments.action](arguments) diff --git a/docs/MARKET_OPPORTUNITIES_SYSTEM.md b/docs/MARKET_OPPORTUNITIES_SYSTEM.md index 8438eae6..6cdbe920 100644 --- a/docs/MARKET_OPPORTUNITIES_SYSTEM.md +++ b/docs/MARKET_OPPORTUNITIES_SYSTEM.md @@ -1,698 +1,85 @@ -# Market Opportunities System +# Market Opportunities v2 -> **Date:** October 2025 -> **Status:** Production -> **Module:** ML Bourse - Market Opportunities -> **Author:** Crypto Rebalancer Team +Manual stock-portfolio decision support. The authenticated scan explains source evidence, industry uncertainty and historical candidate screens. It does not generate broker orders or validated forward-return forecasts. -## Vue d'ensemble +## Contract and source selection -Le **Market Opportunities System** identifie automatiquement des opportunités d'investissement **en dehors du portefeuille actuel**, en analysant les gaps sectoriels et en suggérant des réallocations intelligentes. +- `GET /api/bourse/opportunities`: authenticated `get_required_user`, no-store JSON envelope, `data.version=2`. +- Parameters: `source`, `file_key`, `horizon`, `candidate_sector`, `sector_targets`, `min_gap_pct`. +- Active source and selected Saxo CSV come from the authenticated user's existing configuration. A conflicting request is rejected. A missing file never selects a newer CSV or demo account. +- CSV positions retain ISIN, exact listing, quote currency, asset class and quantity. Value currency comes from the original header, before the legacy connector removes its unit. Current verified FX converts those dated source values to USD. No indicative FX or parity fallback is accepted. +- Missing source valuations remain `null`, with the position retained. They are never turned into zero. Unknown values block full-portfolio sector bounds, concentration percentages and scenario valuation. +- Cash comes only from `_cash.json`. Missing cash remains unavailable. USD cash and non-USD cash are dated separately from securities. Current verified FX is used for non-USD cash. +- Import/file dates are labeled `imported_at`; financial `valuation_as_of` remains null when the CSV does not provide a trustworthy valuation date. Acquisition dates are not inferred from notification/value/import dates. +- Manual positions are supported with an explicit USD value contract; cash remains unavailable. Saxo API positions require an explicit USD valuation contract; unavailable contracts fail visibly rather than guess units. +- Private snapshots are request-scoped and never stored in a shared classification/result cache. `snapshot_id` prevents using a scenario after changes to the user, CSV contents/selection, cash record or verified FX. -**Objectifs:** -- Détecter les secteurs sous-représentés ou manquants -- Identifier les meilleures opportunités d'achat (actions + ETFs) -- Suggérer des ventes intelligentes pour financer les opportunités -- Simuler l'impact de la réallocation sur le risque et la diversification +## Industry evidence and independent dimensions -**Différence avec Recommendations Tab:** -- **Recommendations Tab**: Analyse positions existantes (BUY/HOLD/SELL) -- **Market Opportunities Tab**: Identifie nouvelles opportunités hors portfolio +`fund_exposure.py` accepts a fixed public iShares product catalog by exact ISIN. The adapter checks the matching page identity, the **Fund** table (never Benchmark), its own observation date, finite non-negative weights, duplicate sectors, and total weights. The observation must be no more than 45 days old and not future-dated. Receipts include issuer URL, observation/retrieval dates and a public-document hash. ---- +Supported public identities: -## Architecture +- iShares Core MSCI World UCITS ETF: IE00B4L5Y983, [issuer product page](https://www.ishares.com/uk/individual/en/products/251882/ishares-msci-world-ucits-etf-acc-fund). +- iShares Core MSCI EM IMI UCITS ETF: IE00BKM4GZ66, [issuer product page](https://www.ishares.com/uk/individual/en/products/264659/ishares-core-msci-em-imi-ucits-etf). -### Backend Components +Only visible sector rows are accepted. Omitted rows, cash and unrecognized labels stay unclassified; partial totals are not scaled to 100%. Unsupported funds expose the missing adapter explicitly. An index factsheet is not treated as actual fund composition. Issuer failures or changed page shapes do not invent weights. -``` -services/ml/bourse/ -├── opportunity_scanner.py # Scan secteurs S&P 500 vs portfolio -├── sector_analyzer.py # Analyse momentum/value/diversification -└── portfolio_gap_detector.py # Suggestions ventes intelligentes -``` - -### Frontend UI - -``` -static/saxo-dashboard.html -└── Onglet "Market Opportunities" - ├── Portfolio Gaps (Cards secteurs manquants) - ├── Top Opportunities (Tableau triable) - ├── Suggested Sales (Suggestions ventes) - └── Impact Simulator (Allocation avant/après) -``` - -### API Endpoint - -``` -GET /api/bourse/opportunities - ?user_id= - &horizon= - &file_key= - &min_gap_pct= -``` - ---- - -## Méthodologie de Scoring - -### 3-Pillar Scoring System - -Chaque gap sectoriel est scoré sur une échelle 0-100 selon 3 piliers: - -#### 1. Momentum Score (40%) - -**Indicateurs:** -- Price momentum 3M/6M (rendements récents) -- RSI (14-day) - Détection surachat/survente -- Relative strength vs SPY (performance relative) - -**Formule:** -```python -momentum_score = ( - price_momentum_score * 0.35 + - rsi_score * 0.35 + - relative_strength_score * 0.30 -) -``` - -**Interprétation:** -- **>70**: Momentum fort (secteur en tendance haussière) -- **50-70**: Momentum modéré -- **<50**: Momentum faible (attendre meilleur timing) - -#### 2. Value Score (30%) - -**Indicateurs:** -- P/E Ratio (valorisation vs moyenne marché) -- PEG Ratio (croissance ajustée, si disponible) -- Dividend Yield (rendement dividendes) - -**Formule:** -```python -value_score = ( - pe_score * 0.40 + - peg_score * 0.35 + - div_yield_score * 0.25 -) -``` - -**Interprétation:** -- **>70**: Secteur sous-évalué (bon point d'entrée) -- **50-70**: Valorisation neutre -- **<50**: Secteur surévalué (attendre correction) - -#### 3. Diversification Score (30%) - -**Indicateurs:** -- Corrélation avec positions existantes -- Volatilité relative -- Exposition sectorielle manquante - -**Formule:** -```python -diversification_score = ( - correlation_score * 0.50 + - volatility_score * 0.30 + - sector_exposure_score * 0.20 -) -``` - -**Interprétation:** -- **>70**: Excellente diversification (faible corrélation) -- **50-70**: Diversification modérée -- **<50**: Faible diversification (redondant avec positions) - -### Composite Opportunity Score - -```python -opportunity_score = ( - momentum_score * 0.40 + - value_score * 0.30 + - diversification_score * 0.30 -) -``` - -**Seuils décision:** -- **≥75**: Opportunité exceptionnelle (strong buy) -- **60-75**: Opportunité solide (buy) -- **50-60**: Opportunité acceptable (considérer) -- **<50**: Opportunité faible (éviter) - ---- - -## Secteurs Standard (GICS Level 1) - -Le système utilise les 11 secteurs GICS standard du S&P 500: - -| Secteur | Range Cible | ETF Proxy | Description | -|---------|-------------|-----------|-------------| -| **Technology** | 15-30% | XLK | Information Technology | -| **Healthcare** | 10-18% | XLV | Healthcare | -| **Financials** | 10-18% | XLF | Financial Services | -| **Consumer Discretionary** | 8-15% | XLY | Consumer Cyclical | -| **Communication Services** | 8-15% | XLC | Communication Services | -| **Industrials** | 8-15% | XLI | Industrials | -| **Consumer Staples** | 5-12% | XLP | Consumer Defensive | -| **Energy** | 3-10% | XLE | Energy | -| **Utilities** | 2-8% | XLU | Utilities | -| **Real Estate** | 2-8% | XLRE | Real Estate / REITs | -| **Materials** | 2-8% | XLB | Materials | - -**Note:** Les secteurs Yahoo Finance sont mappés automatiquement aux secteurs GICS standard. - ---- - -## Horizons Temporels - -### Short-term (1-3 mois) - -**Objectif:** Tactical plays, rotations sectorielles courtes - -**Pondérations scoring:** -- Momentum: 50% (priorité tendance court terme) -- Value: 20% -- Diversification: 30% - -**Recommandations:** -- ETFs sectoriels (liquidité élevée) -- Pas d'actions individuelles (trop volatiles) - -### Mid-term (6-12 mois) - -**Objectif:** Thématiques sectorielles, positionnement stratégique - -**Pondérations scoring:** -- Momentum: 40% (équilibré) -- Value: 30% -- Diversification: 30% - -**Recommandations:** -- Mix ETFs + actions solides (large caps) -- Secteurs en tendance structurelle - -### Long-term (2-3 ans) - -**Objectif:** Mégatrends, allocation stratégique durable - -**Pondérations scoring:** -- Momentum: 30% -- Value: 40% (priorité valorisation long terme) -- Diversification: 30% - -**Recommandations:** -- Actions quality (large caps stables) -- ETFs diversifiés -- Focus dividendes et croissance durable - ---- - -## Contraintes de Réallocation - -### Protection Portfolio (Hard Limits) - -| Contrainte | Valeur | Rationale | -|------------|--------|-----------| -| **Max vente par position** | 30% | Évite liquidation forcée | -| **Top N holdings protégés** | 2 | Préserve colonne vertébrale portfolio | -| **Détention minimale** | 30 jours | Évite wash sales, frais transaction | -| **Max allocation par secteur** | 25% | Diversification obligatoire | -| **Protection stop loss** | Validation | Respect trailing stops existants | - -### Logique de Détection des Ventes - -**Critères prioritaires:** - -1. **Over-concentration (>15% portfolio)** - - Score: +50 points si >15% - - Rationale: "Over-concentrated (X% of portfolio)" - -2. **Weak momentum (3M return <-10%)** - - Score: +50 points si <-10% - - Rationale: "Weak momentum (-X% 3M)" - -3. **Negative return (<0%)** - - Score: +20 points - - Rationale: "Negative momentum (-X% 3M)" - -4. **Near stop loss (-10% to -5%)** - - Score: ×0.5 (réduction score vente) - - Rationale: "Near stop loss (reduce caution)" - -**Exclusions:** -- Top 2 holdings (jamais vendus) -- Positions <30 jours (trop récentes) -- Positions protégées par stop loss - ---- - -## API Reference - -### Endpoint Principal - -```http -GET /api/bourse/opportunities -``` - -**Query Parameters:** - -| Paramètre | Type | Requis | Default | Description | -|-----------|------|--------|---------|-------------| -| `user_id` | string | OK | - | User ID (multi-tenant) | -| `horizon` | string | Error | `"medium"` | Time horizon: `short`/`medium`/`long` | -| `file_key` | string | Error | `null` | Saxo CSV file key (optional) | -| `min_gap_pct` | float | Error | `5.0` | Minimum gap percentage (0-50) | - -**Response Format:** - -```json -{ - "gaps": [ - { - "sector": "Utilities", - "current_pct": 0.0, - "target_pct": 12.0, - "gap_pct": 12.0, - "etf": "XLU", - "score": 87.3, - "confidence": 0.85, - "momentum_score": 82.0, - "value_score": 91.0, - "diversification_score": 89.0 - } - ], - "opportunities": [ - { - "symbol": "XLU", - "name": "Utilities Select Sector SPDR", - "sector": "Utilities", - "type": "ETF", - "score": 87.3, - "confidence": 0.85, - "action": "BUY", - "horizon": "medium", - "capital_needed": 12000.0, - "rationale": "Utilities sector gap: 12.0% underweight" - } - ], - "suggested_sales": [ - { - "symbol": "NVDA", - "current_value": 25000.0, - "sale_pct": 30.0, - "sale_value": 7500.0, - "rationale": "Over-concentrated (25.0% of portfolio)", - "stop_loss_safe": true - } - ], - "impact": { - "before": { - "Technology": 52.0, - "Healthcare": 10.0, - "Utilities": 0.0 - }, - "after": { - "Technology": 38.0, - "Healthcare": 10.0, - "Utilities": 12.0 - }, - "risk_before": 7.2, - "risk_after": 6.4, - "total_freed": 15000.0, - "total_invested": 12000.0 - }, - "summary": { - "total_gaps": 3, - "total_opportunities": 5, - "total_sales": 2, - "capital_needed": 12000.0, - "capital_freed": 15000.0, - "sufficient_capital": true - }, - "horizon": "medium", - "generated_at": "2025-10-28T14:30:00Z" -} -``` - ---- - -## Frontend UI Guide - -### Onglet "Market Opportunities" - -**Accès:** Dashboard Bourse → Tab "Market Opportunities" - -### 1. Horizon Selector - -``` -┌─────────────────────────────────────────────────┐ -│ Investment Horizon │ -│ [1-3 Months] [6-12 Months (OK)] [2-3 Years] │ -│ [ Scan for Opportunities] │ -└─────────────────────────────────────────────────┘ -``` - -**Actions:** -- Sélectionner horizon → Adapte scoring -- Cliquer "Scan" → Lance analyse complète - -### 2. Portfolio Gaps (Cards) - -``` -┌────────────┐ ┌────────────┐ ┌────────────┐ -│ Utilities │ │ Financials │ │ Real Estate│ -│ Score: 87 │ │ Score: 78 │ │ Score: 72 │ -│ 0% → 12% │ │ 0% → 8% │ │ 0% → 5% │ -│ Gap: 12% │ │ Gap: 8% │ │ Gap: 5% │ -│ ETF: XLU │ │ ETF: XLF │ │ ETF: XLRE │ -└────────────┘ └────────────┘ └────────────┘ -``` - -**Interprétation:** -- **Score >70** (vert): Opportunité forte -- **Score 50-70** (orange): Opportunité modérée -- **Score <50** (gris): Opportunité faible +Individual equities use a secondary Yahoo company-sector profile only when its listing and quote currency match. The URL and retrieval date are shown. An effective sector date is unavailable, and this weaker evidence is labeled separately from dated issuer look-through. -### 3. Top Opportunities (Table) +Geography is independent of industries. Company domicile is labeled as domicile, not revenue exposure. No verified fund geography adapter is presently included. Asset classes remain source metadata; broad ETF names and geographic labels never become economic sectors. Non-industry fund exposures remain outside industry coverage. An equity-only denominator is not guessed. -| Symbol | Sector | Score | Type | Capital Needed | Rationale | -|--------|--------|-------|------|----------------|-----------| -| XLU | Utilities | 87 | ETF | €12,000 | Utilities sector gap: 12.0% underweight | -| XLF | Financials | 78 | ETF | €8,000 | Financials sector gap: 8.0% underweight | +Coverage is explicitly scoped: all valued securities, or only the known-value subset when some positions lack values. The subset coverage is not full-portfolio coverage. -**Actions:** -- Tri par colonne (Symbol, Score, Capital) -- Top 10 opportunités affichées +## Targets and conservative intervals -### 4. Suggested Sales (Table) +Default industry targets reproduce the legacy normalized midpoint of static sector ranges. They are a **generic reference**, with no current-index or personal-policy claim. Complete user-entered targets total 100% and are labeled `personal_policy`. -| Symbol | Current Value | Sell % | Frees | Rationale | -|--------|---------------|--------|-------|-----------| -| NVDA | €25,000 | 30% | +€7,500 | Over-concentrated (25.0% of portfolio) | -| META | €10,000 | 50% | +€5,000 | High valuation, weak momentum | +With complete valuations, an industry's lower bound is its verified allocation. Its upper bound adds all unclassified securities weight, capped at 100%. A sector is certainly underweight only when `target - upper_bound` exceeds the selected threshold; certainly overweight only when `lower_bound - target` exceeds it. Unknown composition can make all assessments Indeterminate. Missing position valuations make all full-portfolio bounds unavailable. Zero certain gaps does not establish balance. -**Actions:** -- Visualisation des positions à réduire -- Capital libéré pour financer opportunités +Targets use all valued securities excluding saved cash, not an inferred equity sleeve. This scope is shown beside the target controls and in responses. -### 5. Impact Simulator +## Candidate exploration and historical ranking -``` -┌──────────────────────────────────────────────────┐ -│ Risk Score: 7.2 → 6.4 │ -│ Capital Freed: €15,000 │ -│ Capital Invested: €12,000 │ -├──────────────────────────────────────────────────┤ -│ Sector Allocation Changes │ -│ Technology: 52% → 38% (-14%) │ -│ Utilities: 0% → 12% (+12%) │ -│ Financials: 0% → 8% (+8%) │ -└──────────────────────────────────────────────────┘ -``` - -**Interprétation:** -- Avant/Après allocation sectorielle -- Impact sur risk score -- Capital net libéré - ---- - -## Exemples d'utilisation - -### Cas 1: Portfolio Tech-Heavy - -**Situation:** -- Tech: 52% (over-concentration) -- Utilities: 0% -- Financials: 0% -- Real Estate: 0% - -**Action:** -```javascript -// User: Clic "Scan Opportunities" (horizon: medium 6-12M) -``` - -**Résultat:** -- **3 gaps détectés:** Utilities, Financials, Real Estate -- **Top opportunity:** XLU (Utilities ETF) - Score 87 -- **Suggested sale:** NVDA 30% → Libère €8,000 -- **Impact:** Tech 52% → 38%, Utilities 0% → 12%, Risk 7.2 → 6.4 - -### Cas 2: Portfolio Bien Diversifié - -**Situation:** -- Allocation équilibrée sur 8 secteurs -- Aucun gap >5% - -**Action:** -```javascript -// User: Clic "Scan Opportunities" -``` - -**Résultat:** -``` -No significant sector gaps detected. Portfolio is well-diversified! -``` - -### Cas 3: Portfolio Sans Liquidité - -**Situation:** -- Gaps détectés: Utilities (12%), Financials (8%) -- Aucune position sur-concentrée -- Top 3 holdings protégés - -**Action:** -```javascript -// User: Clic "Scan Opportunities" -``` - -**Résultat:** -- Opportunités identifiées -- **Suggested sales:** Vide (aucune position éligible) -- Message: "No sales needed. Portfolio has sufficient liquidity..." - ---- - -## Maintenance & Evolution - -### P0 (Implémenté) - -[OK] Scan secteurs S&P 500 vs portfolio -[OK] Scoring 3-pillar (Momentum/Value/Diversification) -[OK] Suggestions ventes intelligentes (max 30%, top 2 protected) -[OK] Impact simulator (avant/après allocation) -[OK] Frontend UI complet (onglet dédié) - -### P1 (Q1 2026) - -- [ ] Top 3 stocks par secteur (pas seulement ETF) -- [ ] Backtesting suggestions (track performance) -- [ ] Alertes auto quand nouveaux gaps (>10%) -- [ ] Intégration module execution (buy/sell orders) - -### P2 (Q2 2026) - -- [ ] ML pour affiner scoring (historical winners) -- [ ] Corrélation cross-asset (bourse + crypto) -- [ ] Simulation Monte Carlo (scénarios multiples) -- [ ] Export rapport PDF (comme Recommendations) - ---- - -## Troubleshooting - -### Problème: Aucun gap détecté - -**Cause:** Portfolio déjà bien diversifié ou `min_gap_pct` trop élevé - -**Solution:** -```javascript -// Réduire le seuil min_gap_pct -const url = `/api/bourse/opportunities?user_id=jack&min_gap_pct=2.0`; -``` - -### Problème: Erreur "No positions found" - -**Cause:** Fichier Saxo CSV non chargé ou user_id incorrect - -**Solution:** -1. Vérifier que Saxo CSV est uploadé -2. Vérifier `localStorage.getItem('activeUser')` -3. Vérifier `file_key` dans URL +Exploration runs independently of certain gaps. The default universe contains the eleven curated sector ETFs. Choosing one sector additionally examines all stocks in that sector's existing curated list. This is not the whole investable market or a suitability screen. -### Problème: Scores tous à 50 +Exact mapped held listings are excluded. A matching candidate ISIN, when the provider supplies it, also excludes a different listing of the same instrument. Missing candidate ISIN leaves that deduplication incomplete. Fund constituent overlap is unassessed and disclosed. Stock candidates require their actual reported industry to match the curated industry. ETF names describe mandate; they do not establish a 100% industry composition. -**Cause:** Données Yahoo Finance indisponibles (rate limit ou symbole invalide) +Prices come from the existing verified adjusted-price pipeline, with no synthetic or proxy series. Today is excluded, listing quote currencies are checked, and complete sessions through the latest completed exchange date are required. Unsupported calendars, holes, stale observations, ambiguous currencies and insufficient history produce explicit reasons. Non-USD histories use dated actual FX with the existing maximum three-day backward alignment tolerance, not current spot FX. -**Solution:** -- Attendre quelques minutes (rate limit Yahoo Finance) -- Vérifier logs backend: `logs/app.log` -- Vérifier que ETF proxy est valide (XLU, XLF, etc.) +Historical windows: short 42 sessions, medium 189, long 756. Their associated holding horizons remain planning labels, not tested return predictions. -### Problème: Suggested sales vide malgré gaps - -**Cause:** Aucune position éligible (toutes protégées ou récentes <30j) - -**Solution:** -- Normal si top 2 holdings représentent >80% portfolio -- Vérifier dates d'acquisition des positions -- Réduire protection (modifier `TOP_N_PROTECTED` dans code) - ---- - -## Dépendances - -### Backend - -```python -# services/ml/bourse/opportunity_scanner.py -from services.ml.bourse.sector_analyzer import SectorAnalyzer - -# services/ml/bourse/sector_analyzer.py -from services.ml/bourse.data_sources import StocksDataSource -from services.ml.bourse.technical_indicators import TechnicalIndicators -import yfinance as yf # Free Yahoo Finance API - -# services/ml/bourse/portfolio_gap_detector.py -from services.ml.bourse.stop_loss_calculator import StopLossCalculator ``` - -### Frontend - -```javascript -// Dépendances globales -window.API_BASE_URL // Config API -window.safeFetch() // HTTP wrapper -localStorage.getItem('activeUser') // Multi-user -formatCurrency() // Formatage devises +score = clip(50 + 15 * mean(daily USD returns) + / sample_std(daily USD returns) * sqrt(window_sessions), 0, 100) ``` ---- +A full window plus one starting close is required. Return, annualized historical volatility, dates, currency and method accompany the score. No value/dividend-yield composite, confidence probability or invented diversification component is added. Ranks are suppressed if candidates end on different dates. Scores describe historical adjusted prices and are not forward validated. -## Sources de Données +## Holding reviews and manual scenarios -### Yahoo Finance (yfinance) +Holding reviews show generic concentration triggers (10% only with complete valuations), missing classification/valuation evidence and missing acquisition data. They do not turn unknown holding periods or unknown stop orders into automatic sale permission. Automatic sales remain empty with a specific eligibility explanation; absence of a sale is not a favorable portfolio judgment. -**Utilisé pour:** -- Prix OHLCV (ETFs sectoriels) -- Fundamental data (P/E, PEG, Dividend Yield) -- Benchmarks (SPY) +`POST /api/bourse/opportunities/scenario` performs read-only arithmetic. No persistence, order service, broker execution, commit or deployment is called. The request contains the selected source/settings and a manual `scenario` with: -**Limites:** -- Rate limit: ~2000 requests/hour -- Délai données: 15 min (free tier) -- Pas de top holdings ETF (future P1) +- current `snapshot_id`; +- 1–50 unique changes: `side`, stable holding/candidate `id`, positive finite `amount_usd`; +- explicit non-negative `costs_usd`, `slippage_pct` from 0 to 10; +- `acknowledge_dated_values: true`; +- optional boolean `include_history`. -**Fallback:** -- Cache local (TTL 4h pour secteurs) -- Scores neutres (50) si données manquantes - -### S&P 500 Secteurs (Static) - -**Configuration:** -- 11 secteurs GICS Level 1 -- Targets ranges basés sur composition S&P 500 -- ETF proxy par secteur (XLK, XLV, etc.) - -**Mise à jour:** -- Annuelle (composition secteurs S&P change peu) -- Fichier: `opportunity_scanner.py` → `STANDARD_SECTORS` - ---- - -## Sécurité & Permissions - -### Multi-Tenant Isolation - -```python -# Backend: TOUJOURS user_id dans query -@router.get("/api/bourse/opportunities") -async def get_market_opportunities( - user_id: str = Query(..., description="User ID") # Required! -): - # ... -``` - -```javascript -// Frontend: TOUJOURS activeUser -const activeUser = localStorage.getItem('activeUser') || 'demo'; -const url = `/api/bourse/opportunities?user_id=${activeUser}`; -``` +Sales cannot exceed held snapshot value. Purchases must be current screened candidates. Combined purchases and costs cannot exceed saved cash plus sales. Securities plus cash after the changes must equal before total minus estimated friction. No executable share quantity, purchase capital recommendation, fund-composition assumption, tax-lot rule or stop-order assurance is created. Purchased ETFs remain unclassified without a verified decomposition. -### Data Privacy - -- **Pas de logs positions** (seulement agrégats) -- **Pas de sharing inter-users** -- **Cache isolé par user** - ---- - -## Performance - -### Backend - -**Optimisations:** -- Cache Yahoo Finance data (4h TTL) -- Async/await pour paralléliser fetches -- Limit top 10 opportunities (pas tout S&P 500) - -**Latence moyenne:** -- Cold start (no cache): ~8-12s -- Warm cache: ~2-3s - -### Frontend - -**Optimisations:** -- Lazy loading onglet (charge au clic) -- Pas de refresh auto (user trigger manual) -- Render incrémental (gaps → opportunities → sales → impact) - -**UX:** -- Loading states pendant scan -- Feedback immédiat (bouton "Scanning...") -- Success message ("[OK] Scan Complete") - ---- - -## Logs & Debugging - -### Backend Logs - -```bash -# Lire logs scan opportunities -Get-Content logs\app.log -Wait -Tail 50 | Select-String "Market opportunities" - -# Logs typiques -# INFO: Market opportunities requested (user=jack, horizon=medium) -# INFO: Detected 3 sector gaps -# INFO: Scan complete: 3 gaps scored, top 3 selected -``` - -### Frontend Debug - -```javascript -// Console logs (si debugLogger enabled) -debugLogger.info('Loading market opportunities (horizon: medium)'); -debugLogger.info('Market opportunities loaded:', data); - -// Inspect last data -console.log(lastOpportunitiesData); -``` +Optional historical risk requires exact USD histories for **all** nonzero positions in both scenarios and common return intervals with the same start and end dates. The full selected signal window and at least 60 daily intervals are required. Historical volatility uses fixed current USD weights, zero-return cash and a daily-rebalanced approximation. Candidate correlation is measured against the actual before portfolio on those same intervals. Partial holdings are not used as a portfolio proxy. Missing histories leave volatility unavailable with counts/reasons. No validated 0–100 Risk Score is implemented. ---- +## Frontend and confidentiality -## Changelog +The Market Opportunities tab uses `static/components/market-opportunities.js`. It checks authenticated account/source/CSV context and request revision before and after asynchronous body decoding, clears obsolete results and prevents markup injection through provider text or URLs. All visible labels and errors are in English. -### v1.0 (October 2025) +The existing Recommendations tab and its independent ML work are preserved. API v2 intentionally replaces the old opportunities payload; external clients must migrate to the versioned contract. Export returns only authorized aggregate audit controls and method metadata, never holdings, amounts, account identifiers, file names, private hashes or keys. -- [OK] Initial release -- [OK] 3-pillar scoring system -- [OK] Intelligent sales suggestions -- [OK] Impact simulator -- [OK] Full UI integration +## Validation and open data work ---- +Synthetic test fixtures verify strict CSV selection, currencies, null valuations, partial issuer receipts, interval bounds, identity matching, real-history requirements and cash conservation. API tests verify authenticated context, validation and no-store responses. DOM tests cover missing evidence, provider text and late responses. Public issuer pages and US/Swiss price series are checked separately from a read-only real-account audit. -*Documentation générée pour Market Opportunities System - Crypto Rebalancer* -*Pour questions: Voir CLAUDE.md section "Features Avancées"* +Remaining data work: issuer adapters for unsupported funds, trustworthy financial valuation dates/complete CSV values, exact candidate ISIN coverage, fund constituent overlap, personal tax/holding/stop-order constraints, personal target policy, and fund geography. Their absence stays visible. No new provider account, credentials, environment or private data image is needed for the implemented adapters. diff --git a/docs/audit/MARKET_OPPORTUNITIES_AUDIT_2026-10-01.md b/docs/audit/MARKET_OPPORTUNITIES_AUDIT_2026-10-01.md new file mode 100644 index 00000000..04c4c3fe --- /dev/null +++ b/docs/audit/MARKET_OPPORTUNITIES_AUDIT_2026-10-01.md @@ -0,0 +1,77 @@ +# Market Opportunities — diagnostic, refonte et contrôles du 1er octobre 2026 + +Le module est désormais refondu dans le checkout ML existant. Le code n'est pas déployé : production et aperçu 8083 conservent leurs images et dates de démarrage. La vérification réelle du nouveau moteur a été exécutée en mémoire dans le conteneur d'aperçu, en lisant uniquement les données du compte autorisé. Aucun montant, nom de position, fichier sélectionné, ISIN privé, clé ou hash de portefeuille n'est reproduit ici. + +## Diagnostic et preuves + +| Étape | Fait vérifié | Cause / conséquence | +|---|---|---| +| Source → CSV | Source Saxo CSV sélectionnée ; positions et cash rattachés au même fichier | Pas de preuve d'un compte démo ou d'un autre CSV utilisé lors du contrôle réel | +| CSV → positions | 32 lignes de fichier : 30 positions identifiées et 2 lignes de synthèse ; 30 ISIN ; 15 actions et 15 ETF | Les réponses normalisées anciennes perdaient ISIN, asset class, dates et devises de cotation | +| Valeurs | 28 valeurs numériques et 2 valeurs de position absentes | L'ancien parseur transformait l'absence de valeur en zéro : incertitude financière masquée | +| Devises | Valeur du CSV explicitement en EUR ; cotations CHF, EUR, GBP et USD | Ne pas convertir la valeur du compte avec la devise de l'instrument ; pas de parité ni de taux de secours pour le nouveau module | +| Dates | Import 2026-09-22 10:00:36 ; cash sauvegardé 2026-09-22 10:01:15.693200Z ; aucune date d'acquisition des 30 positions | La date d'import n'est pas une date financière confirmée. Les dates de notification/valeur ne prouvent pas une date d'achat | +| Identité | Mapping explicite place/cotation ; vérification de devise des historiques | Une ligne suisse déclarée USD ne peut pas emprunter silencieusement l'historique CHF. Le fournisseur peut manquer pour la cotation exacte | +| Classification ancienne | Environ 44,08 % du montant valorisé ventilé ; 55,92 % non ventilé ; fonds diversifiés sans décomposition ; profils sans date sectorielle effective | Une catégorie géographique ne doit pas remplir un secteur économique. Une étiquette ETF ne prouve pas les poids de ses secteurs | +| Écarts anciens | 11 assessments Indeterminate dans l'aperçu ; 0 écart certain | L'incertitude de composition explique ces résultats. Elle ne prouve pas un portefeuille équilibré | +| Candidats anciens | Exploration déclenchée seulement après les écarts certains : 0 candidat | Défaut de conception : une information sectorielle manquante empêchait même l'examen du marché | +| Classement ancien | Fenêtres historiques et composantes partielles ; diversification absente ; problème d'unité du dividend yield dans le composite historique | Ce composite ne constitue pas une preuve financière. Le nouveau module l'abandonne au profit d'un écran historique explicite | +| Ventes anciennes | 2 positions protégées, 28 exclues pour date de détention manquante ; aucune vente éligible | Critères restrictifs avant analyse ; absence d'éligibilité, pas preuve de qualité du portefeuille. Cash absent du scan ; zéro besoin de financement pouvait apparaître suffisant | +| Simulation ancienne | Sans achats appliqués ; Risk Score jamais calculé ; risque latent de vente recherchée par `symbol` sur des positions ayant `instrument_id` | Pas de changement ne mesure pas un bénéfice de réallocation ; risque de conservation des montants à corriger | +| Logs / environnements | Aucun ERROR/Traceback dans la fenêtre d'audit initiale ; conteneurs toujours en fonctionnement et inchangés au contrôle final | Les zéros venaient de chemins fonctionnels silencieux ; l'aperçu ajoutait des limites visibles mais gardait la chaîne restrictive | + +Le constat des deux valeurs absentes complète et corrige le premier diagnostic : les deux zéros observés dans le parseur ne sont pas des valorisations vérifiées. Le nouveau module conserve les deux positions avec une valeur indisponible. Il bloque les bornes sectorielles du portefeuille complet et sa simulation plutôt que donner un total artificiel. + +## Résultat du moteur refondu sur le compte réel + +- 30 positions conservées, dont 30 avec ISIN, 0 avec date d'acquisition et 2 sans valeur source. +- Cohérence source/CSV/cash confirmée ; aucune autre sélection utilisée. +- 15 classifications d'actions issues d'un profil secondaire explicitement identifié, 1 ventilation de fonds acceptée depuis l'émetteur avec date et ISIN exacts, 14 fonds sans décomposition acceptée. +- Couverture **52,49 % du sous-ensemble valorisé** ; reste non ventilé **47,51 % de ce sous-ensemble**. La couverture du portefeuille complet est inconnue puisque deux valeurs manquent. Ce chiffre n'est pas une preuve de meilleure diversification. +- 11 catégories sectorielles Indeterminate, avec bornes complètes indisponibles. +- Univers initial : 11 ETF sectoriels examinés, 3 cotations déjà détenues exclues et 8 candidats avec scores effectivement calculés sur des historiques USD complets. Classement descriptif, sans prévision validée ni instruction d'achat. +- 0 vente automatique, avec les limites d'éligibilité expliquées. Le contrôle réel n'a soumis aucun scénario financier ni aucun ordre. La conservation des montants et les historiques de scénario sont vérifiés par des tests synthétiques identifiés comme tels. + +## Lots : décisions prises, critères et données encore nécessaires + +| Lot | Mise en œuvre / suite | Sources requises | Critères de qualité | +|---|---|---|---| +| 1 — Contrat du portefeuille | Implémenté : sélection stricte, champs riches, valeur/devise distinctes, cash du même CSV, dates et nulls | CSV sélectionné et son cash existant ; FX public vérifié | Jamais substituer un fichier/utilisateur ; conserver les positions non valorisées ; pas de valeur ou date inventée | +| 2 — Classification | Implémenté : secteurs, géographie et classes séparés ; deux adaptateurs publics iShares, un applicable au contrôle réel | Émetteur, ISIN exact, table Fund, observation datée ; profil secondaire pour une action | Ventilation ≤45 jours, non future, poids finis et cohérents, résidu conservé. Jamais remplacer le fonds réel par son indice | +| 3 — Écarts et candidats | Implémenté : bornes conservatrices, cible générique explicitée, politique personnelle optionnelle, exploration indépendante des écarts | Référence codée documentée ; politique personnelle si saisie ; univers public limité et profils courants | Aucun équilibre déduit d'un zéro ; raisons d'exclusion visibles ; ISIN manquant et chevauchement de fonds signalés | +| 4 — Historique / classement | Implémenté : USD historique, calendrier exact, jours complets, fenêtres entières, score et méthode affichés | Cours ajustés Yahoo et FX historiques réels pour la cotation exacte | Pas de proxy silencieux ; rejet des trous/devise ambiguë/staleness ; pas de rang entre dates différentes ; pas de probabilité ou rendement futur inventés | +| 5 — Revue et scénario | Implémenté : revues de détention et simulation manuelle avec IDs, coûts et conservation du cash ; volatilité et corrélation historiques optionnelles | Valeur de chaque position, cash cohérent ; tous les historiques exacts pour le risque ; frais/slippage saisis | Pas de vente automatique sans contraintes vérifiées ; pas de capital créé ; aucun ordre ; indisponible si une valeur ou un historique nécessaire manque | +| 6 — Sources restantes | Ouvert : adaptateurs supplémentaires pour les 14 fonds sans source acceptée ; dates financières et deux valorisations manquantes | Tables réelles du fonds ou fichiers de l'émetteur datés. Franklin et UBS demandent une vérification dédiée ; une fiche d'indice ne suffit pas | Documenter part/class/ISIN, unité, date, source et couverture. Ne pas viser 100 % par une approximation | +| 7 — Validation de l'interface | Tests DOM terminés ; validation visuelle/API du module déployé encore à faire | Publication autorisée du paquet dans l'aperçu 8083 existant, avec les mêmes montages privés RO | Refaire les seuls contrôles agrégés autorisés sur jack, vérifier mobile/desktop et erreurs de source/scénario ; production reste inchangée | + +Les acquisitions, lots fiscaux, stop orders, contraintes personnelles de vente, géographie réelle des revenus/fonds et chevauchements de constituants restent non vérifiés. Les fonds achetés dans un scénario ne reçoivent pas une composition supposée. Aucun Risk Score 0–100 validé n'est ajouté ; la volatilité historique éventuelle est une mesure distincte. + +## Validation et préservation + +Les tests couvrent sélection/fichiers/devises, valeurs absentes et format pandas 3, identité/date/poids des fonds, bornes, conservation et dépassement de cash, identité de vente, fenêtres/historiques manquants, dates communes de rendement, transmission API, réponses tardives et injection DOM. Les observations publiques iShares datées du 29 septembre 2026 ont été relues ; leurs dix lignes visibles couvrent 98,04 % et 97,97 %, sans renormalisation. Une cotation publique US et une cotation publique suisse ont validé le chemin de calendrier et FX historique jusqu'au 30 septembre 2026, avec exclusion de la journée courante. + +Les autres endpoints du fichier API sont conservés octet pour octet. Les modifications fonctionnelles du chantier ML hors de ce module sont préservées. Le contrôle par empreintes a révélé qu'un test préexistant supprimait et recréait `config/score_registry.json` : son `last_updated` a été régénéré pendant les tests. Ce fichier était déjà modifié avant l'intervention ; aucune restauration de son ancien timestamp non récupéré n'est prétendue. Le test utilise désormais `tmp_path` pour éviter toute nouvelle écriture dans le registre du checkout. Le registre est exclu du paquet selon la règle préexistante ; aucune configuration de production n'a changé. + +Le paquet de code prépare aussi un garde-fou d'aperçu qui autorise uniquement le nouveau POST de calcul tout en bloquant entraînements, configuration et ordres. Le garde-fou actuellement déployé n'est pas modifié. Les sources privées et clés restent sur robot2, hors des images et de ce rapport. Aucun commit, push, nouveau worktree/environnement ou changement de production n'a été réalisé. + +La preuve des tests et le manifeste de paquet sont enregistrés dans les sorties locales du checkout isolé. Les contrôles sur le compte réel se limitent aux agrégats autorisés. L'export de l'interface suit la même limite : pas de noms de positions, montants, identifiants de fichier ou hashes privés. + +## Reprise et validation du 2 octobre 2026 + +Les derniers correctifs sont validés : 72 tests ciblés et 3286 tests unitaires réussis, 13 ignorés, couverture 49,46 %. Les 156 tests frontend précédents restent applicables, sans changement JS depuis leur succès. Contrôle de préservation : aucune suppression parmi 1440 fichiers, sept changements attendus dont l’exception de timestamp déjà documentée ; autres endpoints API byte-identiques. + +Le compte réel a été revérifié en mémoire, sans déploiement : 30 positions, 30 ISIN, 2 valeurs absentes ; sélection et cash concordants ; 52,51 % de couverture du seul sous-ensemble valorisé, 47,49 % non ventilé, 11 assessments Indeterminate ; 8 candidats historiques calculés, 3 déjà détenus et zéro vente automatique. Les dates de source restent celles du contrôle précédent. La couverture du portefeuille complet reste inconnue et la simulation réelle reste bloquée. + +Images et démarrages de production/aperçu inchangés au contrôle. Publication sur le seul aperçu 8083 et vérification visuelle/API restent soumises à autorisation. Le relais du 2 octobre et les journaux datés remplacent l’état de validation interrompu du relais du 1er octobre. Aucun commit, push ou ordre. + +## Contrôle du module publié et correction du transport + +La publication sur le seul aperçu 8083 a été autorisée le 2 octobre. La première validation déployée a découvert un défaut absent des premiers mocks : safeFetch retourne déjà {ok,status,data,error}, tandis que le contrôleur appelait response.json(). L’API répondait 200 mais l’affichage échouait avec une erreur technique. Scan et scénario consomment désormais response.data et response.error. Les tests de contrat utilisent le vrai helper ; ils couvrent succès, erreur de validation, scénario synthétique et changement de contexte pendant son décodage asynchrone. Suite frontend complète : 16 suites, 159 tests réussis. La preuve backend reste valide (aucun changement Python depuis 3286 tests et 49,46 % de couverture). + +Les refus API déployés ont réussi : token absent, identité incohérente, autre CSV, Europe comme cible sectorielle, écriture de configuration et simulation avec valorisations manquantes. La validation desktop/mobile du contrôleur corrigé reste à terminer. L’environnement, les neuf montages et la production sont conservés ; le conteneur initial v13 est gardé arrêté pour retour arrière. Aucune donnée privée n’est incluse dans la nouvelle image. + +## Lisibilité et contrôle fonctionnel final + +Le contrôleur corrigé a réussi les scans réels en 1440 px et 390 px : même utilisateur/CSV, 30 positions, 11 assessments, 11 candidats examinés dont 8 avec scores calculés sur des fenêtres finissant le 1er octobre ; exclusions, manque de valorisation, absence de vente automatique et export agrégé correctement affichés. Les derniers ajustements donnent aux tableaux une largeur minimale avec défilement interne sur mobile et traduisent le scope technique en anglais lisible. Les 159 tests frontend passent après ces ajustements. Les deux faux échecs du premier contrôle r2 étaient des assertions de format : no-store reste présent dans no-cache, no-store, must-revalidate et les placeholders -- et — expriment le même effacement. Aucun middleware n’a été modifié. + +Les captures de contrôle masquent avant création les paragraphes contenant fichier/cash ainsi que toutes les tables d’identités/instruments. Seuls agrégats et pixels sans positions ni montants sont retournés. Le résultat détaillé de publication est sauvegardé séparément après validation de cette dernière image. diff --git a/docs/audit/MARKET_OPPORTUNITIES_DEPLOYMENT_2026-10-02.md b/docs/audit/MARKET_OPPORTUNITIES_DEPLOYMENT_2026-10-02.md new file mode 100644 index 00000000..8ae64b14 --- /dev/null +++ b/docs/audit/MARKET_OPPORTUNITIES_DEPLOYMENT_2026-10-02.md @@ -0,0 +1,57 @@ +# Market Opportunities — publication et relais final du 2 octobre 2026 + +État courant : module v2 publié et validé sur l’aperçu ML existant, http://192.168.1.200:8083/static/bourse-recommendations.html, onglet Market Opportunities. L’utilisateur a autorisé cette publication et ses contrôles par « ok, go ». Ce rapport remplace les mentions provisoires de publication/validation encore en préparation des relais précédents. Aucun déploiement de production, commit, push, ordre financier ou nouvel environnement. + +## Publication vérifiée + +- Image actuelle : sha256:4ffe67a32a9f33c4b64a1a1221edffdf8e7e945c6cafcd78c15cfec36fd6d0dd. +- Tag : smartfolio-ml-preview:market-opportunities-v2-20261002-r3. +- Démarrage : 2026-10-02T09:36:15.871443053Z ; état healthy ; healthz HTTP 200. +- Paquet : outputs/ml-reliability/ml-reliability-market-opportunities-v2-2026-10-02-r3.zip. +- SHA256 : 4bf5786e0a315fd64ab2e18c30654c8e96bb085a71d317e358c4acd51f475042 ; 356 entrées vérifiées, 62 artefacts publics. Les paquets sans r3 sont dépassés. +- 114 fichiers réellement servis comparés au paquet, y compris le guard ; transformation HTML connue limitée à l’injection idempotente du bandeau d’aperçu. +- Environnement et neuf montages identiques à l’aperçu initial ; données de jack et auth montées RO ; rootfs RO. Aucun fichier de compte ni .env ajouté à l’image. Aucun secret retourné dans les sorties. +- Scheduler et entraînement automatique désactivés. Guard : 13 POST existants conservés et seul calcul scenario ajouté ; PUT/PATCH/DELETE scenario refusés ; écritures financières/configuration refusées sur handler synthétique, isolation des cookies vérifiée. Aucun appel réel d’ordre financier. + +Production inchangée : smartfolio-api, image sha256:0c724b8b8ff87d73885bf0e109276c7b1da2b211f3c9c497b33f9bcda544f2aa, démarrage 2026-09-28T09:01:59.068663028Z, en fonctionnement et healthz HTTP 200. Aucun ERROR, traceback ou échec Market Opportunities dans la fenêtre des logs de l’image finale ; aucun log brut exporté. + +## Compte réel et interface + +Vérifications sur jack, source bourse et CSV réellement sélectionnés, cash du même CSV. Aucune lecture d’un autre fichier de positions ni d’un compte démo. Les essais de mauvaise identité/fichier sont refusés avant chargement. Seuls les agrégats autorisés sont présents dans les rapports. + +- 30 positions, 30 ISIN, aucune date d’acquisition et 2 positions sans valeur source. +- Couverture sectorielle : 52,51 % du sous-ensemble valorisé ; 47,49 % non ventilé dans ce sous-ensemble. Le portefeuille complet ne peut pas être mesuré puisque deux valeurs manquent. +- 15 classifications secondaires de sociétés, 1 ventilation de fonds datée acceptée, 14 fonds sans ventilation vérifiée acceptée. +- 11 évaluations sectorielles Indeterminate avec bornes complètes indisponibles ; zéro écart certain ne démontre pas l’équilibre. +- 11 ETF sectoriels examinés : 8 avec scores effectivement calculés sur 189 sessions USD, fenêtre finissant le 1er octobre 2026 ; 3 cotations déjà détenues exclues et expliquées. Ce sont des candidats descriptifs, pas des recommandations d’achat validées. +- Zéro vente automatique, raison explicite. Simulation réelle refusée avec explication des valorisations manquantes ; aucun résultat fictif de scénario montré. +- Import 2026-09-22T10:00:36, date financière de valorisation inconnue, cash 2026-09-22T10:01:15.693200Z ; valeurs EUR et cotations CHF/EUR/GBP/USD. Import, cash et données de marché ne sont pas synchrones ; limites visibles. + +Desktop 1440 px et mobile 390 px : scan HTTP 200, source/identité concordantes, résultats et raisons affichés, méthode et absence de prévision expliquées, aucun pageerror ou débordement horizontal du document. Les tableaux ont un défilement interne sur petit écran. Un changement d’horizon supprime les anciens résultats. L’export contient uniquement les contrôles agrégés approuvés, aucun nom/ISIN/valeur de position, nom de CSV, hash privé ou clé. + +Refus API validés : token absent, identité incohérente, autre CSV demandé, Europe comme cible sectorielle, écriture de configuration et scénario avec valeurs manquantes. Cache-Control inclut no-store avec no-cache et must-revalidate. Les données privées restent sur robot2. Les captures de contrôle sont masquées avant création pour supprimer fichier/cash et toutes les identités/tables d’instruments ; les vues masquées desktop/mobile ont été inspectées. Aucun pixel de position ou montant n’est exporté. + +## Corrections et tests + +Le contrôle déployé initial a révélé un défaut de transport : safeFetch retourne déjà le JSON dans data, tandis que le contrôleur attendait response.json(). Scan et scénario consomment désormais son vrai contrat et les erreurs visibles. Les mocks initiaux ont été remplacés/complétés par des tests avec le vrai helper, dont décodage tardif, succès, erreur et scénario synthétique. La suite frontend finale passe : 16 suites et 159 tests. La lisibilité mobile a été ajustée après inspection des captures. Aucun middleware commun n’a été modifié. + +Backend : 3286 tests unitaires réussis, 13 ignorés, couverture 49,46 %. Preuve réutilisée car aucun changement Python depuis cette validation. 72 tests ciblés incluent les alias suisses et refus de substitutions de devise/ISIN. Les calculs de scénarios sont testés sur fixtures identifiées comme synthétiques, jamais présentées comme résultat réel. + +Préservation : les autres endpoints API restent octet pour octet identiques à leur sauvegarde préintervention. Aucune suppression des 1440 fichiers de référence. Les modifications ML existantes ont été conservées dans le paquet et les artefacts publics montés ont été comparés avant bascule. Exception historique : un ancien test a régénéré last_updated dans config/score_registry.json, déjà modifié avant cette intervention. Original exact non récupéré ; test corrigé pour utiliser tmp_path, registre exclu de la livraison. Ne pas restaurer aveuglément ce fichier. + +## Limites et prochaine étape + +Données encore nécessaires : les deux valorisations manquantes avec date financière fiable, ventilations réelles/datées des 14 fonds restants, politique personnelle si souhaitée, acquisitions/lots fiscaux/stop orders et autres contraintes de vente. Les chevauchements de constituants et la géographie économique restent non calculés. Aucun Risk Score 0–100 validé ni rendement futur validé n’est ajouté. Il faut progresser par sources vérifiables et conserver les indisponibilités visibles, sans proxy silencieux. Aucune écriture dans les données de compte n’est autorisée par cette publication. + +Retour arrière initial disponible : conteneur arrêté smartfolio-ml-preview-api-before-market-opportunities-20261002, image v13 sha256:88d929489739ef104a7aeec255af1f3366b7424315db658c87611b99682479fe. Conserver l’image actuelle avant toute restauration, contrôler les identités/labels, puis ne renommer/redémarrer que les conteneurs d’aperçu ; production reste intouchée. Attention : le rollback du script r3 restaure r2, tandis que ce conteneur initial permet de revenir à v13. Les conteneurs intermédiaires sont gardés arrêtés ; pas de nettoyage automatique. + +## Reprise et fichiers utiles + +- Diagnostic/méthode : docs/audit/MARKET_OPPORTUNITIES_AUDIT_2026-10-01.md ; docs/MARKET_OPPORTUNITIES_SYSTEM.md. +- Moteur : services/ml/bourse/{market_snapshot,fund_exposure,market_analysis}.py ; api/ml_bourse_endpoints.py. +- UI : static/components/market-opportunities.js ; static/bourse-recommendations.html. +- Preuves locales : outputs/market-opportunities/{runtime-final-2026-10-02,deployed-functional-checks-2026-10-02,package-verification-2026-10-02,validation-summary-2026-10-02}.json ; frontend-tests-2026-10-02.log ; unit-with-coverage-2026-10-02.log. +- Scripts publics de publication/contrôle : D:/Python/smartfolio/outputs/market-opportunities/deploy_preview_20261002_r3.py et browser_validate_20261002.py ; copies sur robot2 dans /home/jack/smartfolio-ml-preview-20260930/. Build/conclusions publics dans market-opportunities-20261002-r3/ ; données/captures privées restent sur robot2. +- Checkout existant : C:/Users/jacks/.codex/worktrees/ml-reliability-current/smartfolio. Réutiliser .venv et node_modules du projet principal ; ne pas relancer l’installateur de staging. + +Prochaine action : retour utilisateur sur cet aperçu ou lot de données vérifiables restant. Ne pas publier en production, committer, pousser ni créer d’ordre sans autorisation distincte. diff --git a/docs/audit/MARKET_OPPORTUNITIES_RELAY_2026-10-02.md b/docs/audit/MARKET_OPPORTUNITIES_RELAY_2026-10-02.md new file mode 100644 index 00000000..44f4f401 --- /dev/null +++ b/docs/audit/MARKET_OPPORTUNITIES_RELAY_2026-10-02.md @@ -0,0 +1,31 @@ +# Market Opportunities — reprise validée le 2 octobre 2026 + +La reprise locale a terminé les contrôles interrompus. Aucun déploiement, commit, push, ordre ou nouvel environnement. La prochaine étape est une validation API/visuelle après autorisation de publication sur l’aperçu existant 8083 ; production 8080 reste inchangée. + +## État vérifié + +- 72 tests ciblés réussis, dont alias suisse sans ISIN, mauvais ISIN, mauvaise devise et refus d’un historique CHF pour une cotation USD. +- Suite unitaire complète : 3286 réussis, 13 ignorés, 27 avertissements ; couverture 49,46 % supérieure au seuil de 30 %. Journal : outputs/market-opportunities/unit-with-coverage-2026-10-02.log. +- Interface : preuve précédente réutilisée, aucun changement JS depuis : 16 suites et 156 tests réussis ; outputs/market-opportunities/frontend-tests.log. +- 1440 fichiers de référence : aucune suppression ; seuls les sept fichiers déclarés diffèrent de la référence. Autres endpoints du fichier API octet pour octet identiques à la sauvegarde initiale. +- Exception historique conservée : config/score_registry.json a vu son last_updated régénéré par un ancien test. Original exact inconnu ; ne pas restaurer aveuglément. Test isolé dans tmp_path ; registre exclu du paquet. + +## Compte réel — agrégats uniquement + +Vérification du moteur en mémoire dans le conteneur d’aperçu existant, sans installation et sans export des données privées. Source et cash concordants avec le CSV sélectionné par jack. 30 positions, 30 ISIN, aucune date d’acquisition, 2 valorisations manquantes. Import 2026-09-22T10:00:36 ; date de valorisation financière inconnue ; cash 2026-09-22T10:01:15.693200Z. Valorisation EUR et cotations CHF/EUR/GBP/USD. + +Couverture 52,5119 % du sous-ensemble valorisé ; 47,4881 % non classifié de ce sous-ensemble. 15 profils secondaires d’actions, 1 décomposition de fonds datée acceptée, 14 sans source acceptée. 11 évaluations sectorielles Indeterminate. 11 candidats examinés : 8 avec scores historiques calculés, 3 déjà détenus, 0 indisponible. Aucune vente automatique. Scénario réel bloqué par les valeurs manquantes. La variation légère de couverture depuis le 1er octobre ne prouve pas une amélioration de diversification. + +Les limites restent visibles : composition de 14 fonds, dates financières, acquisitions/fiscalité/stop orders, politique personnelle, recouvrement des constituants, géographie économique et Risk Score validé indisponibles. Horizons et scores sont descriptifs de l’historique, sans rendement futur validé. Les simulations mécaniques ont été testées sur fixtures explicites, pas affichées comme des résultats du compte réel. + +## Préparation et prochaine action + +Paquet reconstruit sous outputs/ml-reliability/ml-reliability-market-opportunities-v2-2026-10-02.zip avec manifeste et SHA256. Les 356 entrées ont des empreintes vérifiées, le code correspond au checkout actuel et aucun chemin de données privées ou .env n’est inclus. La construction Docker et la validation visuelle du module déployé restent à effectuer après autorisation. Le paquet sans date précédent est périmé. Kit d’aperçu : outputs/market-opportunities/market-opportunities-preview-kit.zip. Préserver le guard courant et ses 13 POST existants ; seul le calcul scenario est ajouté. Ordres, entraînements et écritures de configuration restent refusés. Les fichiers privés restent montés en lecture seule sur robot2, hors images et rapports. + +Après accord : appliquer ces deux paquets seulement à l’aperçu ML existant, préserver ses montages et la possibilité de revenir à l’image actuelle, puis vérifier jack/source/CSV, résultats et limites, mobile/desktop, erreurs et refus des écritures. Aucune publication de production, aucun commit/push ni ordre autorisé. + +Fichiers : docs/audit/MARKET_OPPORTUNITIES_AUDIT_2026-10-01.md ; docs/MARKET_OPPORTUNITIES_SYSTEM.md ; services/ml/bourse/{market_snapshot,fund_exposure,market_analysis}.py ; static/components/market-opportunities.js ; api/ml_bourse_endpoints.py. Vérification agrégée : D:/Python/smartfolio/outputs/market-opportunities/verify_robot2.py. Utiliser .venv et node_modules existants du projet principal. Ne pas relancer l’installateur de staging. + +## Autorisation et progression de publication + +L’utilisateur a répondu « ok, go » à la publication de l’aperçu 8083 et aux contrôles. Une première image a été lancée, puis le contrôle navigateur a trouvé l’erreur de contrat safeFetch décrite dans l’audit. Correction locale scan/scénario et tests avec helper réel : 159 tests frontend / 16 suites réussis. Une image corrigée et une nouvelle validation du vrai parcours sont en préparation. Le conteneur initial v13 reste conservé ; production, données privées, clés et environnement n’ont pas changé. Les statistiques de paquet et empreintes sont à prendre dans le dernier manifeste et la preuve de publication, pas dans la première archive du 2 octobre. diff --git a/docs/audit/MARKET_OPPORTUNITIES_UI_2026-10-02.md b/docs/audit/MARKET_OPPORTUNITIES_UI_2026-10-02.md new file mode 100644 index 00000000..a5f2acd2 --- /dev/null +++ b/docs/audit/MARKET_OPPORTUNITIES_UI_2026-10-02.md @@ -0,0 +1,27 @@ +# Market Opportunities — correction de présentation, 2026-10-02 + +État : correction publiée sur l’aperçu existant 8083 et contrôlée sur le compte sélectionné. Ce document complète MARKET_OPPORTUNITIES_DEPLOYMENT_2026-10-02.md et actualise uniquement la version de présentation. Aucun changement de production, ordre financier, commit ou push. + +## Cause et correction + +Le renderer créait déjà des tables HTML mais sans styles de cellules, séparateurs, en-têtes ni alignement des nombres. Les nombreux paragraphes et motifs répétés rendaient le résultat difficile à parcourir. Les anciens contrôles de largeur ne suffisaient pas à valider la qualité visuelle. + +Les quatre fichiers modifiés sont static/bourse-recommendations.html, static/components/market-opportunities.js, static/css/bourse-recommendations.css et static/tests/market-opportunities.test.js. Ajout de tables structurées avec en-têtes, alternance de lignes, alignement numérique, badges et liens de preuve ; cartes de chiffres clés ; panneaux dépliables de provenance, méthode, instruments et revues de positions. Les limites essentielles restent visibles. Styles limités à #opportunities et utilisant les variables du thème existant. Défilement horizontal interne et région accessible au clavier pour les tables étroites. Tous les libellés visibles restent anglais. + +## Validation et limites + +160 tests frontend réussis dans 16 suites ; 9 tests ciblés réussis après la dernière simplification du renderer. Le backend et les autres fichiers du chantier ML sont identiques au paquet r3 : comparaison des paquets, quatre fichiers de présentation/test changés, aucune addition ni suppression. + +Contrôles navigateur réels à 1440 et 390 pixels : source et CSV sélectionnés concordants, 11 lignes de secteurs, 11 candidats et 30 revues présents ; absence de débordement de page et d’erreur JavaScript ; bordures, padding et alignement numérique appliqués ; ouvertures/fermetures des panneaux fonctionnelles. Le changement d’horizon invalide les résultats. Contrôles JWT, refus d’un autre CSV, export agrégé et refus de scénario avec valeurs manquantes conservés. Captures clair/sombre examinées ; contenus privés remplacés sur robot2 avant toute capture ou export d’image. + +Les deux valeurs de positions manquantes continuent de bloquer les bornes globales et la simulation. La couverture reste celle du sous-ensemble valorisé. Aucun résultat ou score financier ajouté pour habiller l’écran. + +## Publication et reprise + +Image courante : smartfolio-ml-preview:market-opportunities-v2-20261002-ui, sha256:61d9334cf9cf2fbce98957d8e8d03e01927d306bfb1c77f51a3a3edce1538392 ; démarrage 2026-10-02T10:37:46.403192347Z. Paquet sha256:420470a65a98256349c4e4a67457436f79747abdc2b41f878f178331a2d931ad. 113 fichiers publics servis concordent avec le paquet (bannière d’aperçu prise en compte), aucun écart. Les deux /healthz répondent 200, zéro ERROR/traceback depuis le démarrage lors du contrôle final. + +Production : image et date de démarrage inchangées. Environnement et montages de l’aperçu préservés, données privées toujours en lecture seule. Retour arrière intermédiaire conservé : smartfolio-ml-preview-api-before-ui-layout-20261002 ; le retour arrière v13 reste conservé séparément. + +Preuves publiques/agrégées dans D:/Python/smartfolio/outputs/market-opportunities : ui-browser-checks-20261002.json, ui-runtime-20261002.json, ui-package-diff-20261002.json et captures *-redacted.png. Tests ciblés dans le checkout isolé : outputs/market-opportunities/ui-tests-20261002.log. Scripts de publication et validation : deploy_preview_20261002_ui.py et browser_validate_ui_20261002.py, copies sur robot2 dans /home/jack/smartfolio-ml-preview-20260930/. + +Prochaine action éventuelle : retour utilisateur sur la présentation. Aucun nouveau contrôle backend requis sans changement de calcul ou de contrat. Production, commit et push nécessitent toujours une autorisation distincte. diff --git a/docs/audit/ML_RELIABILITY_DELIVERY_2026-09-30.md b/docs/audit/ML_RELIABILITY_DELIVERY_2026-09-30.md new file mode 100644 index 00000000..70de39b2 --- /dev/null +++ b/docs/audit/ML_RELIABILITY_DELIVERY_2026-09-30.md @@ -0,0 +1,228 @@ +# Remise à niveau ML SmartFolio — livraison du 30 septembre 2026 + +## Résultat + +Implémentation isolée sur la base réellement observée en production : +806338b522e89647c3f49260e0f418dbea1da761. Prévisualisation initiale : localhost:8082; aperçu connecté ensuite sur robot2:8083, +avec accord explicite pour un montage en lecture seule du compte jack. +Aucune publication, intégration aux allocations, nouvelle recherche directionnelle, +ni automatisation financière n'a été exécutée. + +Sur 84 évaluations actif/horizon, 58 produisent une estimation +de volatilité validée rétrospectivement et 26 restent non évaluables. +Méthodes retenues : {'ridge': 12, 'ewma': 44, 'lstm': 2}. 12 intervalles à 90 % ont passé la couverture +de confirmation; les autres restent absents. Ces résultats ne prédisent pas un prix, +une hausse ou un rendement financier. Les raisons d'indisponibilité sont visibles. + +## Lot 1 — référence, compte et écrans + +### Observations de production, par lectures seules + +- Image exécutée : sha256:0c724b8b8ff87d73885bf0e109276c7b1da2b211f3c9c497b33f9bcda544f2aa, + démarrée le 28 septembre à 09:01:59 UTC; empreinte HTML AI Dashboard : + ae03117de525b153309e8818b7eae8f2f020ebb90b5d7eb49414a013fff29932. + Le HEAD du répertoire seul n'est pas une preuve de l'image; les deux ont été relevés. +- Baseline du 30 septembre à 11:45 UTC : 39 fichiers de modèles, 11 artefacts de + volatilité présents et aucun modèle de volatilité chargé. Le monitoring affichait + quatre entrées saines avec confiance 0,5 malgré aucune inférence enregistrée. + Le HTML comportait 82,3 % et les prédictions étaient absentes. +- Contrôle authentifié à 13:15 UTC : compte jack, CoinTracking API, 191 lignes après filtrage du seuil USD; + source Saxo CSV, 30 positions, import du 22 septembre. L'identité JWT et X-User + ont été vérifiées dans la session utilisée pour ces lectures. +- La première démonstration locale utilisait demo et ne validait pas le périmètre + personnel. Elle a été remplacée par le compte jack et la copie minimale expressément + autorisée : symboles, ordre, sources et dates uniquement. Aucun montant, quantité, + secret ou fichier de portefeuille n'a été copié. + +### Corrections et preuves + +Inventaire : [capacités et consommateurs](ML_RELIABILITY_INVENTORY_2026-09-30.md). +AI Dashboard conserve ses onglets, démarre sur les actifs du compte, expose 25/50/250 +actifs et le nombre omis; les benchmarks sont explicites. Les états inconnus, +performances, dates et confiances fictives ont été retirés. Les chevauchements +d'icônes et le débordement mobile des tooltips ont été corrigés. + +Les pages Analytics, Market Regimes, Cycle Analysis, Dashboard et bourse distinguent +diagnostics, probabilités d'état, corrélations historiques et indicateur externe. +Les textes ajoutés à l'interface sont en anglais. Les autres valeurs financières +du compte ne sont pas répliquées dans le preview minimal. + +## Lot 2 — contrat et fonctionnement + +Le service partagé fournit valeurs nulles, raisons, dates, unités, fournisseur, +dataset, version du code, empreinte et période d'entraînement. Les adaptateurs +legacy harmonisent predictions/prediction/volatility_forecast. Un succès HTTP +n'affirme plus que toutes les prédictions sont disponibles. + +Le snapshot /api/ml/overview ne dépend pas d'un ensemble de gouvernance. +Utilisateur/source/fichier sélectionné sont vérifiés; aucun fichier plus récent, +autre source, token enveloppé ou ETF homonyme ne remplace silencieusement une donnée. +Les réponses retardées sont invalidées après changement de périmètre. + +Les fichiers présents, chargements et inférences réussies ont des comptes distincts, +liés à l'empreinte courante. Chargement sécurisé et compatibilité des features : +échec explicite, aucune substitution fictive. GET et ouverture de page n'entraînent +rien. Entraînement réservé à une action administrateur; publication atomique et +scellée après validation des fichiers, métadonnées et reproduction de l'inférence. +Les nouveaux artefacts portent governance_eligible=False. + +Les lectures coûteuses du service s'exécutent hors de la boucle asynchrone. +Dans le preview, Redis absent provoquait environ 22 secondes d'attente lors du +contrôle de session : Redis a été désactivé uniquement localement, JWT maintenu. +Ce réglage local ne constitue pas une modification de la production. + +## Lot 3 — diagnostics et prévisions de risque + +[Protocole gelé](ML_RELIABILITY_PROTOCOL_2026-09-30.md) : +clôtures quotidiennes vérifiées, cibles 7/30 jours calendaires, annualisation +365/252, première séance cible prévue en bourse, transformations entraînement seul, +purge par fin de cible, 730 jours minimum, calibration/test de 183 jours, +avance de 183 jours et 365 jours réservés à la confirmation. + +Comparaison bornée : persistence, EWMA 0,94, Ridge alpha=1 et LSTM corrigé. +Un candidat appris doit gagner au moins 5 % de QLIKE, deux tiers des fenêtres, +respecter MAE et conserver son avantage en confirmation. Sinon la référence simple +est retenue. Les modèles directionnels, funding et hurdle rejetés ne sont pas relancés. + +Les états HMM sont A–D sans mapping économique vérifié; leurs probabilités ne +prédisent pas une hausse. Les règles économiques et cycles sont descriptifs. +Les reconstructions historiques sont rétrospectives. Les corrélations historiques +restent utilisables; les prévisions Transformer restent expérimentales. + +### Estimations retenues + +QLIKE ci-dessous est celui de la confirmation (plus faible = meilleur). +Chaque rapport JSON conserve les scores des quatre candidats par fenêtre, +la purge, les périodes, le dataset et l'empreinte de l'artefact. + +| Marché | Actif | 7 jours : méthode / QLIKE | 30 jours : méthode / QLIKE | Intervalles 90 % publiés | +|---|---|---|---|---| +| crypto | ADA | ewma / 0.5645 | ridge / 0.1245 | 7j | +| crypto | BCH | ridge / 0.6249 | ridge / 0.3022 | Aucun | +| crypto | BNB | ewma / 0.6795 | ewma / 0.6242 | Aucun | +| crypto | BTC | ridge / 0.4321 | ewma / 0.4740 | 7j | +| crypto | DOT | ewma / 0.8167 | ewma / 0.3288 | Aucun | +| crypto | ETH | ewma / 0.6410 | ewma / 0.4138 | 7j, 30j | +| crypto | LINK | ewma / 0.4323 | ewma / 0.2290 | 7j | +| crypto | LTC | ewma / 0.6892 | ewma / 0.3513 | Aucun | +| crypto | SOL | ewma / 0.4112 | ewma / 0.3440 | 7j, 30j | +| crypto | XRP | ewma / 0.7687 | ewma / 0.5705 | 7j | +| stocks | AAPL | ewma / 0.5276 | ridge / 0.2142 | Aucun | +| stocks | AGGS.SW | Unavailable | Unavailable | Aucun | +| stocks | AMD | ewma / 0.8094 | ridge / 0.2215 | 7j | +| stocks | BRK-B | ewma / 0.5367 | ridge / 0.1348 | Aucun | +| stocks | CHSPI.SW | Unavailable | Unavailable | Aucun | +| stocks | CRSP | ewma / 0.5537 | ridge / 0.0842 | Aucun | +| stocks | CSPX.AS | Unavailable | Unavailable | Aucun | +| stocks | FLXI.DE | Unavailable | Unavailable | Aucun | +| stocks | GLEN.L | Unavailable | Unavailable | Aucun | +| stocks | GOOGL | ewma / 0.4406 | ridge / 0.1820 | 7j | +| stocks | IWDA.AS | Unavailable | Unavailable | Aucun | +| stocks | KO | ewma / 0.5064 | ridge / 0.1736 | Aucun | +| stocks | META | ewma / 0.8191 | ewma / 0.4366 | Aucun | +| stocks | MSFT | ewma / 0.7720 | ridge / 0.4592 | Aucun | +| stocks | NVDA | ewma / 0.5035 | ewma / 0.0990 | Aucun | +| stocks | PLTR | lstm / 0.6522 | lstm / 0.1658 | 7j, 30j | +| stocks | QQQ | ewma / 0.5211 | ewma / 0.2434 | Aucun | +| stocks | SLHN.SW | Unavailable | Unavailable | Aucun | +| stocks | SMH.L | Unavailable | Unavailable | Aucun | +| stocks | SPY | ewma / 0.5332 | ewma / 0.2113 | Aucun | +| stocks | SSLN.L | Unavailable | Unavailable | Aucun | +| stocks | TSLA | ewma / 0.4004 | ewma / 0.1105 | Aucun | +| stocks | UBSG.SW | Unavailable | Unavailable | Aucun | +| stocks | UETW.DE | Unavailable | Unavailable | Aucun | +| stocks | VGK | ewma / 0.3920 | ewma / 0.2089 | Aucun | +| stocks | VWO | ewma / 0.5163 | ewma / 0.2516 | Aucun | +| stocks | WFRD | ewma / 0.3540 | ridge / 0.0399 | Aucun | +| stocks | WRDUSW_CHF.SW | Unavailable | Unavailable | Aucun | +| stocks | XGDU.MI | Unavailable | Unavailable | Aucun | +| stocks | XLI | ewma / 0.4354 | ewma / 0.1004 | Aucun | +| stocks | XLP | ewma / 0.2991 | ewma / 0.0889 | Aucun | +| stocks | XLU | ewma / 0.4510 | ewma / 0.0541 | Aucun | + +### Conclusions et limites + +Les deux LSTM retenus concernent PLTR, à 7 et 30 jours. Ce résultat spécifique +ne justifie pas d'étendre le réseau aux autres actifs ni une prévision de prix. + +BTC à 7 jours retient Ridge : gain QLIKE de développement 8,39 %, 8 fenêtres +gagnantes sur 10 et gain de confirmation 13,66 %. BTC à 30 jours conserve EWMA : +le contrôle MAE empêche de retenir Ridge malgré son gain QLIKE. L'intervalle BTC +30 jours est omis (couverture 74,63 %). SPY/QQQ conservent EWMA; leurs intervalles +couvrent 100 % de la confirmation et sont omis car trop larges. + +Les historiques boursiers refusés contiennent des clôtures invalides ou ne +satisfont pas les contrôles : ils ne sont ni remplis ni remplacés par un proxy. +Les autres symboles du compte sans acquisition/artefact vérifié restent indisponibles, +y compris les tokens enveloppés et les symboles à identité incertaine. + +Cette confirmation est rétrospective; ses fenêtres contiennent des cibles qui se +chevauchent. Aucune significativité statistique ni rentabilité n'est revendiquée. +Plusieurs exécutions ont réparé purge/calendriers, rafraîchi les données, +vérifié les métadonnées, les empreintes et l'encodage. Aucun seuil ni hyperparamètre +n'a été modifié pour optimiser la confirmation après ces répétitions. + +Les actions utilisent l'historique actuellement ajusté par le fournisseur pour +splits/dividendes, sans archive des révisions disponible à chaque date. Une preuve +historique de décision en temps réel n'est donc pas établie. Les artefacts appris +conservent leur période d'entraînement antérieure à la confirmation; aucun refit +sur cette confirmation n'a été publié. + +## Lot 4 — contrôles et livraison + +- Suite globale : 3 404 tests Python réussis, 27 ignorés, couverture 51,09 %. + La correction finale du proxy fournisseur et le test d'empreinte sont inclus + dans cette suite globale; 31 tests ciblés ont également réussi. 119 tests JavaScript, 11 suites. +- Ruff et mypy : réussis; OpenAPI anglais : 457 chemins. + Bandit : zéro problème de sévérité moyenne/haute; pip-audit : aucune vulnérabilité connue. +- Contrats : sorties complètes/partielles/absentes/périmées/zéro, artefact incompatible, + altération après chargement, chargement/API/session en échec. +- JWT réel, X-User discordant, session expirée et rôle administrateur sont testés. + Deux utilisateurs, deux sources et deux CSV ont des contrôles d'isolation. +- Absence d'entraînement en lecture même avec force_retrain; normalisation, + purge, mutation du futur, LSTM causal et reproduction des artefacts vérifiées. +- Les tests globaux ont nécessité l'autorisation des hôtes test/testserver. + Les compteurs de limitation sont remis à zéro entre tests; la limitation reste + active à l'intérieur d'un test. La production n'est pas assouplie. +- Les modifications étrangères du checkout original ont été préservées. + Le report sur la base production a conservé les corrections de devises, source, + retry et scoring. Les différences de fins de ligne héritées sont exclues du paquet. + + +### Vérification visuelle et dernières corrections + +AI Dashboard, Stock Analytics, Market Regimes, Cycle Analysis, Unified Analytics +et Monitoring ont été contrôlés dans un seul onglet Chrome sous `jack`, à 390 et +1440 pixels. Aucun débordement horizontal de page ne subsiste; les tableaux peuvent +défiler à l'intérieur de leur carte. Les quatre onglets AI sont conservés. + +L'aperçu ML personnel utilise uniquement la capture privée consentie des symboles, +ordre, sources et dates, acquise le 30 septembre à 13:15 UTC. Ce n'est pas une connexion +au portefeuille en direct. Aucun montant, quantité, profil complet ou clé fournisseur +n'a été copié. Stock Analytics a donc été vérifié dans son état explicite « source +non configurée », sans prétendre reproduire les données financières complètes. + +Une erreur d'authentification CoinGecko était propagée en 401 et provoquait à tort +une déconnexion SmartFolio. Le proxy traduit désormais les refus fournisseur 401/403 +en 502; la session SmartFolio continue à être contrôlée normalement. Trois cas de +régression couvrent 401, 403 et 503. La session locale `jack` reste authentifiée. + +Les critères boursiers du tableau Regimes correspondent aux règles sur 200 clôtures, +avec leur méthode descriptive. Aucun « Unavailable% » ni zéro d'export de substitution. +Sur Cycles, les performances alternatives codées en dur et les esquisses de prix sans +reçu fournisseur sont retirées. Leur état est Unavailable avec raison. L'indice d'ajustement +à trois événements historiques curated est affiché sur 100 comme heuristique rétrospective; +il ne devient ni une accuracy prévisionnelle ni une précision publiée aux consommateurs. +L'action manuelle d'analyse historique et l'action Alternatives ont été vérifiées. + +Les preuves se trouvent dans outputs/ml-reliability : rapports par actif/horizon, +reçus SHA-256, logs des contrôles et checks-summary.json. +Le paquet portable possède son manifeste SHA-256 et exclut entièrement les données +utilisateur et la capture privée. [Procédure réversible](ML_RELIABILITY_RELEASE_2026-09-30.md). + +La vérification Linux/Python 3.11 et le contrôle authentifié de l'image isolée ont +été exécutés sur robot2. Le [rapport de l'aperçu](ML_RELIABILITY_ROBOT2_PREVIEW_2026-09-30.md) +détaille les corrections de dépendances et de source révélées lors du test réel. +La validation humaine et les contrôles de publication restent nécessaires. Aucune publication en production +n'est autorisée par cette livraison. Le journal de consultations est disponible mais +désactivé; aucun entraînement récurrent ni automatisme financier n'est ajouté. diff --git a/docs/audit/ML_RELIABILITY_INVENTORY_2026-09-30.md b/docs/audit/ML_RELIABILITY_INVENTORY_2026-09-30.md new file mode 100644 index 00000000..94764609 --- /dev/null +++ b/docs/audit/ML_RELIABILITY_INVENTORY_2026-09-30.md @@ -0,0 +1,49 @@ +# Inventaire ML — 30 septembre 2026 + +## Périmètre et sources de vérité + +Le service partagé services/ml/reliability.py calcule les résultats de lecture. +services/ml/risk_evaluation.py utilise les mêmes features, horizons et estimateurs +pour la comparaison chronologique. api/schemas/ml_contract.py décrit les valeurs, +les états, la validation et la provenance. Aucun GET ne lance un entraînement. + +| Capacité | Calcul et fournisseur | Artefact / entraînement | API et consommateurs | Conclusion | +|---|---|---|---|---| +| Volatilité crypto | Clôtures quotidiennes Binance Spot USDT, rendements logarithmiques, annualisation 365 | JSON scellé dans models/validated_risk; comparaison explicite persistence/EWMA/Ridge/LSTM | /api/ml/overview, /unified/predict, adaptateurs legacy; AI Dashboard, Analytics | Prévisions 7/30 jours seulement après confirmation | +| Volatilité bourse | Yahoo Finance/yfinance, auto_adjust=True, calendriers de place, annualisation 252 | JSON scellé; transformations entraînement seul; calendrier vérifié | /api/ml/overview, /api/ml/bourse/forecast; AI Dashboard, Bourse Analytics | Prévision validée rétrospectivement ou Unavailable | +| Régime économique | Drawdown sur 200 clôtures et distance à MA200; critères explicites | Pas de réseau requis; règles descriptives | Overview, Analytics, Market Regimes, Dashboard, bourse | Diagnostic, aucune probabilité de hausse | +| États HMM crypto | Caractéristiques et moyennes apprises; probabilités des états | Artefact legacy sécurisé, features compatibles; GET sans réentraînement | /api/ml/crypto/regime; Market Regimes, graphiques BTC/ETH | Experimental; A–D sans correspondance économique vérifiée | +| Histoire des régimes | Reconstruction avec modèle ajusté plus tard | Historique rétrospectif explicitement identifié | /api/ml/crypto/regime/history; graphiques régimes | Ne prouve pas une décision possible en temps réel | +| Corrélation historique | Pearson, 90 rendements quotidiens communs; sources vérifiées | Aucun entraînement | Overview et diagnostic bourse | Descriptive; Partial si actifs absents/périmés | +| Transformer corrélation | Modèle legacy de corrélation future | Aucun comparatif indépendant confirmé | Registre et adaptateurs de corrélation | Experimental; prévisions masquées | +| Cycle Bitcoin | Heuristique de phase, cycle et distances; composante de scoring existante | Aucun modèle prédictif validé | Cycle Analysis, Risk Dashboard, Analytics | Diagnostic heuristique; allocation existante préservée | +| Fear & Greed | Observation Alternative.me avec date et fournisseur | Aucun modèle ML | Overview, Analytics, AI Dashboard | Indicateur externe; absent si fournisseur indisponible | +| Sentiment ML par actif | Aucun adaptateur indépendant validé connecté | Legacy non validé | Endpoint legacy sentiment, registre | Unavailable; aucune valeur neutre de substitution | +| Signaux techniques bourse | Accord de règles RSI/MACD/Bollinger, observations requises | Pas de probabilité calibrée | /api/ml/bourse/signals, Bourse Recommendations | Descriptive; accord de règles ≠ probabilité | +| Direction crypto / funding / hurdle | Recherche causale existante du 13 septembre | Expériences closes; aucune relance | config/ml_capability_registry.json et recherche canonique | Rejected; aucune intégration aux allocations | +| Alertes ML | Prédicteur désactivé | Réactivation hors chantier | Alerts, AI Dashboard, monitoring | Rejected / disabled | +| Modèles legacy | Fichiers .pth/.keras/.pkl, métadonnées et schémas vérifiés | Présence, chargement et inférence distingués; chargement sécurisé | /api/ml/status, monitoring, registry | Experimental tant que la confirmation manque | +| Santé et performances | Comptes observés et métriques des artefacts validés | Santé, confiance et performances inconnues restent nulles | /monitoring/health, /metrics/{model}; Monitoring, Model Status | Aucune santé inventée à partir des fichiers | +| Gouvernance / allocation | Chaîne de gouvernance existante | governance_eligible=False pour les nouveaux artefacts | Allocation / execution existantes | Aucune nouvelle intégration ni automatisme financier | + +## Contrat et identité + +Chaque résultat indique actif, marché, diagnostic/forecast, cible, horizon, unité, +valeur éventuellement nulle, état, raison, dates et validation. Provenance : +fournisseur, dataset, code, empreinte d'artefact et période d'entraînement. +Les diagnostics sans apprentissage n'ont pas de période d'entraînement. +Les probabilités non évaluées sont absentes. Seuls les intervalles avec couverture +de confirmation entre 85 % et 95 % sont publiés. + +Les requêtes personnelles portent l'utilisateur authentifié, la source et le fichier +CSV sélectionné. Le service refuse un changement silencieux de source ou de fichier. +Les mappings Saxo conservent la place; un ETF Londres n'est pas remplacé par son +homonyme américain. Les tokens enveloppés et symboles suffixés ne sont pas assimilés +à BTC, ETH ou SOL. Aucun cache global de résultat personnel n'est ajouté. + +Le dashboard ouvre les actifs du compte; les benchmarks sont un mode explicite. +Limites d'affichage : 25, 50 ou jusqu'à 250 actifs, avec le nombre d'actifs omis. +Les événements de source/fichier invalident l'identité de la requête en cours. +La copie minimale autorisée sert uniquement au preview et est refusée en production. + +Les anciennes alternatives de cycle à performances fixes et les comparaisons de prix illustratives sans reçu fournisseur sont indisponibles. Seul l'ajustement heuristique rétrospectif à des événements curated reste affiché, explicitement sans preuve de prévision. diff --git a/docs/audit/ML_RELIABILITY_PROTOCOL_2026-09-30.md b/docs/audit/ML_RELIABILITY_PROTOCOL_2026-09-30.md new file mode 100644 index 00000000..abfbf424 --- /dev/null +++ b/docs/audit/ML_RELIABILITY_PROTOCOL_2026-09-30.md @@ -0,0 +1,68 @@ +# ML reliability — protocol frozen before evaluation + +Decision support for manual use only. No allocation integration, recurring training, +directional research, production publication or order execution is authorized here. + +## Data and targets + +Daily closes with documented provider, dataset identity and adjustment policy. +Crypto: 7/30 calendar days, annualization 365. Stocks: first scheduled session +on/after the calendar target, annualization 252; adjusted close policy required. +Target variance uses returns strictly after decision close through target close. +Missing observations are never backfilled. Legacy 1h/4h/1d targets are unavailable. + +## Frozen comparison + +Persistence (past realized volatility), EWMA lambda 0.94, Ridge alpha 1; +corrected LSTM is an additional candidate only with compatible causal features. +At least 730 training days, 183 calibration days, 183 test days; advance 183 days; +reserve final 365 days for confirmation. Purge using target end dates. +Transforms fit only on training. Select on mean QLIKE, check MAE and folds. +Learned candidate must improve QLIKE >=5%, win >=2/3 folds, and maintain +that improvement on untouched confirmation. Otherwise retain the simple reference. +90% intervals require separate calibration and confirmation coverage 85–95%. +No verified confirmation means no published forecast or probability. + +## Existing research decisions + +Reuse crypto-forecast-lot2 causal research and dated data, preserving its identities. +Directional models and ML alert predictor remain rejected/disabled. +Transformer correlation forecasts remain experimental. Historical correlations, +economic rules and cycle diagnostics are descriptive, not forecasting evidence. +HMM probabilities refer to latent states, not future price direction; reconstructed +histories are retrospective unless independently proven causal. + +## Delivery gates + +Contracts: complete, partial, absent, stale, zero, incompatible artifact, load error, +API error and expired session. Tenant/source isolation, no training during reads, +future mutation, purge, train-only normalization and reproducibility. +Desktop/mobile checks and required project tests. Reports must separate observed +facts, validation conclusions and unavailable capabilities. Deployment requires +separate approval, authenticated checks and rollback preparation. + +## Fixed implementation parameters and interpretation + +Causal features: RV7, RV30, RV90, EWMA variance (lambda 0.94), absolute log return. +All realized-volatility calculations use population standard deviation (ddof=0). +LSTM: sequence 14, one layer with hidden width 8, Softplus positive output, +12 full-batch epochs, Adam learning rate 0.01, seed 1729, deterministic CPU. +Ridge: alpha 1 with training-only mean/std; negative predictions clipped at zero. +QLIKE uses variance ratio y²/p² - log(y²/p²) - 1, numerical floor 1e-10. +MAE is measured in annualized volatility fractions. + +The first calibration boundary includes 10 calendar days of slack to satisfy +730 observed training days after target-end purge even on exchange holidays. +Every boundary is purged independently by target end, including confirmation. +Expanding training advances 183 days. There is no hyperparameter search. +Calibration residual absolute errors define one symmetric 90% interval width; +lower bounds are clipped at zero. Confirmation, rather than calibration alone, +decides whether the interval may be published. + +Retrospective confirmation is an out-of-development comparison within this run, +not evidence of genuinely unseen future performance. Runs repeated to repair +calendar boundaries, refresh provider data and verify metadata are recorded in the +delivery report. No method or hyperparameter was chosen after those repeats to +optimize confirmation results. Targets overlap; no significance claim is made. +Stock inputs use the vendor's current split/dividend adjusted history, not a +point-in-time corporate-action archive. Exchange calendars determine target sessions. diff --git a/docs/audit/ML_RELIABILITY_RELEASE_2026-09-30.md b/docs/audit/ML_RELIABILITY_RELEASE_2026-09-30.md new file mode 100644 index 00000000..8dd35ae0 --- /dev/null +++ b/docs/audit/ML_RELIABILITY_RELEASE_2026-09-30.md @@ -0,0 +1,77 @@ +# Livraison et retour arrière ML — 30 septembre 2026 + +## État + +Implémentation préparée dans un checkout isolé issu de la version production +806338b522e89647c3f49260e0f418dbea1da761. Après autorisation, un aperçu Docker +séparé a été construit sur robot2:8083. Le compte jack et le registre de connexion +sont montés en lecture seule; aucune donnée utilisateur ne figure dans l'image. +Aucun commit, push, fusion ou déploiement de production n'a été exécuté. +Voir le [rapport de l'aperçu robot2](ML_RELIABILITY_ROBOT2_PREVIEW_2026-09-30.md). + +Le paquet produit par scripts/package_ml_reliability.py contient les seuls fichiers +modifiés sémantiquement, les nouveaux fichiers autorisés, les historiques publics +vérifiés, les artefacts JSON et les rapports d'évaluation. Un manifeste SHA-256 +contrôle chaque entrée. Aucun portefeuille, montant, quantité, clé, session, profil +utilisateur, fichier .env ou capture privée n'est inclus. + +## Conditions avant publication, après autorisation distincte + +1. Relire le manifeste et vérifier toutes ses empreintes. Confirmer que la production + exécute toujours la version/image de référence; une évolution impose un nouveau + report des corrections et les contrôles correspondants. +2. Intégrer les fichiers sur une branche de livraison propre basée sur la version + production appropriée. Les différences de fins de ligne existantes ne doivent + pas provoquer la réécriture des fichiers étrangers au chantier. +3. Construire et vérifier une image Linux/Python 3.11 isolée avec les dépendances + du requirements.txt. Le contrôle Windows/Python 3.13 ne remplace pas cette étape. + Exécuter les contrôles de contrat, sécurité, chargement JSON et calendriers. +4. Les données publiques data/ml_verified sont exclues du Dockerfile par + .dockerignore; les installer explicitement dans le volume de données, puis + contrôler les reçus et leurs empreintes. Les modèles JSON doivent être présents + dans l'image ou dans un volume de modèles explicitement monté. +5. Enregistrer l'image précédente et la configuration de lancement sans exposer + les secrets; conserver les anciens fichiers publics remplacés. Sauvegarder + séparément le volume utilisateur existant, sans l'incorporer au paquet. +6. Exiger ML_AUTO_TRAIN=0, conserver RUN_SCHEDULER selon le fonctionnement existant, + ne pas activer de scheduler ML ni modifier la gouvernance. Supprimer la variable + ML_PORTFOLIO_SNAPSHOT. Maintenir JWT, cohérence X-User et rôle administrateur. +7. Présenter l'image isolée sur un port distinct et vérifier la session jack, + CoinTracking API, le CSV Saxo sélectionné, les dates et les actifs réellement + disponibles. Vérifier également une session expirée et une seconde identité. +8. Publier seulement après ces contrôles et l'autorisation de production. + L'ancien deploy.sh --force ne convient pas à une livraison qui doit préserver + des changements locaux. + +## Retour arrière + +Image de référence observée le 30 septembre : +sha256:0c724b8b8ff87d73885bf0e109276c7b1da2b211f3c9c497b33f9bcda544f2aa. +Conteneur : smartfolio-api. Démarrage observé : 2026-09-28T09:01:59.068663028Z. +Volumes observés : données et logs; aucun volume de modèles n'était monté. + +En cas d'échec de session, de chargement ou de contrat, relancer l'image précédente +avec les mêmes paramètres et volumes. Restaurer les fichiers publics remplacés +depuis leur sauvegarde. Ne pas réinitialiser les profils ou portefeuilles. +Contrôler healthz, la session authentifiée, les sources, puis AI Dashboard et bourse. +Les nouveaux artefacts peuvent rester archivés hors du chemin actif, jamais être +déclarés validés par un ancien chargeur. + +## Observation après publication éventuelle + +ML_INFERENCE_JOURNAL=1 active, après publication autorisée, un journal SQLite +par utilisateur. Il enregistre les consultations manuelles de prévisions +disponibles, source, dates cibles, versions et provenance, sans quantités/montants. +Ce journal est désactivé dans le preview et par défaut. Il ne réentraîne rien et +n'exécute aucun ordre. La comparaison avec les réalisations est une analyse +ultérieure, pas un entraînement récurrent. + +## Preview privé + +La copie locale autorisée contient uniquement les symboles, l'ordre, les sources +et les dates. Elle est datée du contrôle authentifié; ce n'est pas une connexion +locale aux clés de production. Les données financières des autres pages ne sont pas répliquées localement. +L'aperçu robot2 utilise ensuite, après accord explicite distinct, un montage en +lecture seule du dossier jack existant sur robot2. Ses caches, Redis, clé JWT, +logs et port sont indépendants. Les cookies de production ne sont pas modifiés. Les sources de marché et les modèles utilisés pour l'inférence +restent les données publiques vérifiées. diff --git a/docs/audit/ML_RELIABILITY_REPORTED_REGRESSIONS_2026-09-30.md b/docs/audit/ML_RELIABILITY_REPORTED_REGRESSIONS_2026-09-30.md new file mode 100644 index 00000000..25115b54 --- /dev/null +++ b/docs/audit/ML_RELIABILITY_REPORTED_REGRESSIONS_2026-09-30.md @@ -0,0 +1,202 @@ +# Régressions signalées lors du contrôle humain de l’aperçu + +## Constat et causes vérifiées + +Le contrôle précédent des pages par défaut était insuffisant pour valider les onglets et actions. Le retour utilisateur invalide la conclusion de disponibilité fonctionnelle générale. + +- Journaux robot2 : POST Rebalance, DI Backtest et Optimization rejetés 403 par la protection de l’aperçu. Ces routes calculent des propositions/simulations ; elles ne passent pas d’ordres. Elles sont désormais autorisées exactement pour POST, pas PUT/DELETE. +- Taxonomie absente de l’aperçu : 102 alias de base. Au contrôle direct authentifié de production, `/taxonomy` renvoie 224 alias (le navigateur utilisateur indiquait 249, écart encore à expliquer). Montage du fichier de référence production en lecture seule dans l’aperçu. +- Risk Score pour la même source cointracking_api et fenêtres 365/90 : 71.5541 production, 69.6327 aperçu avant restauration. Attribution causale complète à vérifier après restauration, sans ajuster les scores pour les faire coïncider. +- Alertes 503 : moteur volontairement non initialisé. Restauration d’un stockage d’alertes isolé en standby, sans démarrer le scheduler ou la gouvernance. L’absence d’alertes dans cet espace n’est pas un historique de production. +- PUT implicites de source bourse pendant les lectures : bloqués par le montage protégé. Restauration de contexte sans persistance implicite ; changements de compte sur disque restent interdits. +- Optimization : ancien chargement CSV global sans user_id. Remplacement par get_unified_filtered_balances avec identité authentifiée et source exacte. L’ancien endpoint d’optimization backtest produisait chiffres fixes/aléatoires : réponse 501 explicite désormais. +- Cycle : sections retirées trop largement. Position descriptive et comparaisons/anatomie rétablies à partir des clôtures Bitcoin avec reçu vérifié. Seuls les cycles disposant de la clôture du halving sont normalisés ; pics rétrospectifs, cycle actuel incomplet, aucune date future prédite. +- Scans : lectures séquentielles et retries longs. Lectures publiques bornées à quatre simultanées ; attente navigateur limitée, sans répétition automatique de calcul coûteux. +- Stock Analytics : probabilités absentes indiquées explicitement ; délai ML adapté. Une sortie absente n’est pas remplacée par une prédiction artificielle. + +## Tests exécutés avant reconstruction + +- 93 tests ML, régressions et optimisation : succès. +- 34 tests contrats/recommandations/auth bourse : succès ; ALLOWED_HOSTS de test configuré. +- 12 tests protection aperçu : succès, dont calcul autorisé et autres méthodes interdites. +- 15 tests frontend ML/source : succès, configuration Jest ESM existante. +- Ruff : succès avant assemblage final. + +## Contrôle fonctionnel restant + +L’aperçu corrigé doit être contrôlé sur les parcours et boutons signalés, desktop et mobile. Les réponses HTTP seules ne constituent pas une preuve de rendu. Rapport de contrôle réel et différences de scores à ajouter après les essais. Aucun déploiement de production autorisé. + +## Contrôle intermédiaire v6 + +- Après restauration de la taxonomie : 224 alias renvoyés par les deux APIs. Risk Score aperçu 71.5410 contre 71.5541 production, écart résiduel 0.0130 à caractériser. +- DI Backtest : deux POST réussis et 441 points dans le graphique equity, compte jack. +- Optimization : POST réussi, source cointracking_api, 36 poids tracés / 37 lignes de table. +- Cycle : position observée, trois lignes d’anatomie, 1440 points dans les graphiques comparatifs. +- Nouveaux contrôles exposent une session fraîche sans source bourse locale malgré une configuration serveur existante : restauration par GET de la source configurée, sans PUT. +- Rebalance consomme une liste de stratégies comme un dictionnaire, donnant des identifiants numériques. Normalisation par strategy.id et rendu immédiat des modèles statiques avant les calculs dynamiques. +- Alias Manager ajoutait les alias inconnus locaux aux entrées de la taxonomie en leur attribuant Others : compteur des alias enregistrés séparé des propositions non classifiées ; erreur API affichée sans liste fictive. +- Les captures privées restent uniquement dans cache/ml-preview sur robot2. + + +## Contrôle fonctionnel v7 et finitions v8 + +Les conclusions de disponibilité générale de v5 sont remplacées par ces contrôles après le retour utilisateur. + +- 19 parcours authentifiés jack : 13 pages desktop et six mobile, widths 1440/390, source crypto cointracking_api et source bourse restaurée depuis la configuration serveur. Aucun débordement du document, aucune exception JavaScript, aucune région encore en chargement au relevé. Les images privées restent sur robot2 ; contrôle automatique du DOM et des actions, validation visuelle humaine encore requise. +- DI Backtest : 441 points et les deux calculs POST 200. Son fonctionnement est distinct de l'ancien backtest d'optimization, désormais explicitement 501 car les chiffres étaient fabriqués. +- Rebalance : POST 200, 11 éléments de résumé, 88 actions, 25 alias inconnus à revoir. Distribution produite en SVG, pas Chart.js : comptage des vrais segments confirmé au contrôle final. Les modifications de taxonomie restent bloquées dans l'aperçu. +- Optimization : 37 lignes de poids et 36 points tracés, source personnelle cointracking_api, succès du POST. +- Cycle : position disponible, trois lignes d'anatomie, 1440 points comparatifs. Les périodes non couvertes restent absentes et les pics sont rétrospectifs. +- Market Regimes : BTC et ETH ont chacun 286 observations tracées, sans erreur. Une règle économique ne fournit pas de probabilité évaluée : Confidence unavailable est attendu. Les légendes de confiance fictive et de fusion avec HMM ont été corrigées dans v8. +- Stock Analytics : 91 sections ML, disponibilité Available/Partial et raison explicite de l'absence de probabilités de régime. +- Recommendations : 30 lignes, source Saxo CSV personnelle. Les deux scans terminent et répondent 200. Opportunities ne produit aucun candidat ; une exposition industrielle non classifiée, notamment ETF, peut remplir un gap apparent. Une liste vide ne prouve pas un portefeuille équilibré. Raison affichée dans v8, sans invention de candidat. +- AI Dashboard : quatre onglets accessibles desktop/mobile. Le compteur indique les artefacts chargés globalement sur le serveur, pas un nombre de modèles prévisionnels utilisables sur les positions choisies. +- Alias : 224 références enregistrées, communes aux deux APIs. Les 25 propositions du dernier plan local expliquent les 249 de l'ancien navigateur ; elles restent séparées et non classifiées. +- Monitoring : environnement isolated_preview et scheduler intentionnellement désactivé ; pas de fausse dégradation de ce seul fait. Standby des alertes dans le stockage isolé, sans historique production. + +### Comparaison privée et journaux + +Comparaison conservée sur robot2, détails financiers non exportés. GRI et exposition par groupe identiques entre production et aperçu. Risk Score frais : 71.55327 contre 71.54007, différence 0.01321. Les cohortes historiques diffèrent pour EGLD3 (résolu EGLD), HMSTR, JUNO, TUSD et VANRY, dont les fichiers avaient des prix invalides. Les 119 historiques publics acceptés conservent exactement les empreintes de production. Aucun ajustement artificiel du score pour supprimer cet écart. + +Journaux pendant les contrôles v7 : zéro traceback et zéro exception PermissionError/FileNotFoundError/ValueError/TimeoutError/ReadTimeout/HTTPStatusError. Seul statut HTTP en erreur : scheduler/health 503 intentionnel. Les journaux bruts restent sur robot2. + +### Contrôles requis + +- Python global : 3422 succès, 27 skipped, 30 warnings, couverture 51.35 % (seuil requis 30 %), 196.79 s. +- Jest global : 134 succès, 13 suites ; renouvelé après les textes v8. +- Linux dans le conteneur : 46 tests ML/régressions réussis. Le processus de test utilise ENVIRONMENT=staging, sans changer le serveur. Les essais avec production puis une valeur test non reconnue ont échoué sur la configuration de la fixture ; aucun changement de logique pour contourner la protection production. +- Protection aperçu : 12 contrôles ; méthodes d'écriture, entraînement et ordres bloqués, calculs explicitement autorisés. +- Les évaluations de volatilité et leurs 58 artefacts n'ont pas changé : les preuves causales restent valides. Les méthodes et limites sont décrites dans le protocole et le rapport de livraison. + +Production non modifiée, aucun commit, fusion, push ou déploiement de production. Prochaine étape : validation humaine de l'aperçu, puis décision séparée concernant l'intégration et la publication. + + +### Complément au contrôle v8 : collecte externe + +Le contrôle strict final v8 a correctement échoué sur Crypto Toolbox 502 : il ne faut pas ignorer cette erreur pour déclarer toutes les APIs saines. La collecte de production renvoie 200 mais scraping_failed=true, avec 30 indicateurs en cache ; elle ne constitue pas une collecte fraîche réussie. L'aperçu a révélé un transport Playwright fermé. La récupération pouvait conserver un objet navigateur déconnecté et ignorer le redémarrage. Correction v9 : lancement sérialisé, destruction du driver déconnecté avant redémarrage, nettoyage après échec. Trois tests dédiés vérifient récupération, appels concurrents et absence de données inventées à l'échec. La capacité externe reste indisponible si la collecte ou sa validation échoue ; ce n'est pas une prévision ML. Le résultat de la collecte après correction est relevé séparément. + + +## État final v9 + +- Collecte externe : HTTP 200, 30 indicateurs, cached=false, sans scraping_failed, sans copie du cache de production. Le cache servi en production reste signalé scraping_failed=true lors de la comparaison. La fraîcheur observée concerne cet essai, pas une garantie de disponibilité permanente du fournisseur. +- Python global après correction backend : 3425 succès, 27 skipped, 30 warnings, couverture 51.36 % / seuil 30 %, 215.25 s. +- Linux sur l'image finale : 61 tests ML/régressions/protection, succès. Frontend : 134 tests, succès ; Python/browser recovery n'altère pas le JavaScript vérifié en v8. +- Root et données production restent RO ; image de production et date de démarrage inchangées. Aperçu final smartfolio-ml-preview:20260930-v9. Aucun entraînement, ordre, intégration des prévisions aux allocations ou publication de production. +- Les historiques acceptés peuvent évoluer naturellement en production entre deux essais. L'exclusion documentée des prix invalides est maintenue, sans remplissage artificiel ni modification de formule pour aligner le score. +- La vérification humaine de l'ensemble des comportements et de la présentation reste à faire sur 8083. Les images privées et détails de comparaison financière restent sur robot2. + + +Attestation finale stricte, 30 septembre 2026 à 21:12 UTC : 19 parcours complets v7 réutilisés pour les fonctions inchangées et sept parcours ciblés desktop/mobile v9 réussis. Aucune erreur API inattendue, aucun débordement, valeurs de test et sources conformes. La seule réponse 503 attendue est celle du scheduler intentionnellement désactivé. Production : image et démarrage inchangés, healthz 200 ; aperçu v9 healthy, root et montages production RO. Espace libre 36.47 GiB. Les conclusions ne remplacent pas la validation humaine et ne prouvent pas la rentabilité future d'une prévision. +## Complément v10/v11 — second retour utilisateur et Admin (1er octobre 2026) + +Les attestations v9 ne couvraient pas le second rendu du panneau Risk d’Analytics ni l’apparition du menu Admin sur l’origine LAN 8083. Elles ne suffisaient donc pas pour les problèmes signalés ensuite. + +### Corrections + +- Admin : suppression de la visibilité forcée par le port 8080/localhost et des anciennes clés `user_roles`. La navigation attend la vérification de session et utilise `userInfo.id/roles`. L’accès reste limité au rôle `admin` par l’API. Les appels Admin transmettent explicitement les headers d’authentification ; aucun rôle du registre n’est modifié. +- Analytics / Risk Dashboard / orchestrateur : paramètres communs (source personnelle, seuil configuré, historique 365 jours, rendements 90 jours, dual window, v2_active, fichier choisi). Le second renderer Analytics arrondit le score, conserve zéro et n’utilise plus les anciennes clés globales de scores. Le rendu principal lit le score de ses métriques API. Les légendes historiques ne prétendent plus représenter un cycle ou une fenêtre de volatilité de 30 jours. +- Comparaison fraîche sur robot2, mêmes paramètres : production 71.5537813157, aperçu v10 71.5407245705. L’ancien 87 de la capture n’est pas reproduit. Les détails financiers de cette comparaison restent sur robot2. Aucune formule ni donnée n’est modifiée pour forcer une égalité. +- Cycle : quatre lignes conservées, avec couverture Complete/Partial/Ongoing/Unavailable. Les drawdowns du cycle 2 sont accessibles indépendamment de la clôture au halving. Le cycle 1 reste sans série faute de données vérifiées ; la normalisation du cycle 2 reste indisponible faute d’ancre. Les anciens schémas de prix sans provenance ne sont pas réintroduits. La position présente temps observé, phase et indice heuristiques, méthode et limites explicites ; aucune date de pic future ni précision prédictive. +- Stock ML : tableau de 30 actifs pour le compte contrôlé, avec 7/30 jours et régime ; détails repliables pour raisons, séances cibles, méthode, validation et provenance. Plus de 91 grandes cartes répétitives. +- Données stock : conservation des acquisitions brutes et de leurs empreintes ; exclusion documentée de clôtures invalides en fin de série et de dates hors séance. Aucun remplissage de trous intérieurs. AGGS.SW et SLHN.SW redeviennent vérifiables ; quatre évaluations chronologiques retiennent EWMA. IWDA.AS, XGDU.MI et SMH.L restent non évaluables avec leurs séances manquantes. Les transformations/artefacts précédemment validés ne changent pas de code_version. +- Opportunities : classification calculée sur une copie, réutilisée pour le scan et le simulateur ; les positions originales ne sont pas modifiées. Allocation du simulateur cohérente avec celle du scan au lieu de Other 100 %. Onze secteurs sont décrits par allocation connue, cible et plage d’écart. Une exposition non classée peut rendre le gap indéterminé ; elle ne devient pas une recommandation d’achat. Les protections des ventes sont conservées. +- AI Dashboard : compteurs portant sur l’univers choisi : 12/50 prévisions disponibles, 6/25 actifs couverts et 7 diagnostics lors du contrôle crypto. Les artefacts chargés par le serveur restent dans Model Status. Les onglets sont conservés. Aucun agrégat de confiance ou Decision Index inventé. +- CSS v11 : en-tête et simulateur boursiers, contrôle de stress Analytics. Défilement local des tables et retour à la ligne des contrôles mobiles. + +### Preuves déjà obtenues + +- Windows global v10 : 3429 tests Python réussis, 27 skipped, 30 warnings ; couverture 51.48 % (seuil 30 %). +- JavaScript : 138 tests, 14 suites, réussis. +- Linux sur v10 : 61 tests de contrats/causalité/source/régressions réussis. Premier lancement hérité d’ENVIRONMENT=production : rejet correct de la fixture de capture privée ; tentative ENVIRONMENT=test refusée par la configuration ; reprise avec development uniquement pour le processus pytest, serveur inchangé. +- Linux : 62/62 artefacts chargés et inférés, 13 intervalles validés, zéro erreur. Empreinte canonique e4f990e0500052d28d65361280a6198c2d3a7a441c73908549145c4a21143c92 inchangée. +- v10, LAN 8083, session vérifiée jack sans rôles préinjectés : Admin visible sur les 14 passages ; Overview, Users, Cache et ML en consultation, HTTP 200. Cycle, ML Stock, allocation du simulateur et compteurs AI vérifiés desktop et mobile. Dix passages complets réussis. +- Le contrôle initial a également détecté deux débordements mobiles, corrigés en v11, et deux checks Risk prenant par erreur la réponse secondaire non paramétrée pour la réponse principale. Le contrôle final attend la réponse v2_active et le rendu, au lieu d’un délai fixe seul. Les passages concernés sont répétés ; les autres preuves sont réutilisées. +- Contrats HTML concernés après CSS : 23 tests réussis, 1 skipped, 1 warning. +- Le contrôle automatique a refusé une préparation pouvant être interprétée comme une exportation privée. Le contrôleur finalement utilisé ne conserve ni réponses financières ni captures ; seulement des booléens, compteurs, chemins/statuts et le score explicitement comparé. + +### Environnement isolé + +Aperçu final : smartfolio-ml-preview:20260930-v11, image sha256:55820f9f87f0f50ce93d4ab4ff03873da6096795e1531552e2b9f124dad85179. Paquet public 331 fichiers, 62 artefacts, SHA256 f2f7a5400a00328cfaad812574d3a69479672de153b79ccc8f0efca51edc4aeb. Robot2 dispose d’environ 36.17 GiB libres après cette image. Session de test conservée, mêmes secrets de session privés sur robot2. Les fichiers jack, registre et alias de production restent montés en lecture seule. Production : même image 0c724b8… et démarrage 2026-09-28T09:01:59.068663028Z, healthz 200. Aucun commit, push, fusion ou déploiement de production. + +Les mutations Admin, l’entraînement, le scheduler et les opérations financières restent désactivés dans l’aperçu. La validation humaine de l’interface reste requise. L’outil interactif du navigateur demeure indisponible à cause de l’erreur ACL Windows ; les contrôles exécutés utilisent Playwright sur robot2 et ne prouvent pas l’intégralité du parcours de saisie du mot de passe dans le navigateur de l’utilisateur. +### Attestation finale v11 + +Les six reprises ciblées réussissent : Analytics Risk desktop/mobile affiche 72/100, égal au score arrondi de son API, et le drawdown affiché correspond aux métriques du même appel ; Risk Dashboard desktop/mobile utilise le périmètre commun. Les pages Stock Analytics et Opportunities mesurent chacune 390 px de largeur documentaire pour un viewport de 390 px. Zéro erreur JavaScript non gérée et zéro réponse HTTP inattendue sur ces passages. Les dix autres passages v10, portant sur les fonctions inchangées, restent valides ; les lectures Admin ont bien été réalisées sur LAN 8083 avec les rôles récupérés depuis /auth/session, pas préinjectés dans userInfo. + +Douze tests de la protection d’aperçu réussissent dans l’image finale. Relecture finale : 62/62 artefacts disponibles et 13 intervalles validés. Logs v11 : aucune Traceback, PermissionError, FileNotFoundError ou TimeoutError ; seule réponse 503 du scheduler volontairement désactivé. Production et montages RO réattestés, root du conteneur RO, état healthy et pages de connexion HTTP 200. Aucun changement de production, aucune écriture Admin/financière, aucune fusion et aucun entraînement déclenché par les consultations. + +Les séries antérieures à la couverture vérifiée, les séances intérieures manquantes, les probabilités prédictives non évaluées et les gaps sectoriels indéterminés restent explicitement limités. Ces résultats techniques ne remplacent ni le test humain du navigateur de jack, ni une preuve de performance financière future. + +## Revue du 1 octobre - cycles, fraicheur et erreurs de logs + +- Comparaison authentifiee des DOM production 8080 et apercu v11, par lecture sur robot2 : frise narrative retiree, table 10 lignes devenue 4 cycles, deux premieres courbes absentes. Le graphique principal conserve ses 4287 observations dans les deux versions. La production affichait des phases futures comme deja acquises ; cet affichage n'est pas une preuve predictive. +- Reference Coin Metrics Community PriceUSD : 2010-07-18 a 2026-05-23, 5789 jours consecutifs, URL/empreinte/recu conserves. Cette archive est utilisee uniquement pour les cycles termines 1-3. Le cycle 4 garde exclusivement les clotures Binance USDT. Aucun raccord de fournisseurs au milieu d'un cycle, aucune utilisation de cette archive pour les forecasts. +- Frise graphique heuristique restauree (bornes explicites en mois). Comparaison quatre cycles, drawdowns causaux, table 13 mesures par cycle : ancrage, prix/dates extremes observes, durees, rendements, dernier prix et fournisseur. La courbe normalisee utilise une echelle logarithmique pour garder les quatre courbes lisibles. Les extrema historiques restent retrospectifs. +- Forecasts crypto : l'acquisition precedente etait figee au 29 septembre. Dataset d'evaluation immuable + extension publique complete des observations dans un cache propre, recue et empreintee, sans entrainement lors des GET. Chaque prefixe historique doit rester exactement identique. Provenance distingue dataset d'evaluation et dataset d'inference. Erreur fournisseur ou donnees invalides conservent la vraie date, et la barriere de fraicheur reste active. Les consultations concurrentes sont bornees et les echecs HTTP ne repetent pas l'acquisition pendant une minute. +- Les 62 artefacts ont ete reevalues explicitement avec le meme protocole et les memes acquisitions gelees, puis publies uniquement dans le checkout isole. BTC 7/30 jours disponible avec observation du 30 septembre en verification locale ; aucune precision directionnelle n'est revendiquee. +- Advanced Analytics : suppression du connecteur incompatible user_id et de toutes les performances simulees. Source authentifiee selectionnee, historique commun sans remplissage, base normalisee 100, annualisation crypto 365, mois composes et drawdowns reels y compris non recuperes. Metadonnees explicites : reconstruction retrospective a poids actuels, couverture partielle possible, pas NAV reel ni backtest. Comparaison de strategies indisponible sans serie validee. +- Mapping Yahoo partage pour SLHN.SW et WRDUSW.SW (ligne CHF connue), au lieu de SLHN/WRDUSW_CHF.SW. Le cours de WRDUSW en USD n'est pas suppose interchangeable avec une cotation CHF. +- CryptoToolbox : lecture publique directe confirme les placeholders zeros a t=0 et les chiffres charges a t=10s. Attente d'hydratation remplace le delai fixe 0.5s ; le rejet d'une vraie reponse invalide reste en place. +- Market Opportunities ne fait pas l'objet d'une nouvelle recherche dans ce lot. La classification incomplete et le besoin utilisateur de recommandations restent a traiter dans une discussion dediee. + +Source historique et attribution : Coin Metrics Community, https://github.com/coinmetrics/data ; licence CC BY-NC 4.0. Prix USD de reference, differents des clotures d'une place Binance USDT. L'archive n'est pas actualisee au 1 octobre ; elle ne fournit donc jamais le cycle courant. + +### Verification complementaire stocks et observation du 1 octobre + +- L'extension publique applique aussi le calendrier des seances aux actions/ETF. Une acquisition Yahoo ajustee recente doit retrouver les clotures de la periode de recouvrement (tolerance de representation numerique uniquement) : une revision de split/dividende est refusee et requiert une reevaluation explicite. Pas de remplissage des seances absentes. AAPL atteint le 30 septembre par cette extension, sans changer le dataset gele. +- Les preuves fournisseur nouvelles sont conservees dans le cache public prive a l'aperçu (reponse JSON Binance, observations CSV Yahoo du SDK), avec empreinte et version SDK. Aucune position personnelle n'y est enregistree. +- Controles locaux : 3443 tests globaux passes, 27 ignores, couverture 51.88% (seuil 30%). 138 tests JavaScript / 14 suites passes. 67 tests ML/source/regressions passes avant l'ajout stocks ; 15 tests du lot du 1 octobre passent avec le controle de revisions Yahoo et de seances manquantes. +- La consultation Admin reste protegee par la session authentifiee et le role existant ; les ecritures restent bloquees dans l'aperçu. Aucun changement en production 8080. + +### Controle authentifie de l'image et correction du dernier historique perime + +- Premier controle UI v12 : cycles et Admin passent en 1440/390, quatre courbes [1319, 1402, 1440, 894] observations, 13 lignes de mesures, frise 5 reperes. Le test AI capturait un appel legacy de benchmarks (6 forecasts) ; le controle a ete corrige pour exiger explicitement le mode portfolio et ses 25 actifs. +- Advanced revele un historique de position arrete au 13 fevrier 2026 : son intersection forcait toutes les positions dans une vieille fenetre. Le cohort filtre maintenant les observations perimees, exige une profondeur minimale explicite et coupe avant le dernier trou, sans remplissage ni lecture future. +- Verification sur robot2 avec jack, source cointracking_api, dans un processus de lecture du code corrige : 348 rendements communs, du 17 octobre 2025 au 30 septembre 2026, couverture en valeur courante 95.4278%, un historique perime exclu. Ces dates, le nombre d'observations, la couverture et le caractere retrospectif sont retournes par l'API et affiches. Le cache historique n'est pas certifie comme dataset de forecasting. +- Collecteur CryptoToolbox force en GET de lecture : HTTP 200, 30 indicateurs, cache false, scraping_failed false (1.1s sur ce controle). Les zéros de chargement ne sont plus publies. +- Controles globaux avant la derniere correction de cohorte : 3446 passes / 27 ignores, couverture 51.93%. 84 passes / 1 ignore sous Linux, 138 tests JavaScript. Le test supplementaire sur une position a historique perime passe dans les 18 tests du lot. + +### Attestation finale du lot - apercu v12 (1 octobre 2026) + +- Image en service : sha256:d989d9d1131f313658f449b9ab857cadffd409472d5876a5276147b891653315. Paquet : 89c1b9e4d0d4192a766f960af8e019246cf65b515a942943609fe9aaec9dbcc5 (341 fichiers, 62 artefacts). Les preuves finales sont externes au paquet de code, relevees apres son lancement. +- Cycles, AI Dashboard et Analytics : controles authentifies jack en desktop 1440 et mobile 390 passent, sans debordement ni erreur JavaScript. AI en mode portfolio : 25 lignes, 12/50 forecasts disponibles, BTC/ETH 7/30 jours disponibles, observation du 30 septembre, identites dataset d'evaluation/d'inference distinctes. Aucun resultat de benchmark utilise comme preuve du portefeuille. +- Admin : Users, Cache et ML repondent 200 en desktop/mobile. Le controle attend maintenant la fin de l'initialisation authentifiee du module avant de cliquer. Aucun changement de role et aucune ecriture Admin. +- Advanced : HTTP 200, source cointracking_api, methode actuelle et couverture explicites. L'historique perime et le trou ne sont pas remplis : fenetre continue 348 rendements, couverture 95.4278%, etat Partial. Date et methode sont visibles. +- Cotations corrigees testees en lecture publique sur robot2 : SLHN.SW et WRDUSW.SW renvoient chacun un historique et la devise CHF. Leur derniere barre fournisseur porte le 1 octobre ; la chaine previsionnelle exclut volontairement le jour UTC incomplet et reste sur la cloture complete du 30 septembre. +- Tests definitifs : 3447 passes / 27 ignores en Python, couverture 51.96%; 138 tests JavaScript / 14 suites; 85 passes / 1 ignore en Linux cible. 18 tests du lot couvrent extensions, prefixe gele, erreurs fournisseur, sessions, revisions de prix ajustes, isolation utilisateur/source, absence de mock et exclusion des historiques perimes. +- Logs du conteneur courant sur les 12 minutes controlees : 0 lignes ERROR, 0 Traceback, aucune recurrence des trois familles signalees (signature balances, alias Yahoo, zeros de scraping). Controle HTTP des pages : aucune erreur dans les lectures retenues. +- Production 8080 : image sha256:0c724b8b8ff87d73885bf0e109276c7b1da2b211f3c9c497b33f9bcda544f2aa et demarrage 2026-09-28T09:01:59.068663028Z inchanges, health HTTP 200. Apercu healthy, donnees de production montees en lecture seule, rootfs RO, UID 1000, limite 3 GiB / 2 CPU. 35.68 GiB libres environ, sans suppression d'image. +- Le lanceur attend desormais healthz 200 avant d'annoncer l'URL, pour eviter un controle premature pendant l'initialisation. Les captures privees du navigateur et les payloads financiers n'ont pas ete exportes. + +Limites maintenues : six actifs du top 25 sont couverts par les previsions de volatilite. Aucun modele de prix/direction, aucune probabilite directionnelle, aucun proxy de jeton enveloppe et aucune integration d'allocation ne sont ajoutes. Market Opportunities reste un travail distinct sur la couverture sectorielle et le besoin de recommandations. La verification fonctionnelle de ce lot ne remplace pas la validation visuelle finale de l'utilisateur ni une autorisation de publication en production. + +## Chargement ML bourse et encodage - 1 octobre 2026 (apercu v13) + +### Cause reproduite et corrections + +- Avec jack et son CSV Saxo configure, la requete portfolio stocks depassait le timeout de 60 secondes. Une requete equivalente avec un delai plus long renvoyait HTTP 200 et 91 resultats (30 actifs x trois sorties, plus la correlation). Le message generique masquait donc un timeout, pas une absence de toutes les predictions. +- Les calendriers exchange_calendars etaient reconstruits pour chaque lecture, puis relus pour le diagnostic, les deux horizons et la correlation. Le cache borne de 64 calendriers publics utilise des cles comprenant la place et les bornes exactes; le changement de jour change la cle. Aucun prix, portefeuille, resultat personnel ni prediction n'est mis dans ce cache. Les controles de hash, prefixe historique, sessions completes et revisions Yahoo restent appliques. +- Nouveau composant stock-ml-insights.js : etat Loading explicite, erreur distinguee pour session expiree, acces refuse, timeout/reseau ou HTTP, verification compte/source/marche/scope, rejet d'une reponse tardive apres changement de compte/source/fichier. Une erreur renvoie false au gestionnaire de sections afin que l'onglet puisse reessayer. Les chiffres precedents sont retires lors du chargement et d'une panne. +- Les sequences UTF-8 corrompues etaient litterales dans bourse-analytics.html (points de suspension, tirets, fleches, separateurs). Elles sont remplacees par des signes ASCII. Les messages de repli ML visibles encore corrompus dans AI Dashboard et Cycle Analysis sont nettoyes et affiches en anglais. +- Aucun changement des calculs de prevision, des artefacts evalues ou des regles d'allocation. Empreinte canonique conservee : 753382359cb9d21bfa992304d1acc27903585c433451a699be449cad8d2eabd1. + +### Preuves finales + +- Image : sha256:88d929489739ef104a7aeec255af1f3366b7424315db658c87611b99682479fe. Paquet v13 : 460254fd4f27565ca1e7525f6952bce07986e0c56045a4e8155503ef8005e8b7, 344 fichiers, 62 artefacts; aucune donnee utilisateur ni cle dans l'image. +- Controle navigateur authentifie jack sur l'image finale, desktop 1440 et mobile 390 : compte, source saxobank et CSV configure concordent. HTTP 200; 30 lignes d'actifs; 38/60 previsions de volatilite disponibles; statut Partial. Chargement mesure 45.76 s et 39.58 s. Aucun caractere corrompu dans les textes charges ou de chargement, aucune erreur JavaScript, aucun debordement horizontal. +- Python unit/integration : 3449 passes, 27 ignores, 30 avertissements, couverture 51.96%, seuil 30% respecte. Log : outputs/ml-reliability/global-pytest-stock-loading-v13-final.log. La premiere tentative sans ALLOWED_HOSTS a ete rejetee correctement; la collecte des anciens E2E necessitant un serveur local a ete arretee. La verification fonctionnelle utilise le navigateur authentifie sur robot2:8083. +- JavaScript : 151 tests passes / 15 suites. Les 13 nouveaux tests couvrent zero reel, resultat partiel, session expiree, acces refuse, timeout, erreur HTTP, reprise apres echec, changement utilisateur/source/fichier et rendu sur. +- Linux : 73 tests ML/regressions passent sur l'image avec la logique finale; apres nettoyage des seuls messages UI, 21 tests du lot passent sur l'image finale. Le test local supplementaire de textes visibles passe aussi. Les tests de calendrier comparent le cache au calendrier original, distinguent les places et verifient le changement de jour. +- Dernieres 12 minutes des logs du conteneur final : aucune ligne ERROR ni Traceback. Source privee et registry d'auth restent montes en lecture seule; aucun payload financier, mot de passe ou pixel prive exporte. +- Production 8080 : image et demarrage inchanges, health HTTP 200. Apercu healthy sur 8083, rootfs RO, UID 1000, 2 CPU / 3 GiB. Environ 35.4 GiB libres; aucune image supprimee. Checkout principal toujours a792a644; corrections conservees dans le checkout ML isole, aucun commit, fusion ou publication production. + +Limites : le chargement bourse reste de l'ordre de 40-46 secondes; les 22 autres demandes de prevision restent indisponibles avec leurs raisons, sans estimation de remplacement. Les nouveaux resultats sont des volatilites annualisees a 7/30 jours, pas des previsions de cours ou de hausse. + +### Relais propose pour une discussion Market Opportunities + +- Objectif : produire un scan utile et explicable pour la source et le CSV Saxo selectionnes, avec donnees verifiees et limites visibles. +- Etat : le scan ne classe aucune opportunite. La ventilation sectorielle verifiee est d'environ 44%; 55.9% reste non ventile, notamment des fonds/ETF. Les intervalles de manque sont indetermines; zero resultat ne prouve pas un portefeuille equilibre. Cette mesure vient des controles et retours precedents, sans nouveau scan dans ce lot. +- Decisions : ne pas deviner la composition des fonds ni convertir les budgets sectoriels en ordres; conserver la coherence source, fichier, devise et date. +- Fichiers utiles : docs/MARKET_OPPORTUNITIES_SYSTEM.md, api/ml_bourse_endpoints.py, services/ml/bourse/opportunity_scanner.py, services/ml/bourse/recommendations_orchestrator.py, static/bourse-recommendations.html, et le present rapport. +- Points ouverts : decomposition sectorielle datee des fonds/ETF, mapping ISIN/place/devise, donnees marche par candidat, politique des cibles et criteres de classement/vente. +- Prochaine action : audit en lecture seule des classifications exactes et du chemin scan -> classements -> simulation, puis plan borne propre a ce module avant modification. diff --git a/docs/audit/ML_RELIABILITY_ROBOT2_PREVIEW_2026-09-30.md b/docs/audit/ML_RELIABILITY_ROBOT2_PREVIEW_2026-09-30.md new file mode 100644 index 00000000..38c6ac0d --- /dev/null +++ b/docs/audit/ML_RELIABILITY_ROBOT2_PREVIEW_2026-09-30.md @@ -0,0 +1,136 @@ +# Aperçu ML sur robot2 — 30 septembre 2026 + +## État et périmètre + +Aperçu isolé : http://192.168.1.200:8083/static/ai-dashboard.html. +La production reste sur 8080, image +`sha256:0c724b8b8ff87d73885bf0e109276c7b1da2b211f3c9c497b33f9bcda544f2aa`, +démarrage de référence `2026-09-28T09:01:59.068663028Z`, base Git +`806338b522e89647c3f49260e0f418dbea1da761`. +Aucun commit, push, fusion, nettoyage Docker ou déploiement de production effectué. +Le retour utilisateur reste nécessaire : le fonctionnement technique ne certifie +pas toutes les fonctions financières du projet. + +## Incident et corrections + +La première proposition de test était prématurée. L'aperçu avait les historiques +vérifiés du nouveau service ML, mais pas le cache public d'historiques attendu +par les consommateurs historiques Risk et Crypto Regimes. L'artefact HMM ETH, +présent dans l'image exécutée en production, manquait également dans l'image de base. +Ces omissions expliquaient Risk 400, GRI indisponible et BTC/ETH sans historique. + +- Copie strictement publique dans les propres volumes de l'aperçu : 119 historiques + journaliers, 4,35 Mo, et les deux artefacts HMM BTC/ETH exécutés en production. + Les empreintes et périodes sont enregistrées dans `public-dependency-receipt.json` + sur robot2. Sept fichiers contenant des prix invalides sont exclus sans remplacement. + Cette copie reproduit les dépendances de production; la provenance fournisseur de + ce cache legacy n'est pas certifiée. Elle ne sert pas à valider les prévisions. +- Une session neuve ne chargeait pas la préférence de source dans le navigateur. + `selected-source.js` lit `/api/users/sources`, vérifie l'identité et reprend seulement + la source configurée et disponible. Une sélection explicite reste prioritaire; + source absente, session invalide ou changement d'identité restent indisponibles. +- Le cache Risk distingue désormais toutes les fenêtres et options de couverture. + Une consultation 30 jours ne peut pas fournir le résultat 365 jours à Advanced. +- La chronologie conserve les états HMM A–D après lissage, distincts des diagnostics + économiques. Les états ne sont plus convertis implicitement en Bear/Bull. +- Retrait de deux badges aléatoires; aucune date d'observation fictive pour ces badges. +- Le GRI absent ne devient plus zéro. Les calculs manuels de stress sont autorisés + dans l'aperçu, tout en bloquant les écritures financières et l'entraînement. +- Correction mobile du header et de la grille Risk : vérifiée sur contenu réel, + document 390 px pour viewport 390 px après application du correctif CSS. + +## Isolation et compte réel + +Seuls `data/users/jack` et le registre de connexion existant sont montés en lecture +seule, après accord explicite. Ils restent sur robot2, hors de l'image. Redis, cache, +logs, volumes de modèles, clé JWT et réseau Docker de test sont séparés. Le système +racine est en lecture seule; les artefacts de risque validés ont aussi un montage RO. +L'API tourne avec UID/GID 1000, limite 3 Go RAM / 2 CPU. Aucun scheduler, entraînement, +ordre ni journal d'inférences n'est activé. Les cookies entrants et sortants sont +isolés pour éviter de modifier la session de production, partagée entre ports. + +Contrôle authentifié : jack / CoinTracking API et CSV Saxo sélectionné (22 septembre, +30 positions). CoinTracking fournit 580 entrées brutes, dont 191 dépassent le seuil +USD du Risk Dashboard. Ce sont deux comptages différents; aucun compte demo ne sert +à cette vérification. La date d'observation du portefeuille API n'est pas exposée par +le fournisseur actuel et reste absente. Le dashboard dit désormais «Source entries». +Sur le périmètre sélectionné : 12/50 estimations crypto et 34/60 estimations actions +sont disponibles; les autres gardent leurs raisons d'indisponibilité. + +## Preuves de fonctionnement + +- 45 tests ciblés Windows, puis 54 tests Linux (contrats, cache, identité, HMM, + garde de l'aperçu). Les 58 artefacts validés ont aussi produit 58 inférences lors + du contrôle Linux; 12 intervalles publiables. Le code de calcul et ses artefacts + sont inchangés par les corrections de présentation et de source. +- 128 tests frontend / 12 suites, dont neuf tests de restauration de source. +- Contrôle global Python : 3413 passent dans la première exécution, 27 sont sautés. + Trois smoke échouaient car le host de leur client (`test`) n'était pas autorisé + par l'environnement de test. Ils passent tous après correction de cette seule + configuration. Couverture globale 51,14 %. Aucune règle de production assouplie. +- JWT manquant : 401; identité divergente : 403; session expirée : 401; + écriture financière : 403. +- Navigateur headless sur robot2, compte jack, session neuve : six pages desktop, + puis Risk / Market Regimes / Advanced en 390 px. Les erreurs principales sont + résolues. GRI numériquement rendu, six scénarios présents; boutons Stress Test + et Monte Carlo vérifiés (modal / graphique sans erreur). +- Les captures financières restent uniquement dans le cache privé de l'aperçu sur + robot2. Seuls les résumés techniques sans montants sont utilisés dans le rapport. + +## Limites et validations restantes + +Les alertes automatiques sont désactivées dans l'aperçu : leurs lectures renvoient +503, sans fausse liste de production. Crypto Toolbox a également renvoyé 502 sur +une collecte externe; aucun résultat de remplacement n'est inventé. Les prédictions +partielles et les expériences rejetées ne deviennent pas des signaux d'allocation. + +Les simulations de stress et Monte Carlo héritées restent des outils de scénarios, +sans validation de probabilités futures établie par ce chantier. Leur fonctionnement +technique n'est pas une validation prévisionnelle. La revue exhaustive de leurs +hypothèses, probabilités affichées et couverture ne doit pas être déclarée achevée +sur la base des seuls smoke tests de l'aperçu. + +Une publication exige la validation humaine, la revue finale du périmètre et une +autorisation distincte. Le préfixe de test et les indisponibilités attendues restent +visibles pendant cette étape. + +## Reproduire / arrêter + +Scripts publics : `deploy/ml-preview/`. Le paquet de code vérifie les empreintes et +exclut les données utilisateur. `setup_robot2.py` vérifie host, image de production, +base Git, marge disque et labels avant toute action limitée aux conteneurs de test. +Les journaux techniques se trouvent dans `/home/jack/smartfolio-ml-preview-20260930`. +Les images précédentes sont conservées. Ne pas nettoyer Docker sans accord distinct. + +Pour arrêter le test (sans toucher à la production), utiliser les noms exacts : +`docker stop smartfolio-ml-preview-api smartfolio-ml-preview-redis`. +Retour utilisateur à la production : http://192.168.1.200:8080. + +## Attestation finale de l'aperçu + +Image exécutée : `sha256:5a24fb1464f995b14d819321dabb7e0d3a9b0506ad0ec0c569e8dfbb03ca630e` (`smartfolio-ml-preview:20260930-v5`), état healthy. +Dernier contrôle : Risk, Market Regimes et Advanced, chacun à 1440 et 390 px, +compte jack, source restaurée depuis la préférence authentifiée. Six contrôles +réussis : aucune erreur visible, aucune exception JavaScript, aucun débordement +du document et aucune erreur API inattendue. Les seuls statuts en erreur relevés +sont ceux explicités ci-dessus (alertes 503; collecte Crypto Toolbox 502). +Production : même image et même StartedAt que la référence, healthz 200. +Login de l'aperçu : HTTP 200. Dossier jack et registre : montages RO; UID/GID +1000:1000; racine RO. Espace libre restant : 37.06 GiB. +Paquet public v5 : SHA-256 `51ce354aaccd826da42f459803c9583c3eb86ca2c6da81870618c00524c41b68`. +Kit public v5 : SHA-256 `d712a4b06e244ea50be0e40998e60b44b036f39ec0d47bebeb6dbacaef3744d8`. +Les captures privées restent sur robot2. La validation humaine est en attente. + + +## Révision après les régressions signalées + +L'attestation v5 ci-dessus ne suffit pas pour les parcours réels ; elle est remplacée par le [rapport de corrections et contrôles fonctionnels](ML_RELIABILITY_REPORTED_REGRESSIONS_2026-09-30.md). Version finale préparée : smartfolio-ml-preview:20260930-v8. Source crypto du compte jack, source bourse restaurée, taxonomie de production en lecture seule. Validation humaine encore requise. Le scheduler et les écritures financières restent désactivés dans le test. + + +Attestation v8 : healthy, image sha256:25cd0266064701add0a74ca18a07ab052cde95714d1681fab7cab06804c0fbe1. Paquet code 7b52b445e7a2e3f9d7530785ac43fb2bb852dd8274dd9791be8c196b34ae5ae4. Production : image et démarrage strictement identiques à la référence, healthz 200. Montages production RO et racine RO, UID 1000:1000, limite 2 CPU/3 GiB, espace libre 36.61 GiB. 58 tests Linux (ML, régressions et protection) passent sur cette image. Les helpers de contrôle ont été affinés après le kit initial ; leurs résultats finaux sont conservés sur robot2. + + +Révision finale v9 après correction de récupération Playwright : image sha256:7e657cbba244c1fbc140328878013c2643bad2ee282dd0c96fb737fffccaef3f, paquet code 21b58862c2f4ed4ea194fbb8e26a557baccae2959dac94f973923eb54eacee84. Collecte externe fraîche réussie (30 indicateurs, HTTP 200) ; 3425 tests Python globaux et 61 tests Linux. Le [rapport des régressions](ML_RELIABILITY_REPORTED_REGRESSIONS_2026-09-30.md) remplace les conclusions v5/v8. Publication de production non autorisée ; validation humaine encore en attente. + + +Contrôle final strict à 21:12 UTC : sept parcours ciblés v9 et 19 parcours complets antérieurs réutilisés réussis, aucune erreur API inattendue, aperçu healthy et production inchangée, marge disque 36.47 GiB. Les attestations techniques sont disponibles dans outputs/ml-reliability/verification-summary-final.json et preview-final-state.json ; les captures et comparaisons financières détaillées restent sur robot2. diff --git a/docs/audit/README.md b/docs/audit/README.md index 1ae2ce68..78df81d3 100644 --- a/docs/audit/README.md +++ b/docs/audit/README.md @@ -428,3 +428,10 @@ SmartFolio dispose d'un **système d'audit complet et automatisé**: **Méthode:** Multi-agents parallèles + automation GitHub Actions **Dernière mise à jour:** 29 Janvier 2026 **Prochaine revue:** Janvier 2026 + +## Remise à niveau ML — 30 septembre 2026 + +- [Inventaire](ML_RELIABILITY_INVENTORY_2026-09-30.md) +- [Protocole gelé](ML_RELIABILITY_PROTOCOL_2026-09-30.md) +- [Rapport par lot et résultats](ML_RELIABILITY_DELIVERY_2026-09-30.md) +- [Livraison et retour arrière](ML_RELIABILITY_RELEASE_2026-09-30.md) diff --git a/models/validated_risk/crypto_ADA_30d.json b/models/validated_risk/crypto_ADA_30d.json new file mode 100644 index 00000000..fb620594 --- /dev/null +++ b/models/validated_risk/crypto_ADA_30d.json @@ -0,0 +1,86 @@ +{ + "method": "ridge", + "features": [ + "rv7", + "rv30", + "rv90", + "ewma", + "abs_return" + ], + "mean": [ + 0.8019006547974186, + 0.9085984222593823, + 0.9515299504724729, + 0.9350743224543042, + 0.0363338714885567 + ], + "scale": [ + 0.4501323835172913, + 0.3503331756155101, + 0.27484631020040584, + 0.3433832277061174, + 0.03734690375467384 + ], + "coef": [ + 0.052803352925104595, + 0.0068335044262679175, + 0.09198545155409901, + 0.008963997483279883, + 0.023645816984761266 + ], + "intercept": 0.9243208459642606, + "asset": "ADA", + "market": "crypto", + "horizon": 30, + "protocol_id": "ml-risk-2026-09-30-v1", + "dataset_id": "verified-daily-crypto-ADA-daceae3a5ead5f22", + "dataset_sha256": "daceae3a5ead5f22cdee0d48aec61fea43998248ac1b0eb40da472f1b983790b", + "code_version": "753382359cb9d21bfa992304d1acc27903585c433451a699be449cad8d2eabd1", + "data_end": "2026-09-29T00:00:00+00:00", + "annualization": 365, + "schema_version": 1, + "training_start": "2018-07-16T00:00:00+00:00", + "training_end": "2025-02-28T00:00:00+00:00", + "validation": { + "state": "retrospectively_validated", + "reason": "Frozen walk-forward protocol; selected ridge. Learned model gates: QLIKE >=5%, folds >=2/3, MAE control and reserved confirmation.", + "protocol_id": "ml-risk-2026-09-30-v1", + "metrics": { + "development_qlike": { + "persistence": 0.4639390813021749, + "ewma": 0.37989708548435686, + "ridge": 0.2928244895745037, + "lstm": 0.45101267046646437 + }, + "learned_candidate": "ridge", + "learned_improvement": 0.22920048412279437, + "learned_wins": 8, + "windows": 9, + "confirmation": { + "persistence": { + "qlike": 0.26984284190717006, + "mae": 0.19215067105066827, + "rows": 335 + }, + "ewma": { + "qlike": 0.230687816539003, + "mae": 0.19030539551860404, + "rows": 335 + }, + "ridge": { + "qlike": 0.12450657177986191, + "mae": 0.16169662168073404, + "rows": 335 + }, + "lstm": { + "qlike": 0.12862213623726898, + "mae": 0.13952163633806214, + "rows": 335 + } + }, + "learned_confirmation_improvement": 0.4602811121634971, + "interval_coverage": 1.0 + } + }, + "interval": null +} \ No newline at end of file diff --git a/models/validated_risk/crypto_ADA_7d.json b/models/validated_risk/crypto_ADA_7d.json new file mode 100644 index 00000000..d576f543 --- /dev/null +++ b/models/validated_risk/crypto_ADA_7d.json @@ -0,0 +1,70 @@ +{ + "method": "ewma", + "features": [ + "rv7", + "rv30", + "rv90", + "ewma", + "abs_return" + ], + "asset": "ADA", + "market": "crypto", + "horizon": 7, + "protocol_id": "ml-risk-2026-09-30-v1", + "dataset_id": "verified-daily-crypto-ADA-daceae3a5ead5f22", + "dataset_sha256": "daceae3a5ead5f22cdee0d48aec61fea43998248ac1b0eb40da472f1b983790b", + "code_version": "753382359cb9d21bfa992304d1acc27903585c433451a699be449cad8d2eabd1", + "data_end": "2026-09-29T00:00:00+00:00", + "annualization": 365, + "schema_version": 1, + "training_start": "2018-07-16T00:00:00+00:00", + "training_end": "2025-03-23T00:00:00+00:00", + "validation": { + "state": "retrospectively_validated", + "reason": "Frozen walk-forward protocol; selected ewma. Learned model gates: QLIKE >=5%, folds >=2/3, MAE control and reserved confirmation.", + "protocol_id": "ml-risk-2026-09-30-v1", + "metrics": { + "development_qlike": { + "persistence": 0.9529701522109685, + "ewma": 0.5589939293585475, + "ridge": 0.5748317142208322, + "lstm": 0.7661177572326723 + }, + "learned_candidate": "ridge", + "learned_improvement": -0.028332659856358022, + "learned_wins": 6, + "windows": 9, + "confirmation": { + "persistence": { + "qlike": 1.7524047676440524, + "mae": 0.33554179694450414, + "rows": 358 + }, + "ewma": { + "qlike": 0.5645247201765755, + "mae": 0.28865104959299764, + "rows": 358 + }, + "ridge": { + "qlike": 0.47193660513369845, + "mae": 0.2565886564260161, + "rows": 358 + }, + "lstm": { + "qlike": 0.5212373601147975, + "mae": 0.23077075249336176, + "rows": 358 + } + }, + "learned_confirmation_improvement": 0.1640107363481209, + "interval_coverage": 0.8715083798882681 + } + }, + "interval": { + "level": 0.9, + "width": 0.509485609204939, + "confirmation_coverage": 0.8715083798882681, + "calibration_rows": 176, + "confirmation_rows": 358 + } +} \ No newline at end of file diff --git a/models/validated_risk/crypto_BCH_30d.json b/models/validated_risk/crypto_BCH_30d.json new file mode 100644 index 00000000..ca089039 --- /dev/null +++ b/models/validated_risk/crypto_BCH_30d.json @@ -0,0 +1,86 @@ +{ + "method": "ridge", + "features": [ + "rv7", + "rv30", + "rv90", + "ewma", + "abs_return" + ], + "mean": [ + 0.7719507288849967, + 0.8914026529376304, + 0.9513524831040853, + 0.917263212824519, + 0.03383456874877485 + ], + "scale": [ + 0.5415779161748646, + 0.4532523053014467, + 0.3609329644674374, + 0.4354115325843784, + 0.04081651708375329 + ], + "coef": [ + 0.07281787151514253, + 0.057795105171755096, + -0.03158649057365681, + 0.03883698345415807, + 0.023460391348420796 + ], + "intercept": 0.8781850547758124, + "asset": "BCH", + "market": "crypto", + "horizon": 30, + "protocol_id": "ml-risk-2026-09-30-v1", + "dataset_id": "verified-daily-crypto-BCH-abf046c2e64d86e3", + "dataset_sha256": "abf046c2e64d86e3a40bc44024d4055dfa80e4a536f548aba0accf97df1ca9ed", + "code_version": "753382359cb9d21bfa992304d1acc27903585c433451a699be449cad8d2eabd1", + "data_end": "2026-09-29T00:00:00+00:00", + "annualization": 365, + "schema_version": 1, + "training_start": "2020-02-26T00:00:00+00:00", + "training_end": "2025-02-28T00:00:00+00:00", + "validation": { + "state": "retrospectively_validated", + "reason": "Frozen walk-forward protocol; selected ridge. Learned model gates: QLIKE >=5%, folds >=2/3, MAE control and reserved confirmation.", + "protocol_id": "ml-risk-2026-09-30-v1", + "metrics": { + "development_qlike": { + "persistence": 0.9842365983511755, + "ewma": 0.8550453946938374, + "ridge": 0.37769114909262175, + "lstm": 0.42787963144237695 + }, + "learned_candidate": "ridge", + "learned_improvement": 0.5582794183367772, + "learned_wins": 5, + "windows": 5, + "confirmation": { + "persistence": { + "qlike": 0.9285190087083358, + "mae": 0.29843226021413016, + "rows": 335 + }, + "ewma": { + "qlike": 0.729920458310441, + "mae": 0.29017916981964, + "rows": 335 + }, + "ridge": { + "qlike": 0.30215237281733437, + "mae": 0.25988981482781254, + "rows": 335 + }, + "lstm": { + "qlike": 0.2494182710572446, + "mae": 0.21319614119662206, + "rows": 335 + } + }, + "learned_confirmation_improvement": 0.5860475352112592, + "interval_coverage": 0.6268656716417911 + } + }, + "interval": null +} \ No newline at end of file diff --git a/models/validated_risk/crypto_BCH_7d.json b/models/validated_risk/crypto_BCH_7d.json new file mode 100644 index 00000000..46bc0eb8 --- /dev/null +++ b/models/validated_risk/crypto_BCH_7d.json @@ -0,0 +1,86 @@ +{ + "method": "ridge", + "features": [ + "rv7", + "rv30", + "rv90", + "ewma", + "abs_return" + ], + "mean": [ + 0.77468676036917, + 0.8924650463263524, + 0.9500901796535419, + 0.9184504577223321, + 0.03388004169630902 + ], + "scale": [ + 0.5405799358083517, + 0.45072015750078137, + 0.35888029822846396, + 0.4330915325395432, + 0.04083961282337764 + ], + "coef": [ + 0.06305339317370467, + 0.01751306325933492, + -0.028443761545029554, + 0.12903684013233319, + 0.044822682846763655 + ], + "intercept": 0.7723180653769822, + "asset": "BCH", + "market": "crypto", + "horizon": 7, + "protocol_id": "ml-risk-2026-09-30-v1", + "dataset_id": "verified-daily-crypto-BCH-abf046c2e64d86e3", + "dataset_sha256": "abf046c2e64d86e3a40bc44024d4055dfa80e4a536f548aba0accf97df1ca9ed", + "code_version": "753382359cb9d21bfa992304d1acc27903585c433451a699be449cad8d2eabd1", + "data_end": "2026-09-29T00:00:00+00:00", + "annualization": 365, + "schema_version": 1, + "training_start": "2020-02-26T00:00:00+00:00", + "training_end": "2025-03-23T00:00:00+00:00", + "validation": { + "state": "retrospectively_validated", + "reason": "Frozen walk-forward protocol; selected ridge. Learned model gates: QLIKE >=5%, folds >=2/3, MAE control and reserved confirmation.", + "protocol_id": "ml-risk-2026-09-30-v1", + "metrics": { + "development_qlike": { + "persistence": 1.5932707862552604, + "ewma": 0.7702112244944019, + "ridge": 0.6223138241345355, + "lstm": 0.653802449234077 + }, + "learned_candidate": "ridge", + "learned_improvement": 0.1920218709574797, + "learned_wins": 6, + "windows": 6, + "confirmation": { + "persistence": { + "qlike": 1.9057430376811937, + "mae": 0.3111089370566388, + "rows": 358 + }, + "ewma": { + "qlike": 0.8752014245747844, + "mae": 0.2992554081695385, + "rows": 358 + }, + "ridge": { + "qlike": 0.6249230009543807, + "mae": 0.2800845781171791, + "rows": 358 + }, + "lstm": { + "qlike": 0.6231453108857872, + "mae": 0.2714552446029866, + "rows": 358 + } + }, + "learned_confirmation_improvement": 0.2859666547526488, + "interval_coverage": 0.8072625698324022 + } + }, + "interval": null +} \ No newline at end of file diff --git a/models/validated_risk/crypto_BNB_30d.json b/models/validated_risk/crypto_BNB_30d.json new file mode 100644 index 00000000..b6a8a349 --- /dev/null +++ b/models/validated_risk/crypto_BNB_30d.json @@ -0,0 +1,64 @@ +{ + "method": "ewma", + "features": [ + "rv7", + "rv30", + "rv90", + "ewma", + "abs_return" + ], + "asset": "BNB", + "market": "crypto", + "horizon": 30, + "protocol_id": "ml-risk-2026-09-30-v1", + "dataset_id": "verified-daily-crypto-BNB-d63248ee19e01f85", + "dataset_sha256": "d63248ee19e01f853b008c198ad6fa901530835b8bb1575d36dd07f261bc0988", + "code_version": "753382359cb9d21bfa992304d1acc27903585c433451a699be449cad8d2eabd1", + "data_end": "2026-09-29T00:00:00+00:00", + "annualization": 365, + "schema_version": 1, + "training_start": "2018-02-04T00:00:00+00:00", + "training_end": "2025-02-28T00:00:00+00:00", + "validation": { + "state": "retrospectively_validated", + "reason": "Frozen walk-forward protocol; selected ewma. Learned model gates: QLIKE >=5%, folds >=2/3, MAE control and reserved confirmation.", + "protocol_id": "ml-risk-2026-09-30-v1", + "metrics": { + "development_qlike": { + "persistence": 0.5600320758634065, + "ewma": 0.4610912252252784, + "ridge": 0.4582148749576425, + "lstm": 0.5631399487265362 + }, + "learned_candidate": "ridge", + "learned_improvement": 0.006238137076303163, + "learned_wins": 6, + "windows": 10, + "confirmation": { + "persistence": { + "qlike": 0.8197517782978112, + "mae": 0.22624404922976063, + "rows": 335 + }, + "ewma": { + "qlike": 0.6241632306770537, + "mae": 0.2167667595231917, + "rows": 335 + }, + "ridge": { + "qlike": 0.406098439342578, + "mae": 0.2306037037829564, + "rows": 335 + }, + "lstm": { + "qlike": 0.3551398387581282, + "mae": 0.2049919167068531, + "rows": 335 + } + }, + "learned_confirmation_improvement": 0.34937141538749805, + "interval_coverage": 0.37910447761194027 + } + }, + "interval": null +} \ No newline at end of file diff --git a/models/validated_risk/crypto_BNB_7d.json b/models/validated_risk/crypto_BNB_7d.json new file mode 100644 index 00000000..8f6c9846 --- /dev/null +++ b/models/validated_risk/crypto_BNB_7d.json @@ -0,0 +1,64 @@ +{ + "method": "ewma", + "features": [ + "rv7", + "rv30", + "rv90", + "ewma", + "abs_return" + ], + "asset": "BNB", + "market": "crypto", + "horizon": 7, + "protocol_id": "ml-risk-2026-09-30-v1", + "dataset_id": "verified-daily-crypto-BNB-d63248ee19e01f85", + "dataset_sha256": "d63248ee19e01f853b008c198ad6fa901530835b8bb1575d36dd07f261bc0988", + "code_version": "753382359cb9d21bfa992304d1acc27903585c433451a699be449cad8d2eabd1", + "data_end": "2026-09-29T00:00:00+00:00", + "annualization": 365, + "schema_version": 1, + "training_start": "2018-02-04T00:00:00+00:00", + "training_end": "2025-03-23T00:00:00+00:00", + "validation": { + "state": "retrospectively_validated", + "reason": "Frozen walk-forward protocol; selected ewma. Learned model gates: QLIKE >=5%, folds >=2/3, MAE control and reserved confirmation.", + "protocol_id": "ml-risk-2026-09-30-v1", + "metrics": { + "development_qlike": { + "persistence": 1.1638516552629508, + "ewma": 0.5661664956574098, + "ridge": 0.569771042113822, + "lstm": 0.6327843718017719 + }, + "learned_candidate": "ridge", + "learned_improvement": -0.006366583830127004, + "learned_wins": 5, + "windows": 10, + "confirmation": { + "persistence": { + "qlike": 1.129598342860046, + "mae": 0.19954660235925975, + "rows": 358 + }, + "ewma": { + "qlike": 0.6794785259862005, + "mae": 0.20921247398116877, + "rows": 358 + }, + "ridge": { + "qlike": 0.5600220806755974, + "mae": 0.21050925486263153, + "rows": 358 + }, + "lstm": { + "qlike": 0.5918914361062144, + "mae": 0.22486989559607823, + "rows": 358 + } + }, + "learned_confirmation_improvement": 0.17580606412428268, + "interval_coverage": 0.7737430167597765 + } + }, + "interval": null +} \ No newline at end of file diff --git a/models/validated_risk/crypto_BTC_30d.json b/models/validated_risk/crypto_BTC_30d.json new file mode 100644 index 00000000..3833a76f --- /dev/null +++ b/models/validated_risk/crypto_BTC_30d.json @@ -0,0 +1,64 @@ +{ + "method": "ewma", + "features": [ + "rv7", + "rv30", + "rv90", + "ewma", + "abs_return" + ], + "asset": "BTC", + "market": "crypto", + "horizon": 30, + "protocol_id": "ml-risk-2026-09-30-v1", + "dataset_id": "verified-daily-crypto-BTC-039ed8266a4b213f", + "dataset_sha256": "039ed8266a4b213f71d9a9c214068c99df7527fc05665f47f0301e499b0881f8", + "code_version": "753382359cb9d21bfa992304d1acc27903585c433451a699be449cad8d2eabd1", + "data_end": "2026-09-29T00:00:00+00:00", + "annualization": 365, + "schema_version": 1, + "training_start": "2017-11-15T00:00:00+00:00", + "training_end": "2025-02-28T00:00:00+00:00", + "validation": { + "state": "retrospectively_validated", + "reason": "Frozen walk-forward protocol; selected ewma. 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Learned model gates: QLIKE >=5%, folds >=2/3, MAE control and reserved confirmation.", + "protocol_id": "ml-risk-2026-09-30-v1", + "metrics": { + "development_qlike": { + "persistence": 1.0398634270852904, + "ewma": 0.4649495927937763, + "ridge": 0.425933435401409, + "lstm": 0.42918000322596506 + }, + "learned_candidate": "ridge", + "learned_improvement": 0.08391481140552914, + "learned_wins": 8, + "windows": 10, + "confirmation": { + "persistence": { + "qlike": 1.000078343458786, + "mae": 0.1646166437299781, + "rows": 358 + }, + "ewma": { + "qlike": 0.5004670391378835, + "mae": 0.15711922909722598, + "rows": 358 + }, + "ridge": { + "qlike": 0.43211531290412414, + "mae": 0.14976009913259794, + "rows": 358 + }, + "lstm": { + "qlike": 0.4414889927873076, + "mae": 0.1464208369345037, + "rows": 358 + } + }, + "learned_confirmation_improvement": 0.13657587990510556, + "interval_coverage": 0.9022346368715084 + } + }, + "interval": { + "level": 0.9, + "width": 0.26125595480567976, + "confirmation_coverage": 0.9022346368715084, + "calibration_rows": 176, + "confirmation_rows": 358 + } +} \ No newline at end of file diff --git a/models/validated_risk/crypto_DOT_30d.json b/models/validated_risk/crypto_DOT_30d.json new file mode 100644 index 00000000..755d1d90 --- /dev/null +++ b/models/validated_risk/crypto_DOT_30d.json @@ -0,0 +1,64 @@ +{ + "method": "ewma", + "features": [ + "rv7", + "rv30", + "rv90", + "ewma", + "abs_return" + ], + "asset": "DOT", + "market": "crypto", + "horizon": 30, + "protocol_id": "ml-risk-2026-09-30-v1", + "dataset_id": "verified-daily-crypto-DOT-602624fb9c11bb21", + "dataset_sha256": "602624fb9c11bb2122dcfd7b133864d248e0b866347b9519d031a7bb10955bc0", + "code_version": "753382359cb9d21bfa992304d1acc27903585c433451a699be449cad8d2eabd1", + "data_end": "2026-09-29T00:00:00+00:00", + "annualization": 365, + "schema_version": 1, + "training_start": "2020-11-16T00:00:00+00:00", + "training_end": "2025-02-28T00:00:00+00:00", + "validation": { + "state": "retrospectively_validated", + "reason": "Frozen walk-forward protocol; selected ewma. 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Learned model gates: QLIKE >=5%, folds >=2/3, MAE control and reserved confirmation.", + "protocol_id": "ml-risk-2026-09-30-v1", + "metrics": { + "development_qlike": { + "persistence": 0.9514468179493545, + "ewma": 0.40471968598717784, + "ridge": 0.4670017609453917, + "lstm": 0.5487508305426027 + }, + "learned_candidate": "ridge", + "learned_improvement": -0.15388941312873783, + "learned_wins": 3, + "windows": 4, + "confirmation": { + "persistence": { + "qlike": 1.9154526987544094, + "mae": 0.4417739559200823, + "rows": 358 + }, + "ewma": { + "qlike": 0.8166796709991451, + "mae": 0.38576715665637057, + "rows": 358 + }, + "ridge": { + "qlike": 0.7994285575176548, + "mae": 0.346455562755963, + "rows": 358 + }, + "lstm": { + "qlike": 0.9400223258358619, + "mae": 0.3141352545985889, + "rows": 358 + } + }, + "learned_confirmation_improvement": 0.021123476063001356, + "interval_coverage": 0.6396648044692738 + } + }, + "interval": null +} \ No newline at end of file diff --git a/models/validated_risk/crypto_ETH_30d.json b/models/validated_risk/crypto_ETH_30d.json new file mode 100644 index 00000000..be9aec70 --- /dev/null +++ b/models/validated_risk/crypto_ETH_30d.json @@ -0,0 +1,70 @@ +{ + "method": "ewma", + "features": [ + "rv7", + "rv30", + "rv90", + "ewma", + "abs_return" + ], + "asset": "ETH", + "market": "crypto", + "horizon": 30, + "protocol_id": "ml-risk-2026-09-30-v1", + "dataset_id": "verified-daily-crypto-ETH-cf74467c03ff4a30", + "dataset_sha256": "cf74467c03ff4a3044c7dd3c1e7b71fecde548882413fdfacd39b6e49269fb8c", + "code_version": "753382359cb9d21bfa992304d1acc27903585c433451a699be449cad8d2eabd1", + "data_end": "2026-09-29T00:00:00+00:00", + "annualization": 365, + "schema_version": 1, + "training_start": "2017-11-15T00:00:00+00:00", + "training_end": "2025-02-28T00:00:00+00:00", + "validation": { + "state": "retrospectively_validated", + "reason": "Frozen walk-forward protocol; selected ewma. 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Learned model gates: QLIKE >=5%, folds >=2/3, MAE control and reserved confirmation.", + "protocol_id": "ml-risk-2026-09-30-v1", + "metrics": { + "development_qlike": { + "persistence": 0.23383457077507283, + "ewma": 0.21452383619928553, + "ridge": 0.29346174910847755, + "lstm": 0.2031299859909978 + }, + "learned_candidate": "lstm", + "learned_improvement": 0.05311228071505869, + "learned_wins": 4, + "windows": 7, + "confirmation": { + "persistence": { + "qlike": 0.29881551539154344, + "mae": 0.2057039840169716, + "rows": 335 + }, + "ewma": { + "qlike": 0.22901890652627255, + "mae": 0.19423828085335434, + "rows": 335 + }, + "ridge": { + "qlike": 0.2971826541214425, + "mae": 0.289065406814149, + "rows": 335 + }, + "lstm": { + "qlike": 0.15973272023838106, + "mae": 0.15951687868821482, + "rows": 335 + } + }, + "learned_confirmation_improvement": 0.30253478779902887, + "interval_coverage": 0.991044776119403 + } + }, + "interval": null +} \ No newline at end of file diff --git a/models/validated_risk/crypto_LINK_7d.json b/models/validated_risk/crypto_LINK_7d.json new file mode 100644 index 00000000..2e71ee99 --- /dev/null +++ b/models/validated_risk/crypto_LINK_7d.json @@ -0,0 +1,70 @@ +{ + "method": "ewma", + "features": [ + "rv7", + "rv30", + "rv90", + "ewma", + "abs_return" + ], + "asset": "LINK", + "market": "crypto", + "horizon": 7, + "protocol_id": "ml-risk-2026-09-30-v1", + "dataset_id": "verified-daily-crypto-LINK-b93c2c5e76e38634", + "dataset_sha256": "b93c2c5e76e38634e50670ccd6d8b2d9fcb66e0035378f13a9c39182dc259047", + "code_version": "753382359cb9d21bfa992304d1acc27903585c433451a699be449cad8d2eabd1", + "data_end": "2026-09-29T00:00:00+00:00", + "annualization": 365, + "schema_version": 1, + "training_start": "2019-04-16T00:00:00+00:00", + "training_end": "2025-03-23T00:00:00+00:00", + "validation": { + "state": "retrospectively_validated", + "reason": "Frozen walk-forward protocol; selected ewma. 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a/outputs/ml-reliability/evaluations/crypto/ADA-30d.json b/outputs/ml-reliability/evaluations/crypto/ADA-30d.json new file mode 100644 index 00000000..ae74c2ae --- /dev/null +++ b/outputs/ml-reliability/evaluations/crypto/ADA-30d.json @@ -0,0 +1,335 @@ +{ + "asset": "ADA", + "market": "crypto", + "horizon": 30, + "protocol_id": "ml-risk-2026-09-30-v1", + "dataset_id": "verified-daily-crypto-ADA-daceae3a5ead5f22", + "dataset_sha256": "daceae3a5ead5f22cdee0d48aec61fea43998248ac1b0eb40da472f1b983790b", + "code_version": "753382359cb9d21bfa992304d1acc27903585c433451a699be449cad8d2eabd1", + "data_end": "2026-09-29T00:00:00+00:00", + "annualization": 365, + "state": "retrospectively_validated", + "selected": "ridge", + "reference": "ewma", + "folds": [ + { + "train_start": "2018-07-16T00:00:00+00:00", + "train_end": "2020-07-24T00:00:00+00:00", + "calibration_start": "2020-08-24T00:00:00+00:00", + "calibration_end": "2021-01-23T00:00:00+00:00", + "test_start": "2021-02-23T00:00:00+00:00", + 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Learned model gates: QLIKE >=5%, folds >=2/3, MAE control and reserved confirmation.", + "protocol_id": "ml-risk-2026-09-30-v1", + "metrics": { + "development_qlike": { + "persistence": 0.7293249841575475, + "ewma": 0.4634028443856721, + "ridge": 0.49601009057553297, + "lstm": 0.4754867967915403 + }, + "learned_candidate": "lstm", + "learned_improvement": -0.02607656071228437, + "learned_wins": 16, + "windows": 33, + "confirmation": { + "persistence": { + "qlike": 0.8294099007917642, + "mae": 0.061466320821678216, + "rows": 246 + }, + "ewma": { + "qlike": 0.45096809947277233, + "mae": 0.053590689634422865, + "rows": 246 + }, + "ridge": { + "qlike": 0.5672262163675642, + "mae": 0.04986012457838943, + "rows": 246 + }, + "lstm": { + "qlike": 0.4482571301737364, + "mae": 0.04613967301745564, + "rows": 246 + } + }, + "learned_confirmation_improvement": 0.0060114436081073475, + "interval_coverage": 0.959349593495935 + } + }, + "interval": null, + "lstm": { + "state": "evaluated", + "reason": "Corrected trailing sequences, train-only normalization, fixed seed 1729, 12 epochs; no legacy checkpoint reuse" + }, + "published": true, + "artifact": "models\\validated_risk\\stocks_XLU_7d.json", + "artifact_sha256": "305e73466e37dcdb57c146b392d24322aa32cb0b7895405333f1348a547a40ad" +} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 303d650f..de900806 100644 --- a/requirements.txt +++ b/requirements.txt @@ -54,3 +54,6 @@ prometheus-fastapi-instrumentator==8.1.0 psutil>=5.9.0 # System resource monitoring (CPU, RAM usage) pyarrow + +# Verified exchange sessions for calendar-day stock volatility targets +exchange-calendars>=4.5,<5 diff --git a/scripts/acquire_cycle_reference.py b/scripts/acquire_cycle_reference.py new file mode 100644 index 00000000..251c1619 --- /dev/null +++ b/scripts/acquire_cycle_reference.py @@ -0,0 +1,61 @@ +"""Explicit acquisition of public BTC reference prices for retrospective cycles only.""" + +import hashlib +import io +import json +from pathlib import Path +import httpx +import numpy as np +import pandas as pd + + +def main(): + url = "https://raw.githubusercontent.com/coinmetrics/data/master/csv/btc.csv" + response = httpx.get(url, timeout=45, follow_redirects=True) + response.raise_for_status() + raw = pd.read_csv(io.BytesIO(response.content), usecols=["time", "PriceUSD"]) + dates = pd.to_datetime(raw.time, utc=True).dt.normalize() + price = pd.to_numeric(raw.PriceUSD, errors="coerce") + frame = pd.DataFrame({"date": dates.dt.strftime("%Y-%m-%d"), "close": price}) + frame = frame[(dates < pd.Timestamp.now(tz="UTC").normalize()) & price.notna()].copy() + index = pd.DatetimeIndex(pd.to_datetime(frame.date, utc=True)) + if ( + index.has_duplicates + or not index.is_monotonic_increasing + or not ((index[1:] - index[:-1]).days == 1).all() + or not np.isfinite(frame.close).all() + or (frame.close <= 0).any() + ): + raise ValueError("Reference history is not consecutive valid daily observations") + directory = Path("data/ml_verified/cycles") + directory.mkdir(parents=True, exist_ok=True) + path = directory / "BTC.csv" + frame.to_csv(path, index=False) + sha = hashlib.sha256(path.read_bytes()).hexdigest() + receipt = { + "file": "BTC.csv", + "sha256": sha, + "dataset_id": "coinmetrics-btc-reference-" + sha[:16], + "provider": "Coin Metrics Community / PriceUSD daily reference rate", + "source_url": url, + "raw_response_sha256": hashlib.sha256(response.content).hexdigest(), + "fetched_at": pd.Timestamp.now(tz="UTC").isoformat(), + "method": "Vendor USD reference rate, distinct from Binance USDT exchange closes. Retrospective historical cycle comparison only; not used to train or infer volatility forecasts.", + "license": "CC BY-NC 4.0", + "attribution": "Coin Metrics Community https://github.com/coinmetrics/data", + } + (directory / "BTC.json").write_text(json.dumps(receipt, indent=2)) + print( + json.dumps( + { + "first": frame.date.iloc[0], + "last": frame.date.iloc[-1], + "rows": len(frame), + "provider": receipt["provider"], + } + ) + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/evaluate_ml_risk.py b/scripts/evaluate_ml_risk.py new file mode 100644 index 00000000..0a07c9de --- /dev/null +++ b/scripts/evaluate_ml_risk.py @@ -0,0 +1,119 @@ +"""Explicit CLI evaluation. No credentials, recurrent jobs or production writes. + +Run from the isolated project root. Research imports verify the acquisition hashes. +Stocks acquisition requires explicit --stocks and records adjustment/calendar policy. +""" +import argparse +import hashlib +import json +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +import pandas as pd +from services.ml.reliability import CapabilityService, digest +from services.ml.risk_evaluation import evaluate + + +def save_history(root, market, asset, frame, provenance): + directory = root / "data/ml_verified" / market + directory.mkdir(parents=True, exist_ok=True) + frame = frame[["date", "close"]].sort_values("date").drop_duplicates("date") + path = directory / f"{asset}.csv" + frame.to_csv(path, index=False) + sha = digest(path) + receipt = {**provenance, "file": path.name, "sha256": sha, "dataset_id": f"verified-daily-{market}-{asset}-{sha[:16]}"} + (directory / f"{asset}.json").write_text(json.dumps(receipt, indent=2), encoding="utf-8") + return receipt + + +def import_research(service, acquisition): + manifest_path = acquisition / "acquisition_manifest.json" + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + imported = [] + for row in manifest["inputs"]: + asset = row["symbol"] + if asset not in ("BTC", "ETH", "SOL", "ADA", "XRP", "LINK", "LTC", "BCH", "BNB", "DOT"): + continue + path = acquisition / row["ohlcv_file"] + if digest(path) != row["ohlcv_file_sha256"]: + raise ValueError(f"Research acquisition hash mismatch: {asset}") + frame = pd.read_csv(path) + receipt = save_history(service.root, "crypto", asset, frame, dict(provider=row["provider_provenance"], research_artifact_id=manifest["artifact_id"], acquisition_manifest_sha256=digest(manifest_path), original_ohlcv_sha256=row["ohlcv_file_sha256"], adjustment_policy="Unadjusted Binance Spot USDT daily closes")) + imported.append(receipt) + return imported + + +def acquire_stocks(service, symbols): + import yfinance as yf + receipts = [] + for symbol in symbols: + symbol = symbol.upper() + data = yf.download(symbol, start="2006-01-01", end=pd.Timestamp.utcnow().strftime("%Y-%m-%d"), auto_adjust=True, actions=True, progress=False) + if isinstance(data.columns, pd.MultiIndex): + data.columns = data.columns.droplevel(1) + if data.empty: + receipts.append(dict(asset=symbol,state="not_evaluable",reason="Provider returned no verifiable daily history")) + continue + frame = pd.DataFrame(dict(date=data.index.strftime("%Y-%m-%d"), close=data["Close"].to_numpy())) + receipts.append(save_history(service.root, "stocks", symbol, frame, dict(provider="Yahoo Finance via yfinance", yfinance_version=yf.__version__, adjustment_policy="auto_adjust=True: split/dividend-adjusted daily closes; current vendor revisions, not a point-in-time corporate-action archive", exchange_calendar=next((calendar for suffix,calendar in {".SW":"XSWX",".AS":"XAMS",".DE":"XFRA",".L":"XLON",".MI":"XMIL"}.items() if symbol.endswith(suffix)), "XNYS"), fetched_at=pd.Timestamp.utcnow().isoformat()))) + return receipts + + +def refresh_crypto(service, symbols): + import httpx + receipts = [] + for asset in symbols: + directory = service.root / "data/ml_verified/crypto" + receipt = json.loads((directory / f"{asset}.json").read_text(encoding="utf-8")) + frame = pd.read_csv(directory / receipt["file"]) + start = pd.Timestamp(frame.date.iloc[-1], tz="UTC")+pd.Timedelta(days=1) + end = pd.Timestamp.utcnow().normalize() + if start >= end: + continue + response = httpx.get("https://data-api.binance.vision/api/v3/klines", params=dict(symbol=asset+"USDT", interval="1d", startTime=int(start.timestamp()*1000), endTime=int(end.timestamp()*1000)-1, limit=1000), timeout=20) + response.raise_for_status() + additions = pd.DataFrame([dict(date=pd.Timestamp(row[0], unit="ms", tz="UTC").strftime("%Y-%m-%d"), close=float(row[4])) for row in response.json() if row[6] < int(end.timestamp()*1000)]) + if additions.empty: + raise ValueError(f"No complete refresh observations for {asset}") + receipts.append(save_history(service.root, "crypto", asset, pd.concat([frame, additions], ignore_index=True), {**receipt, "fetched_at": pd.Timestamp.utcnow().isoformat(), "refresh_raw_response_sha256": hashlib.sha256(response.content).hexdigest()})) + return receipts + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--research-acquisition", type=Path) + parser.add_argument("--stocks", nargs="*", default=[]) + parser.add_argument("--market", choices=["crypto", "stocks"], default="crypto") + parser.add_argument("--assets", nargs="+", default=["BTC", "ETH", "SOL"]) + parser.add_argument("--report-name", default=None, help="Distinct aggregate report name") + parser.add_argument("--publish", action="store_true", help="Publish validated artifacts in this isolated checkout only") + parser.add_argument("--refresh-crypto", action="store_true", help="Explicitly append complete daily Binance Spot closes before evaluation") + args = parser.parse_args() + service = CapabilityService(refresh_observations=False) + receipts = [] + if args.research_acquisition: + receipts.extend(import_research(service, args.research_acquisition)) + if args.stocks: + receipts.extend(acquire_stocks(service, args.stocks)) + if args.refresh_crypto: + receipts.extend(refresh_crypto(service, args.assets)) + directory = service.root / "outputs/ml-reliability" + directory.mkdir(parents=True, exist_ok=True) + reports = [] + for asset in args.assets: + for horizon in (7, 30): + try: + report = evaluate(service, args.market, asset.upper(), horizon, publish=args.publish) + except Exception as exc: + report = dict(asset=asset, market=args.market, horizon=horizon, state="not_evaluable", reason=str(exc), published=False) + reports.append(report) + report_dir = directory / "evaluations" / args.market + report_dir.mkdir(parents=True, exist_ok=True) + (report_dir / f"{asset.upper()}-{horizon}d.json").write_text(json.dumps(report, indent=2, allow_nan=False), encoding="utf-8") + print(json.dumps({k: report.get(k) for k in ("asset", "horizon", "state", "selected", "reason", "published")}), flush=True) + (directory / ((args.report_name or f"{args.market}-evaluation")+".json")).write_text(json.dumps(dict(receipts=receipts, reports=reports), indent=2, allow_nan=False), encoding="utf-8") + + +if __name__ == "__main__": + main() diff --git a/scripts/package_ml_reliability.py b/scripts/package_ml_reliability.py new file mode 100644 index 00000000..7998976c --- /dev/null +++ b/scripts/package_ml_reliability.py @@ -0,0 +1,115 @@ +"""Package verified ML code/data only. Never includes user data or credentials.""" +import argparse +import hashlib +import json +import subprocess +import sys +from datetime import datetime, timezone +from pathlib import Path +from zipfile import ZipFile, ZIP_DEFLATED + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +from services.ml.reliability import CapabilityService, code_version, PROTOCOL_ID + +BASE = "806338b522e89647c3f49260e0f418dbea1da761" +NEW_FILES = ( + 'docs/audit/MARKET_OPPORTUNITIES_DEPLOYMENT_2026-10-02.md', + 'docs/audit/MARKET_OPPORTUNITIES_UI_2026-10-02.md', + 'deploy/ml-production/release_robot2.py', + 'docs/audit/MARKET_OPPORTUNITIES_RELAY_2026-10-02.md', + 'docs/audit/MARKET_OPPORTUNITIES_AUDIT_2026-10-01.md', + 'tests/unit/test_market_opportunities_api.py', + 'static/tests/market-opportunities.test.js', + 'services/ml/bourse/fund_exposure.py', + 'services/ml/bourse/market_analysis.py', + 'services/ml/bourse/market_snapshot.py', + 'static/components/market-opportunities.js', + 'tests/unit/test_market_opportunities_v2.py', + + "static/components/stock-ml-insights.js", "static/tests/stock-ml-insights.test.js", "tests/unit/test_stock_ml_loading.py", + "services/ml/live_observations.py", "scripts/acquire_cycle_reference.py", "tests/unit/test_ml_oct1_repairs.py", + "scripts/repair_ml_stock_observations.py", "static/components/ml-result-table.js", + "static/core/risk-request.js", "static/core/role-policy.js", + "static/tests/ml-feedback-regressions.test.js", "tests/unit/test_ml_feedback_regressions.py", + "static/tests/bourse-source-context.test.js", + "docs/audit/ML_RELIABILITY_REPORTED_REGRESSIONS_2026-09-30.md", + "services/ml/cycle_diagnostics.py", "tests/unit/test_ml_reported_regressions.py", + "config/ml_capability_registry.json", + "services/ml/reliability.py", "services/ml/risk_evaluation.py", + "services/ml/portfolio_context.py", "scripts/evaluate_ml_risk.py", + "scripts/package_ml_reliability.py", "static/components/ml-overview.js", + "static/core/selected-source.js", "static/tests/selected-source.test.js", "static/tests/ml-overview.test.js", "tests/unit/test_ml_reliability.py", "tests/unit/test_ml_preview_regressions.py", + "docs/audit/ML_RELIABILITY_PROTOCOL_2026-09-30.md", + "docs/audit/ML_RELIABILITY_INVENTORY_2026-09-30.md", + "docs/audit/ML_RELIABILITY_DELIVERY_2026-09-30.md", + "docs/audit/ML_RELIABILITY_RELEASE_2026-09-30.md", "docs/audit/ML_RELIABILITY_ROBOT2_PREVIEW_2026-09-30.md", +) +SKIP = {"config/score_registry.json", "data/taxonomy_aliases.json"} +def git(*args): + return subprocess.check_output(["git", "-C", str(ROOT), *args]) +def sha(content): + return hashlib.sha256(content).hexdigest() +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--output", type=Path, default=ROOT/"outputs/ml-reliability/ml-reliability-release.zip") + args = parser.parse_args() + output = args.output.resolve() + if not output.is_relative_to((ROOT/"outputs/ml-reliability").resolve()): + raise ValueError("Release output must stay in the isolated ML output directory") + if git("merge-base", BASE, "HEAD").decode().strip() != BASE: + raise ValueError("Review the production base before packaging") + service = CapabilityService(refresh_observations=False) + registry = json.loads((ROOT/"models/validated_risk/registry.json").read_text(encoding="utf-8")) + for name, entry in registry.items(): + market, remainder = name.removesuffix(".json").split("_", 1) + asset, days = remainder.rsplit("_", 1) + artifact, _ = service.artifact(market, asset, int(days.removesuffix("d"))) + _, receipt = service.history(market, asset) + if artifact["dataset_id"] != receipt["dataset_id"] or entry["governance_eligible"] is not False: + raise ValueError("Artifact/dataset mismatch or unexpected governance eligibility") + paths = {} + for name in git("diff", "--name-only", BASE).decode().splitlines(): + if name in SKIP: + continue + content = (ROOT/name).read_bytes() + original = git("show", BASE+":"+name) + if content.replace(b"\r\n",b"\n") == original.replace(b"\r\n",b"\n"): + continue + if not name.startswith(("api/", "services/", "static/", "tests/", "docs/")) and name not in (".gitignore", ".gitattributes", "requirements.txt"): + raise ValueError("Unexpected modified path: "+name) + paths["code/"+name] = ROOT/name + for name in NEW_FILES: + if not (ROOT/name).is_file(): + raise ValueError("Required delivery file is missing: "+name) + paths["code/"+name] = ROOT/name + for directory, suffixes in (("data/ml_verified", {".json",".csv"}), ("models/validated_risk", {".json"}), ("outputs/ml-reliability/evaluations", {".json"})): + for path in sorted((ROOT/directory).rglob("*")): + if path.is_file() and path.suffix in suffixes: + paths["public/"+str(path.relative_to(ROOT)).replace("\\","/")] = path + for name in ("checks-summary.json", "production-readonly.json"): + path = ROOT/"outputs/ml-reliability"/name + if path.exists(): + paths["proof/"+name] = path + manifest = dict(base_commit=BASE, code_version=code_version(), protocol_id=PROTOCOL_ID, + created_at=datetime.now(timezone.utc).isoformat(), governance_integration=False, + private_data_included=False, entries=[ + dict(path=name, sha256=sha(path.read_bytes()), bytes=path.stat().st_size) + for name,path in sorted(paths.items())]) + output.parent.mkdir(parents=True, exist_ok=True) + with ZipFile(output,"w",ZIP_DEFLATED) as bundle: + for name,path in sorted(paths.items()): + bundle.write(path,name) + bundle.writestr("manifest.json",json.dumps(manifest,indent=2)) + with ZipFile(output) as bundle: + for row in manifest["entries"]: + if sha(bundle.read(row["path"])) != row["sha256"]: + raise ValueError("Packaged file hash mismatch") + if any("private/" in name or "data/users/" in name or ".env" in name for name in bundle.namelist()): + raise ValueError("Private content was included") + (output.with_suffix(".manifest.json")).write_text(json.dumps(manifest,indent=2),encoding="utf-8") + (output.with_suffix(".sha256")).write_text(sha(output.read_bytes())+" "+output.name+"\n",encoding="utf-8") + print(json.dumps(dict(package=str(output),files=len(paths),artifacts=len(registry),sha256=sha(output.read_bytes())))) +if __name__ == "__main__": + main() + diff --git a/scripts/repair_ml_stock_observations.py b/scripts/repair_ml_stock_observations.py new file mode 100644 index 00000000..4994ed92 --- /dev/null +++ b/scripts/repair_ml_stock_observations.py @@ -0,0 +1,95 @@ +"""Explicit repair of recorded provider rows; no filling of missing sessions.""" + +import argparse +import json +import sys +import shutil +from pathlib import Path +import numpy as np +import pandas as pd +import exchange_calendars as xc + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from services.ml.reliability import digest, CapabilityService +from scripts.evaluate_ml_risk import save_history + + +def repair_frame(frame, calendar, today): + dates = pd.DatetimeIndex(pd.to_datetime(frame.date, utc=True)).normalize() + if dates.has_duplicates or not dates.is_monotonic_increasing: + raise ValueError("Provider date order is invalid") + if len(frame) == 0: + raise ValueError("Provider returned no rows") + sessions = xc.get_calendar(calendar, start=dates[0].date(), end=dates[-1].date()).sessions + sessions = sessions.tz_localize("UTC") if sessions.tz is None else sessions.tz_convert("UTC") + complete = (dates < pd.Timestamp(today).normalize()) & dates.isin(sessions) + excluded = dates[~complete].strftime("%Y-%m-%d").tolist() + clean = frame.loc[complete].copy() + values = pd.to_numeric(clean.close, errors="coerce").to_numpy() + good = np.isfinite(values) & (values > 0) + valid = np.flatnonzero(good) + if not len(valid): + raise ValueError("No finite positive closes") + last = valid[-1] + excluded += clean.date.iloc[last + 1 :].tolist() + # Only incomplete trailing provider rows are dropped. Interior gaps stay visible. + clean = clean.iloc[: last + 1] + return clean, { + "excluded_dates": excluded, + "policy": "Exclude non-session/incomplete dates and trailing invalid provider rows; never fill an interior missing close", + } + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--assets", nargs="+", required=True) + args = parser.parse_args() + service = CapabilityService() + directory = service.root / "data/ml_verified/stocks" + for asset in args.assets: + receipt = json.loads((directory / (asset + ".json")).read_text()) + path = directory / receipt["file"] + if digest(path) != receipt["sha256"]: + raise ValueError("Acquisition hash mismatch") + raw = directory / "raw" / receipt["sha256"] + raw.mkdir(parents=True, exist_ok=True) + shutil.copy2(path, raw / path.name) + shutil.copy2(directory / (asset + ".json"), raw / (asset + ".json")) + frame, audit = repair_frame( + pd.read_csv(path), receipt["exchange_calendar"], pd.Timestamp.utcnow() + ) + receipt = save_history( + service.root, + "stocks", + asset, + frame, + { + **receipt, + "original_provider_sha256": receipt["sha256"], + "raw_archive": str((raw / path.name).relative_to(directory)), + "observation_filter": audit, + }, + ) + try: + close, _ = service.history("stocks", asset) + status = "Verified" + reason = None + except ValueError as error: + status = "Unavailable" + reason = str(error) + print( + json.dumps( + { + "asset": asset, + "status": status, + "reason": reason, + "excluded_rows": len(audit["excluded_dates"]), + "data_as_of": frame.date.iloc[-1], + } + ), + flush=True, + ) + + +if __name__ == "__main__": + main() diff --git a/services/ml/auto_trainer.py b/services/ml/auto_trainer.py index 1ce562c1..8501bdf1 100644 --- a/services/ml/auto_trainer.py +++ b/services/ml/auto_trainer.py @@ -59,45 +59,9 @@ def __init__(self): logger.info(" ML Auto-Trainer initialized") def start(self): - """Start the auto-trainer scheduler""" - if self._is_running: - logger.warning("Auto-trainer already running") - return - - # Schedule regime models training (daily - 3am) - self.scheduler.add_job( - func=self._train_regime_models, - trigger=CronTrigger(hour=3, minute=0), - id='regime_training_daily', - name='Daily Regime Models Training', - replace_existing=True - ) - - # Schedule volatility models training (daily - midnight) - self.scheduler.add_job( - func=self._train_volatility_models, - trigger=CronTrigger(hour=0, minute=0), - id='volatility_training_daily', - name='Daily Volatility Models Training', - replace_existing=True - ) - - # Schedule correlation models training (weekly - Sunday 4am) - self.scheduler.add_job( - func=self._train_correlation_models, - trigger=CronTrigger(day_of_week='sun', hour=4, minute=0), - id='correlation_training_weekly', - name='Weekly Correlation Models Training', - replace_existing=True - ) - - self.scheduler.start() - self._is_running = True - - logger.info(" ML Auto-Trainer started") - logger.info(" • Regime models: Daily at 3am") - logger.info(" • Volatility models: Daily at midnight") - logger.info(" • Correlation models: Every Sunday at 4am") + """Retired: model training requires an explicit protected action.""" + logger.info("Automatic ML training is disabled by the reliability protocol") + return False def stop(self): """Stop the auto-trainer scheduler""" diff --git a/services/ml/bourse/currency_detector.py b/services/ml/bourse/currency_detector.py index d72ae799..fa06751d 100644 --- a/services/ml/bourse/currency_detector.py +++ b/services/ml/bourse/currency_detector.py @@ -183,6 +183,17 @@ def detect_currency_and_exchange( logger.debug(f" Symbol transformation: {symbol} → {transformed_symbol}") symbol = transformed_symbol + # Legacy callers sometimes append the Yahoo venue before resolving a + # currency-qualified Saxo alias, e.g. WRDUSW_CHF.SW. + if '.' in symbol: + qualified_base, qualified_suffix = symbol.rsplit('.', 1) + if re.search(r'_[A-Z]{3}$', qualified_base.upper()): + if qualified_suffix.upper() != 'SW': + raise ValueError(f'Unverified currency-qualified venue: {symbol}') + if exchange_hint and self._parse_exchange_hint(symbol, exchange_hint)[0] != '.SW': + raise ValueError(f'Conflicting exchange information for {symbol}') + return self.detect_currency_and_exchange(qualified_base, isin=isin, exchange_hint='SWX') + # An explicit trading venue identifies the listing. A domicile (ISIN) # or a generic symbol map must not override it (e.g. SAP on NYSE). if exchange_hint: diff --git a/services/ml/bourse/fund_exposure.py b/services/ml/bourse/fund_exposure.py new file mode 100644 index 00000000..fc645209 --- /dev/null +++ b/services/ml/bourse/fund_exposure.py @@ -0,0 +1,133 @@ +"""Dated issuer sector look-through. Unknown weights are never renormalized.""" +from __future__ import annotations + +import hashlib +import math +import re +from datetime import date, datetime, timezone +from html.parser import HTMLParser + +SECTORS = ('Technology', 'Healthcare', 'Financials', 'Consumer Discretionary', + 'Communication Services', 'Industrials', 'Consumer Staples', 'Energy', + 'Utilities', 'Real Estate', 'Materials') +LABELS = {**{s: s for s in SECTORS}, 'Information Technology': 'Technology', + 'Health Care': 'Healthcare', 'Communication': 'Communication Services', + 'Financial Services': 'Financials', 'Consumer Cyclical': 'Consumer Discretionary', + 'Consumer Defensive': 'Consumer Staples', 'Basic Materials': 'Materials'} +# Public product identities, never a manifest of an account's holdings. +ISSUER_PRODUCTS = { + 'IE00B4L5Y983': 'https://www.ishares.com/uk/individual/en/products/251882/ishares-msci-world-ucits-etf-acc-fund', + 'IE00BKM4GZ66': 'https://www.ishares.com/uk/individual/en/products/264659/ishares-core-msci-em-imi-ucits-etf', +} +MAX_EXPOSURE_AGE_DAYS = 45 + + +class IssuerTableParser(HTMLParser): + """Read Fund cells and the date inside the sector tab, never a nearby NAV date.""" + def __init__(self): + super().__init__() + self.depth = 0 + self.panel_depth = None + self.panel_text = [] + self.table_depth = None + self.table_headers = [] + self.rows = [] + self.row = None + self.cell = None + self.text = [] + + def handle_starttag(self, tag, attrs): + if tag in ('br', 'img', 'input', 'meta', 'link', 'hr', 'source', 'wbr'): + return + self.depth += 1 + attrs = dict(attrs) + if attrs.get('id') == 'tabpanel-exposureBreakdowns-sector': + self.panel_depth = self.depth + if self.panel_depth is not None and tag == 'table' and attrs.get('data-id') == 'exposurebreakdowns-sector-table': + self.table_depth = self.depth + if self.table_depth is not None: + if tag == 'tr': + self.row = [] + elif tag in ('td', 'th'): + self.cell = [] + self.cell_tag = tag + + def handle_endtag(self, tag): + if tag in ('br', 'img', 'input', 'meta', 'link', 'hr', 'source', 'wbr'): + return + if tag in ('td', 'th') and self.cell is not None: + value = ' '.join(' '.join(self.cell).split()) + if self.cell_tag == 'th': + self.table_headers.append(value) + elif self.row is not None: + self.row.append(value) + self.cell = None + if tag == 'tr' and self.row is not None: + if self.row: + self.rows.append(self.row) + self.row = None + if self.table_depth == self.depth: + self.table_depth = None + if self.panel_depth == self.depth: + self.panel_depth = None + self.depth = max(0, self.depth - 1) + + def handle_data(self, data): + self.text.append(data) + if self.panel_depth is not None: + self.panel_text.append(data) + if self.cell is not None: + self.cell.append(data) + + +def parse_ishares(html, isin, url, today=None): + today = today or date.today() + parser = IssuerTableParser() + parser.feed(html) + if isin not in ' '.join(parser.text): + raise ValueError('Issuer page does not confirm the exact fund ISIN') + if parser.table_headers != ['Type', 'Fund']: + raise ValueError('The issuer table is not an unambiguous Fund sector breakdown') + panel = ' '.join(parser.panel_text) + dates = re.findall(r'as of\s+(\d{1,2}/[A-Za-z]+/\d{4})', panel, re.I) + if len(set(dates)) != 1: + raise ValueError('The issuer sector breakdown has no unambiguous observation date') + observed = datetime.strptime(dates[0].replace('/Sept/', '/Sep/'), '%d/%b/%Y').date() + if not 0 <= (today - observed).days <= MAX_EXPOSURE_AGE_DAYS: + raise ValueError('The issuer sector breakdown is stale or dated in the future') + weights = {} + unassigned = 0.0 + for row in parser.rows: + if len(row) != 2: + raise ValueError('Unexpected issuer table shape') + pct = float(row[1].replace('%', '').replace(',', '')) + if not math.isfinite(pct) or not 0 <= pct <= 100: + raise ValueError('Invalid issuer sector weight') + sector = LABELS.get(row[0]) + if sector: + if sector in weights: + raise ValueError('Duplicate issuer sector row') + weights[sector] = pct / 100 + else: + unassigned += pct / 100 + if not weights or sum(weights.values()) + unassigned > 1 + 1e-9: + raise ValueError('Issuer weights do not form a valid partial breakdown') + return dict(weights=weights, known_fraction=sum(weights.values()), as_of=observed.isoformat(), + retrieved_at=datetime.now(timezone.utc).isoformat(), source_url=url, + source_kind='issuer_fund_breakdown', source_sha256=hashlib.sha256(html.encode()).hexdigest(), + identity_basis='exact_isin', reason='Partial Fund table; omitted and non-industry rows remain unclassified') + + +async def issuer_exposure(isin, client): + url = ISSUER_PRODUCTS.get(isin) + if not url: + return dict(weights={}, known_fraction=0, as_of=None, source_url=None, source_kind='unavailable', + reason='No verified dated fund-sector adapter for this ISIN') + try: + response = await client.get(url) + response.raise_for_status() + return parse_ishares(response.text, isin, url) + except Exception: + # Never include URLs with query tokens or provider exception payloads in UI/logs. + return dict(weights={}, known_fraction=0, as_of=None, source_url=url, source_kind='unavailable', + reason='Issuer breakdown unavailable, stale, or rejected by identity/date/weight checks') diff --git a/services/ml/bourse/market_analysis.py b/services/ml/bourse/market_analysis.py new file mode 100644 index 00000000..5f89d70e --- /dev/null +++ b/services/ml/bourse/market_analysis.py @@ -0,0 +1,412 @@ +"""Explainable screening and cash-conserving scenarios; never an order engine.""" +from __future__ import annotations + +import asyncio +import math +import re +from datetime import datetime, timedelta, timezone + +import numpy as np +import pandas as pd + +from services.ml.bourse.fund_exposure import LABELS, SECTORS, issuer_exposure +from services.ml.bourse.horizons import OPPORTUNITY_HORIZONS +from services.ml.bourse.market_snapshot import number + +SECTOR_ETFS = dict(zip(SECTORS, ('XLK', 'XLV', 'XLF', 'XLY', 'XLC', 'XLI', 'XLP', 'XLE', 'XLU', 'XLRE', 'XLB'))) +CALENDARS = {'NMS': 'XNYS', 'NGM': 'XNYS', 'NCM': 'XNYS', 'NYQ': 'XNYS', 'PCX': 'XNYS', + 'ASE': 'XNYS', 'BTS': 'XNYS', 'EBS': 'XSWX', 'SWX': 'XSWX', 'LSE': 'XLON', + 'GER': 'XETR', 'AMS': 'XAMS', 'MIL': 'XMIL', 'PAR': 'XPAR', 'HKG': 'XHKG', + 'JPX': 'XTKS', 'KSC': 'XKRX'} + + +def exposure_summary(positions, targets, min_gap=5): + missing_values = sum(p['value_usd'] is None for p in positions) + total = sum(p['value_usd'] for p in positions if p['value_usd'] is not None) + known = {s: 0.0 for s in SECTORS} + for p in positions: + if p['value_usd'] is None: + continue + for sector, weight in p['classification']['weights'].items(): + known[sector] += p['value_usd'] * weight + classified = sum(known.values()) + unknown = max(0, total - classified) + if total <= 0: + return dict(coverage=None, unclassified_pct=None, sector_assessment=[], denominator_usd=total, + unvalued_positions=missing_values, valued_positions=len(positions) - missing_values, + coverage_scope='valued_subset_only' if missing_values else 'all_valued_securities', + reason='No positive valued securities; no allocation conclusion') + missing = unknown / total * 100 + assessments = [] + for sector in SECTORS: + lower = known[sector] / total * 100 + upper = min(100, lower + missing) + target = targets[sector] + deficit = target - upper + excess = lower - target + status = 'Underweight' if deficit >= min_gap and deficit > 0 else 'Overweight' if excess >= min_gap and excess > 0 else 'Indeterminate' if lower <= target <= upper and missing > 1e-8 else 'Within threshold' + assessments.append(dict(sector=sector, lower_pct=lower if not missing_values else None, + upper_pct=upper if not missing_values else None, target_pct=target, + status=status if not missing_values else 'Indeterminate', + certain_deficit_pct=max(0, deficit) if not missing_values else None, + certain_excess_pct=max(0, excess) if not missing_values else None)) + return dict(coverage=classified / total, unclassified_pct=missing, sector_assessment=assessments, + unvalued_positions=missing_values, valued_positions=len(positions) - missing_values, + coverage_scope='valued_subset_only' if missing_values else 'all_valued_securities', + denominator_usd=total, + reason=('Some position values are missing. Coverage is for the known-value subset; no full-portfolio industry intervals or conclusions can be computed. ' if missing_values else '') + 'Percentages use valued securities, excluding saved cash. Unknown and non-industry fund weights stay outside industry coverage. No equity-only denominator is assumed.') + + +def holding_reviews(positions, summary): + total = summary['denominator_usd'] + reviews = [] + for p in positions: + weight = p['value_usd'] / total * 100 if total and p['value_usd'] is not None and not summary.get('unvalued_positions') else None + reasons = [] + if p['value_usd'] is None: + reasons.append('Source valuation missing; position is retained, not counted as zero') + if weight is not None and weight > 10: + reasons.append('Position exceeds the generic 10% concentration review threshold') + if p['classification']['known_fraction'] < 1 - 1e-8: + reasons.append(p['classification']['reason']) + if not p['acquisition_date']: + reasons.append('Acquisition date is unavailable; holding-period and tax eligibility are not assessed') + reviews.append(dict(position_id=p['id'], symbol=p['symbol'], name=p['name'], weight_pct=weight, + review_reasons=reasons, action='REVIEW' if reasons else 'NO_RULE_TRIGGER', + sale_eligibility='unassessed', + sale_reason='No automatic sale: acquisition dates, tax lots, stop orders and personal limits are not verified')) + return reviews + + +class PublicMarketData: + """Bounded per-request work. Only the existing public price cache persists.""" + def __init__(self): + self.limit = asyncio.Semaphore(4) + self.profiles = {} + self.histories = {} + self.source = None + + async def profile(self, symbol): + if symbol not in self.profiles: + async def fetch(): + import yfinance as yf + async with self.limit: + try: + info = await asyncio.wait_for(asyncio.to_thread(lambda: yf.Ticker(symbol).info), 12) + return info if isinstance(info, dict) else {} + except Exception: + return {} + self.profiles[symbol] = asyncio.create_task(fetch()) + return await self.profiles[symbol] + + async def history(self, symbol, horizon, expected_currency=None): + key = (symbol, horizon, expected_currency) + if key not in self.histories: + async def fetch(): + from services.ml.bourse.data_sources import StocksDataSource + from services.ml.live_observations import completed_stock_sessions + from services.risk.bourse.calculator import BourseRiskCalculator + info = await self.profile(symbol) + async with self.limit: + if self.source is None: + self.source = StocksDataSource() + calendar = CALENDARS.get(info.get('exchange')) + if not calendar: + raise ValueError('Exchange calendar cannot be verified for this listing') + end = datetime.now(timezone.utc).replace(tzinfo=None) + frame = await self.source.get_ohlcv_data(symbol, OPPORTUNITY_HORIZONS[horizon].history_calendar_days, end) + native = frame.attrs.get('native_currency') + if expected_currency and native != expected_currency: + raise ValueError('Provider quote currency differs from the exact held listing') + if frame.attrs.get('price_origin') != 'yahoo' or frame.attrs.get('price_adjustment') != 'split_and_dividend_adjusted': + raise ValueError('Adjusted prices with verified provider provenance are required') + # Drop today's bar on every exchange, even if the market has closed. + frame = frame.loc[frame.index.normalize() < pd.Timestamp(end.date())].copy() + if frame.empty or frame.index.duplicated().any(): + raise ValueError('Completed prices are empty or contain duplicate sessions') + frame = frame.sort_index() + sessions = completed_stock_sessions(calendar, frame.index[0].date(), end.date()).tz_localize(None) + if not frame.index.equals(sessions) or frame.attrs.get('dropped_observations', 0): + raise ValueError('History is incomplete or stale relative to completed exchange sessions') + if not native: + raise ValueError('Provider quote currency is unavailable') + if native != 'USD': + fx = await self.source.fetcher.fetch_historical_fx(native, frame.index[0].to_pydatetime() - timedelta(days=7), end) + frame = BourseRiskCalculator._convert_prices_to_usd(frame, fx) + close = frame['close'] + if not np.isfinite(close).all() or (close <= 0).any(): + raise ValueError('Historical prices contain invalid observations') + close.attrs = dict(frame.attrs, currency='USD', calendar=calendar, source_url=f'https://finance.yahoo.com/quote/{symbol}/history/') + return close + self.histories[key] = asyncio.create_task(fetch()) + return await self.histories[key] + +def listing(position): + from services.ml.portfolio_context import stock_symbol + base, _, mic = position['symbol'].partition(':') + # The common helper has no ISIN argument. Qualified Saxo aliases need it. + if re.search(r'_(?:CHF|EUR|GBP|JPY|USD)(?:\.[A-Z]+)?$', base.upper()): + if not position.get('isin'): + raise ValueError('The qualified listing needs its exact ISIN') + if base.upper() in ('WRDUSW_USD', 'WRDUSW_USD.SW'): + raise ValueError('The exact USD listing has no verified provider mapping') + hints = {'xnas': 'NASDAQ', 'xnys': 'NYSE', 'arcx': 'NYSE', 'xswx': 'SWX', 'xvtx': 'SWX', + 'xams': 'AMS', 'xetr': 'XETRA', 'xlon': 'LSE', 'xmil': 'MIL'} + if mic.lower() not in hints: + raise ValueError('The qualified listing has no verified venue') + from services.ml.bourse.currency_detector import CurrencyExchangeDetector + mapped, currency, _ = CurrencyExchangeDetector().detect_currency_and_exchange(base, isin=position['isin'], exchange_hint=hints[mic.lower()]) + if currency != position.get('quote_currency'): + raise ValueError('Qualified ticker currency differs from the source quote currency') + from services.ml.reliability import valid_symbol + return valid_symbol(mapped) + return stock_symbol(position['symbol']) + + +async def classify(position, provider, client): + p = dict(position) + try: + p['history_symbol'] = listing(p) + p['mapping_reason'] = 'Explicit source listing; quote currency will be checked against history' + except ValueError: + p['history_symbol'] = None + p['mapping_reason'] = 'Exact listing mapping is unavailable; no replacement series' + if p['asset_class'].upper() in ('ETF', 'FUND', 'MUTUAL FUND'): + p['classification'] = await issuer_exposure(p['isin'], client) + p['geography'] = dict(status='unavailable', reason='No verified geography adapter; geography is separate from economic sectors') + elif p['asset_class'].upper() in ('STOCK', 'EQUITY', 'EQUITIES') and p['history_symbol']: + info = await provider.profile(p['history_symbol']) + sector = LABELS.get(info.get('sector')) + currency = {'GBp': 'GBP', 'GBX': 'GBP'}.get(info.get('currency'), info.get('currency')) + if not p.get('quote_currency') or currency != p['quote_currency']: + sector = None + p['classification'] = dict(weights={sector: 1} if sector else {}, known_fraction=1 if sector else 0, + as_of=None, retrieved_at=datetime.now(timezone.utc).isoformat(), + source_url=f"https://finance.yahoo.com/quote/{p['history_symbol']}/profile/", + source_kind='secondary_company_profile' if sector else 'unavailable', + reason='Company sector from a secondary provider; effective classification date unavailable' if sector else 'Company profile, industry or exact quote currency unavailable') + p['geography'] = dict(status='company_domicile_only', country=info.get('country'), + reason='Company domicile does not measure revenue geography') + else: + p['classification'] = dict(weights={}, known_fraction=0, as_of=None, source_url=None, source_kind='unavailable', + reason='Asset class or exact listing is not verified as an equity industry exposure') + p['geography'] = dict(status='unavailable', reason='Geography is unavailable') + return p + + +def historical_screen(close, sessions): + if len(close) < sessions + 1: + raise ValueError(f'History needs {sessions + 1} complete observations for this window') + window = close.iloc[-sessions - 1:] + returns = window.pct_change(fill_method=None).dropna() + deviation = float(returns.std(ddof=1)) + ratio = float(returns.mean()) / deviation * math.sqrt(sessions) if deviation > 0 else None + if ratio is None or not math.isfinite(ratio): + raise ValueError('Historical variability is insufficient to calculate a score') + score = max(0, min(100, 50 + 15 * ratio)) + return dict(score=score, return_pct=(float(window.iloc[-1]) / float(window.iloc[0]) - 1) * 100, + annualized_volatility_pct=deviation * math.sqrt(252) * 100, + sessions=sessions, start=window.index[0].date().isoformat(), end=window.index[-1].date().isoformat(), + currency='USD', method='clip(50 + 15 * mean(daily returns) / sample std * sqrt(window sessions), 0, 100)', + meaning='Historical risk-adjusted price screen. No expected return, probability or validated holding-period forecast.') + + +def candidate_universe(sector): + if sector not in (*SECTORS, 'all'): + raise ValueError('Unknown candidate sector') + if sector == 'all': + return [dict(symbol=e, name=f'{s} sector ETF', intended_sector=s, kind='ETF') for s, e in SECTOR_ETFS.items()] + from services.ml.bourse.sector_analyzer import SECTOR_TOP_STOCKS + etf = SECTOR_ETFS[sector] + return [dict(symbol=etf, name=f'{sector} sector ETF', intended_sector=sector, kind='ETF')] + [ + dict(symbol=s, name=n, intended_sector=sector, kind='STOCK') for s, n, _ in SECTOR_TOP_STOCKS[etf]] + + +async def screen_candidate(candidate, positions, provider, horizon): + row = dict(candidate, id=candidate['symbol'], status='unavailable', exclusions=[], metrics=None, rank=None) + held = {p['history_symbol'] for p in positions if p['history_symbol']} + if row['symbol'] in held: + row['status'] = 'held' + row['exclusions'] = ['The exact mapped listing is already held'] + return row + info = await provider.profile(row['symbol']) + row['reported_sector'] = LABELS.get(info.get('sector')) + row['isin'] = info.get('isin') + if row['isin'] and any(p['isin'] == row['isin'] for p in positions): + row['status'] = 'held' + row['exclusions'] = ['The same ISIN is already held on another listing'] + return row + if row['kind'] == 'STOCK' and row['reported_sector'] != row['intended_sector']: + row['exclusions'] = ['Current provider sector differs from the curated sector or is unavailable'] + return row + try: + close = await provider.history(row['symbol'], horizon) + row['metrics'] = historical_screen(close, OPPORTUNITY_HORIZONS[horizon].signal_sessions) + row.update(status='screened', price_source=close.attrs.get('source_url'), price_retrieved_at=close.attrs.get('retrieved_at')) + except Exception as exc: + row['exclusions'] = [str(exc) if isinstance(exc, ValueError) else 'Verified completed price history is unavailable'] + row['limits'] = ['Curated universe, not the whole market. No personal suitability or trading eligibility check.', + 'Fund constituents may overlap held funds. No constituent-level overlap calculation.', + 'ISIN deduplication is incomplete when the candidate provider does not expose an ISIN.', + 'ETF sector name describes its mandate; it is not a verified 100% sector decomposition.', + 'No valuation or diversification composite is invented.'] + return row + + +async def analyze(snapshot, horizon='medium', sector='all', targets=None, min_gap=5, provider=None): + if horizon not in OPPORTUNITY_HORIZONS: + raise ValueError('Invalid holding horizon') + from services.ml.bourse.opportunity_scanner import default_sector_targets, parse_sector_targets + allocations = parse_sector_targets(targets) if targets is not None else default_sector_targets() + provider = provider or PublicMarketData() + import httpx + async with httpx.AsyncClient(timeout=15, follow_redirects=False) as client: + # Issuer calls also bounded. No private symbols/ISINs are written to a cache. + semaphore = asyncio.Semaphore(4) + async def one(p): + async with semaphore: + return await classify(p, provider, client) + positions = await asyncio.gather(*(one(p) for p in snapshot['positions'])) + summary = exposure_summary(positions, allocations, min_gap) + candidates = await asyncio.gather(*(screen_candidate(c, positions, provider, horizon) for c in candidate_universe(sector))) + ranked = sorted((c for c in candidates if c['status'] == 'screened'), key=lambda c: c['metrics']['score'], reverse=True) + for rank, row in enumerate(ranked, 1): + row['rank'] = rank + # Scores across different observation dates are not a comparable ranking. + dates = {c['metrics']['end'] for c in ranked} + if len(dates) > 1: + for row in ranked: + row['rank'] = None + result = dict(version=2, nature='manual_decision_support', generated_at=datetime.now(timezone.utc).isoformat(), + context=snapshot['context'], snapshot_id=snapshot['snapshot_id'], cash=snapshot['cash'], + horizon=horizon, horizon_details=OPPORTUNITY_HORIZONS[horizon].metadata(), candidate_sector=sector, + target_source='personal_policy' if targets is not None else 'generic_reference', + target_note='Generic reference: normalized midpoint of the legacy static industry ranges, not current index weights or a recommended personal policy.' if targets is None else 'User-entered industry targets for all valued securities, excluding saved cash.', + targets=allocations, min_gap_pct=min_gap, positions=positions, exposure=summary, + candidates=candidates, holding_reviews=holding_reviews(positions, summary), + automatic_sales=[], automatic_sale_reason='No automated sale decision without personal constraints and verified acquisition/tax/stop-order data.', + candidate_summary=dict(total=len(candidates), screened=len(ranked), held=sum(c['status'] == 'held' for c in candidates), + unavailable=sum(c['status'] == 'unavailable' for c in candidates), + ranking_comparable=len(dates) <= 1), + risk_score=None, risk_reason='No validated portfolio robustness score is implemented here. Optional scenarios report actual historical volatility separately.', + conclusion='Zero screened candidates or certain gaps does not establish that this portfolio is balanced.', + scenario=None) + return result + + +def simulate(analysis, scenario): + """Estimated dollar amounts at snapshot values, never quantities or broker orders.""" + if not isinstance(scenario, dict) or set(scenario) - {'snapshot_id', 'trades', 'costs_usd', 'slippage_pct', 'acknowledge_dated_values', 'include_history'}: + raise ValueError('Invalid scenario fields') + if scenario.get('snapshot_id') != analysis['snapshot_id']: + raise ValueError('The source snapshot changed; scan again before simulating') + if scenario.get('acknowledge_dated_values') is not True: + raise ValueError('Confirm that the scenario uses dated position values and saved cash') + if any(p['value_usd'] is None for p in analysis['positions']): + raise ValueError('Some selected positions have no source valuation; a full-portfolio scenario cannot be valued') + cash = analysis['cash']['value_usd'] + if cash is None: + raise ValueError('Source-consistent verified cash is unavailable; a funding scenario cannot be calculated') + cash = number(cash, 'Saved cash') + costs = number(scenario.get('costs_usd'), 'Total estimated costs in USD') + slippage = number(scenario.get('slippage_pct'), 'Estimated slippage percentage') + if costs < 0 or not 0 <= slippage <= 10: + raise ValueError('Costs must be non-negative and slippage between 0 and 10%') + trades = scenario.get('trades') + if not isinstance(trades, list) or not 1 <= len(trades) <= 50: + raise ValueError('Choose between 1 and 50 scenario changes') + after = {p['id']: dict(p) for p in analysis['positions']} + purchases = {c['id']: c for c in analysis['candidates'] if c['status'] == 'screened'} + seen = set() + gross = 0.0 + for trade in trades: + if not isinstance(trade, dict) or set(trade) != {'side', 'id', 'amount_usd'}: + raise ValueError('Each change needs side, id and amount_usd') + side, identity = trade['side'], trade['id'] + if not isinstance(identity, str) or side not in ('sell', 'buy') or (side, identity) in seen: + raise ValueError('Unknown side, invalid identity or duplicate change') + seen.add((side, identity)) + amount = number(trade['amount_usd'], 'Scenario amount') + if amount <= 0: + raise ValueError('Scenario amounts must be positive') + gross += amount + if side == 'sell': + if identity not in after or amount > after[identity]['value_usd']: + raise ValueError('A sale must identify a held position and cannot exceed its snapshot value') + after[identity]['value_usd'] -= amount + cash += amount + else: + if identity not in purchases: + raise ValueError('A purchase must identify a screened candidate in the current scan') + c = purchases[identity] + after['candidate:' + identity] = dict(id='candidate:' + identity, symbol=c['symbol'], name=c['name'], + isin=c.get('isin'), history_symbol=c['symbol'], quote_currency=None, value_usd=amount, + classification=dict(weights={}, known_fraction=0, reason='Candidate fund composition or dated company classification is not added to verified allocation by assumption')) + # For screened individual stocks, the observed secondary sector is available. + if c['kind'] == 'STOCK' and c.get('reported_sector'): + after['candidate:' + identity]['classification'] = dict(weights={c['reported_sector']: 1}, known_fraction=1, reason='Secondary company profile') + cash -= amount + friction = costs + gross * slippage / 100 + cash -= friction + if cash < -1e-8: + raise ValueError('The combined changes exceed saved cash plus sale proceeds after costs and slippage') + before_total = sum(p['value_usd'] for p in analysis['positions']) + analysis['cash']['value_usd'] + after_positions = list(after.values()) + after_total = sum(p['value_usd'] for p in after_positions) + cash + if not math.isclose(after_total, before_total - friction, abs_tol=1e-6): + raise ValueError('Scenario cash conservation check failed') + exposure = exposure_summary(after_positions, analysis['targets'], analysis['min_gap_pct']) + return dict(status='calculated', before_total_usd=before_total, after_total_usd=after_total, + cash_before_usd=analysis['cash']['value_usd'], cash_after_usd=max(0, cash), costs_and_slippage_usd=friction, + exposure_before=analysis['exposure'], exposure_after=exposure, + positions_after=after_positions, trades=trades, + historical_risk=None, risk_score=None, + limits=['Amounts are manual USD estimates at dated snapshot values, not executable quantities.', + 'Fees and slippage are user assumptions. Taxes, FX execution spreads, liquidity, lot sizes and stop orders are not modeled.', + 'Purchased ETFs remain unclassified until an exact dated composition is verified.', + 'No financial orders are created or submitted.']) + + +async def scenario_history(analysis, scenario, provider=None): + provider = provider or PublicMarketData() + risk = dict(status='unavailable', volatility_before_pct=None, volatility_after_pct=None, correlation_changes=[], + reason='All valued holdings need exact USD histories and common completed return intervals; no partial-portfolio proxy') + combined = {p['id']: p for p in analysis['positions'] + scenario['positions_after'] if p['value_usd'] > 0} + async def prices(p): + if not p['history_symbol']: + raise ValueError('An exact held listing cannot be mapped') + if not p['id'].startswith('candidate:') and not p.get('quote_currency'): + raise ValueError('Held quote currency is missing; exact currency matching is unavailable') + return p['id'], await provider.history(p['history_symbol'], analysis['horizon'], p.get('quote_currency')) + outcomes = await asyncio.gather(*(prices(p) for p in combined.values()), return_exceptions=True) + failed = sum(isinstance(x, Exception) for x in outcomes) + risk.update(positions_required=len(outcomes), histories_available=len(outcomes) - failed) + if failed: + return risk + closes = dict(outcomes) + returns = pd.concat({k: v.pct_change(fill_method=None) for k, v in closes.items()}, axis=1).dropna() + # Same start AND end dates for each daily return: no mixed holiday intervals. + starts = pd.concat({k: pd.Series(v.index, index=v.index).shift(1) for k, v in closes.items()}, axis=1).reindex(returns.index) + aligned = starts.nunique(axis=1) == 1 + returns = returns.loc[aligned] + required = max(60, OPPORTUNITY_HORIZONS[analysis['horizon']].signal_sessions) + if len(returns) < required or not np.isfinite(returns.to_numpy()).all(): + risk['reason'] = f'Fewer than {required} common completed daily USD return intervals' + return risk + def weighted(positions, cash): + total = sum(p['value_usd'] for p in positions) + cash + if total <= 0: + raise ValueError('Portfolio total must be positive') + # Cash return is held at zero; no invented interest or FX account return. + return sum((returns[p['id']] * (p['value_usd'] / total) for p in positions if p['value_usd'] > 0), pd.Series(0., index=returns.index)) + before = weighted(analysis['positions'], scenario['cash_before_usd']) + after = weighted(scenario['positions_after'], scenario['cash_after_usd']) + risk.update(status='calculated', volatility_before_pct=float(before.std(ddof=1) * math.sqrt(252) * 100), + volatility_after_pct=float(after.std(ddof=1) * math.sqrt(252) * 100), + sessions=len(returns), start=starts.loc[returns.index[0]].iloc[0].date().isoformat(), end=returns.index[-1].date().isoformat(), + reason='Historical fixed USD weights including zero-return cash; daily-rebalanced approximation. No validated Risk Score or forward forecast.') + for p in scenario['positions_after']: + if p['id'].startswith('candidate:') and p['value_usd'] > 0: + correlation = returns[p['id']].corr(before) + risk['correlation_changes'].append(dict(symbol=p['symbol'], correlation_to_before=float(correlation) if pd.notna(correlation) else None)) + return risk diff --git a/services/ml/bourse/market_snapshot.py b/services/ml/bourse/market_snapshot.py new file mode 100644 index 00000000..d0e568b0 --- /dev/null +++ b/services/ml/bourse/market_snapshot.py @@ -0,0 +1,226 @@ +"""A strict, private, request-scoped snapshot for manual decision support.""" +from __future__ import annotations + +import csv +import hashlib +import json +import math +import re +from datetime import datetime, timezone +from pathlib import Path + + +def number(value, label): + if isinstance(value, bool) or value is None: + raise ValueError(f"{label} is missing or invalid") + try: + result = float(value) + except (TypeError, ValueError): + raise ValueError(f"{label} is missing or invalid") from None + if not math.isfinite(result): + raise ValueError(f"{label} must be finite") + return result + + +def csv_value_currency(content: bytes): + """Read the original header before the legacy connector removes its unit.""" + for encoding in ('utf-8-sig', 'cp1252', 'latin1'): + try: + text = content.decode(encoding) + except UnicodeDecodeError: + continue + for delimiter in (',', ';', '\t'): + header = next(csv.reader(text.splitlines(), delimiter=delimiter), []) + if len(header) < 2: + continue + currencies = [] + for label in header: + match = re.fullmatch(r'(?:Valeur actuelle|Market Value)\s*\(([A-Z]{3})\)', label.strip(), re.I) + if match: + currencies.append(match[1].upper()) + if len(currencies) == 1: + return currencies[0] + return None + return None + + +def normalize_rows(rows, valuation_currency, rate): + """Keep ISIN, exact listing and quote currency separate from value currency.""" + normalized = [] + identities = set() + for row in rows: + symbol = str(row.get('symbol') or row.get('instrument_id') or '').strip() + isin = str(row.get('isin') or '').upper().strip() + identity = str(row.get('position_id') or '') + '|' + isin + '|' + symbol + if not symbol or identity in identities: + raise ValueError('Missing or duplicate position identity; holdings cannot be silently merged') + identities.add(identity) + quantity = number(row.get('quantity'), 'Position quantity') + native = None if row.get('_valuation_missing') is True else number(row.get('market_value'), 'Position value') + if quantity <= 0 or (native is not None and native < 0): + raise ValueError('This analysis requires long positions with non-negative values') + usd = number(native * rate, 'USD position value') if native is not None else None + normalized.append(dict( + id=hashlib.sha256(identity.encode()).hexdigest()[:20], symbol=symbol, isin=isin or None, + name=str(row.get('name') or row.get('instrument') or row.get('asset_name') or symbol), + asset_class=str(row.get('asset_class') or 'Unknown'), + quote_currency=row.get('currency'), exchange=row.get('exchange'), quantity=quantity, + value_native=native, value_usd=usd, valuation_currency=valuation_currency, + valuation_reason='Selected CSV valuation is missing; never treated as zero' if native is None else 'Explicit selected CSV value', + acquisition_date=row.get('purchase_date') or row.get('opened_at'), + )) + return normalized + + +def load_csv_snapshot(user, file_key=None, root=None, rate_getter=None, row_loader=None): + from api.services.user_fs import UserScopedFS + from services.fx_service import get_verified_rate + root = root or Path(__file__).resolve().parents[3] + if not re.fullmatch(r'[A-Za-z0-9_-]+', user): + raise ValueError('Invalid authenticated user identifier') + fs = UserScopedFS(str(root), user) + config = json.loads(Path(fs.get_path('config.json')).read_text(encoding='utf-8')) + selected = config.get('sources', {}).get('bourse', {}) + active = selected.get('active_source') + if active not in ('saxobank', 'saxobank_csv'): + raise ValueError('The active stock source is not Saxo CSV') + effective = selected.get('selected_csv_file') + if not isinstance(effective, str) or not effective or '/' in effective or '\\' in effective or Path(effective).suffix.lower() != '.csv': + raise ValueError('No valid selected Saxo CSV; no other file is substituted') + if file_key and effective != file_key: + raise ValueError('The requested CSV differs from the active selection; refresh the source') + path = Path(fs.get_path('saxobank/data/' + effective)) + if not path.is_file(): + raise ValueError('The selected Saxo CSV is unavailable; no other file is substituted') + content = path.read_bytes() + currency = csv_value_currency(content) + if not currency: + raise ValueError('The selected CSV value currency is not explicit in its header') + rate = number((rate_getter or get_verified_rate)(currency), 'Verified value FX rate') + if rate <= 0: + raise ValueError('Verified value FX rate must be positive') + if row_loader is None: + from connectors.saxo_import import SaxoImportConnector + connector = SaxoImportConnector() + frame = connector._load_file(path) + # Request-local connector: replace its indicative conversion, not a global service. + connector._convert_to_usd = lambda amount, unused_currency: amount * rate + rows = [] + for _, original in frame.iterrows(): + placeholders = {'', 'nan', 'none', 'n/a', '-', '—'} + identifiers = [str(original.get(k, '')).strip() for k in ('Symbol', 'ISIN')] + if not any(value.lower() not in placeholders for value in identifiers): + continue # Report summary lines do not identify a tradable position. + # pandas 3 infers a strict string dtype; numeric normalization needs object cells. + row = original.astype(object).copy() + missing_value = False + for field in ('Quantity', 'Market Value'): + raw = str(row.get(field, '')).replace('\u00a0', '').replace(' ', '').replace("'", '').replace(',', '.') + if field == 'Market Value' and raw.lower() in placeholders: + missing_value = True + row[field] = 0 # Only to retain the identity through the old connector. + else: + row[field] = number(raw, 'CSV ' + field) + if row['Quantity'] <= 0 or row['Market Value'] < 0: + raise ValueError('Zero/short quantities and negative values require a separate analysis; no row is dropped') + row['Account Currency'] = currency + parsed = connector._process_position(row, user_id=user) + if not parsed: + raise ValueError('An identified CSV position was rejected; no partial portfolio conclusion') + source_currency = str(original.get('Currency', '')).strip() + parsed['currency'] = source_currency.upper() if source_currency.lower() not in placeholders else None + if missing_value: + parsed['market_value'] = None + parsed['_valuation_missing'] = True + rows.append(parsed) + else: + rows = row_loader(user, effective) + positions = normalize_rows(rows, currency, rate) + # Do not display the connector's old indicative conversion as verified data. + cash_path = Path(fs.get_path('saxobank/cash/' + effective + '_cash.json')) + cash = dict(value_usd=None, currency=None, as_of=None, status='unavailable', reason='No cash record for the selected CSV') + if cash_path.is_file(): + saved = json.loads(cash_path.read_text(encoding='utf-8')) + try: + cash_currency = saved['currency'] + amount = number(saved.get('cash_amount'), 'Cash amount') + cash_rate = number((rate_getter or get_verified_rate)(cash_currency), 'Cash FX rate') + if cash_rate <= 0: + raise ValueError('Cash FX rate must be positive') + cash = dict(value_usd=amount * cash_rate, currency=cash_currency, as_of=saved.get('last_updated'), + status='available', reason='Saved cash for the same selected CSV; not a live bank balance') + except (ValueError, KeyError, TypeError): + cash['reason'] = 'Saved cash cannot be valued with an explicit currency and verified FX rate' + # A file timestamp is not a financial valuation date. + imported = datetime.fromtimestamp(path.stat().st_mtime, timezone.utc).isoformat() + match = re.match(r'^(\d{8}_\d{6})_', effective) + if match: + try: + imported = datetime.strptime(match[1], '%Y%m%d_%H%M%S').isoformat() + except ValueError: + pass + fx_as_of = None + if currency != 'USD' or cash.get('currency') not in (None, 'USD'): + from services import fx_service + timestamp = getattr(fx_service, '_RATES_SOURCE_TIMESTAMP', 0) + fx_as_of = datetime.fromtimestamp(timestamp, timezone.utc).isoformat() if timestamp else None + metadata = dict(user_id=user, source='saxobank_csv', file_key=effective, imported_at=imported, + valuation_as_of=None, valuation_currency=currency, reporting_currency='USD', + fx_as_of=fx_as_of, fx_rate=rate, + warnings=['The import date is not a confirmed valuation date. Source values are converted with current verified FX, not historical FX.', + 'Saved cash and positions are dated separately. Confirm balances before using a scenario.']) + missing_count = sum(p['value_usd'] is None for p in positions) + metadata['unvalued_positions'] = missing_count + if missing_count: + metadata['warnings'].append(f'{missing_count} positions have no source valuation. Coverage describes the valued subset only; full-portfolio sector conclusions and scenarios are unavailable.') + if path.read_bytes() != content: + raise ValueError('The selected CSV changed during analysis; scan again') + scope = hashlib.sha256(content + json.dumps(cash, sort_keys=True).encode() + str(rate).encode() + user.encode() + effective.encode()).hexdigest() + return dict(positions=positions, cash=cash, context=metadata, snapshot_id=scope) + + +async def load_snapshot(user, source, file_key, request, api_base): + """No source/CSV fallback and no global cache of private snapshots.""" + root = Path(__file__).resolve().parents[3] + from api.services.user_fs import UserScopedFS + if not re.fullmatch(r'[A-Za-z0-9_-]+', user): + raise ValueError('Invalid authenticated user identifier') + fs = UserScopedFS(str(root), user) + config = json.loads(Path(fs.get_path('config.json')).read_text(encoding='utf-8')) + active = config.get('sources', {}).get('bourse', {}).get('active_source') + active = 'saxobank_csv' if active == 'saxobank' else active + source = 'saxobank_csv' if source == 'saxobank' else source + if source and source != active: + raise ValueError('The requested source differs from the active stock source; refresh the source') + if active == 'saxobank_csv': + import asyncio + return await asyncio.to_thread(load_csv_snapshot, user, file_key) + if active not in ('manual_bourse', 'saxobank_api') or file_key: + raise ValueError('Select an explicit supported stock source') + if active == 'manual_bourse': + from services.sources import source_registry + provider = source_registry.get_source(active, user, root) + if not provider: + raise ValueError('The selected manual source is unavailable') + items = await provider.get_balances() + rows = [dict(symbol=i.symbol, quantity=i.amount, market_value=i.value_usd, + currency=i.currency, asset_class=i.asset_class, name=i.alias) for i in items] + else: + import httpx + from api.ml_bourse_endpoints import _forward_authenticated_headers + async with httpx.AsyncClient(timeout=30) as client: + response = await client.get(api_base + '/api/saxo/api-positions', headers=_forward_authenticated_headers(request, user)) + if response.status_code == 401: + raise ValueError('Saxo Bank is not connected') + response.raise_for_status() + rows = response.json().get('data', {}).get('positions', []) + # Only accept an explicit value-currency contract from the API. + if any(r.get('valuation_currency') != 'USD' for r in rows): + raise ValueError('The Saxo API positions do not expose an explicit USD valuation contract') + positions = normalize_rows(rows, 'USD', 1) + context = dict(user_id=user, source=active, file_key=None, imported_at=None, valuation_as_of=None, + valuation_currency='USD', reporting_currency='USD', fx_as_of=None, + warnings=['Cash and financial valuation dates are unavailable for this source.']) + return dict(positions=positions, context=context, + cash=dict(value_usd=None, status='unavailable', reason='No source-consistent cash record', currency=None, as_of=None), + snapshot_id=hashlib.sha256(json.dumps(positions, sort_keys=True).encode()).hexdigest()) diff --git a/services/ml/bourse/opportunity_scanner.py b/services/ml/bourse/opportunity_scanner.py index 000f4a5f..4258dd93 100644 --- a/services/ml/bourse/opportunity_scanner.py +++ b/services/ml/bourse/opportunity_scanner.py @@ -306,7 +306,8 @@ async def scan_opportunities( logger.info(f" Scanning opportunities for {len(positions)} positions (horizon: {horizon})") # 1. Extract current sector allocation - current_allocation = self._extract_sector_allocation(positions) + classified_positions = self._classify_positions(positions) + current_allocation = self._extract_sector_allocation(classified_positions) logger.debug(f"Current allocation: {current_allocation}") # 2. Detect gaps vs standard sectors @@ -334,6 +335,8 @@ async def scan_opportunities( "all_gaps": scored_gaps, "top_gaps": top_gaps, "current_allocation": current_allocation, + "_classified_positions": classified_positions, + "sector_assessment": self._describe_sector_bounds(current_allocation, targets, unclassified_pct), "target_allocations": targets if targets is not None else default_sector_targets(), "target_source": "user" if targets is not None else "generic_reference", "classification_coverage": (100.0 - unclassified_pct) / 100.0, @@ -389,71 +392,48 @@ def _enrich_position_with_sector(self, symbol: str) -> str: logger.info(f" {symbol} → Error fetching sector: {e}") return "Unknown" - def _extract_sector_allocation(self, positions: List[Dict[str, Any]]) -> Dict[str, float]: - """ - Extract sector allocation from portfolio positions. - Automatically enriches positions with sectors from Yahoo Finance if missing. - - Args: - positions: List of positions with sector info - - Returns: - Dict mapping sector → allocation percentage - """ - try: - # Calculate total portfolio value - # Note: Saxo positions use "market_value" field (already in USD) - total_value = sum(p.get("market_value", 0) or p.get("market_value_usd", 0) for p in positions) - - if total_value == 0: - logger.warning("Total portfolio value is 0") - return {} - - # Group by sector - sector_values = {} - for pos in positions: - # Try to get existing sector, otherwise enrich from Yahoo Finance - sector_raw = pos.get("sector") - - if not sector_raw or sector_raw == "Unknown": - # Support both "symbol" and "instrument_id" field names - symbol = pos.get("symbol") or pos.get("instrument_id") - if symbol: - sector_raw = self._enrich_position_with_sector(symbol) - # Do not mutate the selected portfolio snapshot. - - sector_raw = sector_raw or "Unknown" - - # Map to GICS sector - sector = SECTOR_MAPPING.get(sector_raw, sector_raw) - - # Skip if not a standard sector - if sector not in STANDARD_SECTORS and sector != "Unknown": - # Try fuzzy match - matched = False - for key in SECTOR_MAPPING.keys(): - if key.lower() in sector_raw.lower(): - sector = SECTOR_MAPPING[key] - matched = True - break - if not matched: - sector = "Other" - - # Use "market_value" field (already in USD for Saxo positions) - value = pos.get("market_value", 0) or pos.get("market_value_usd", 0) - sector_values[sector] = sector_values.get(sector, 0) + value - - # Convert to percentages - allocation = { - sector: (value / total_value) * 100 - for sector, value in sector_values.items() - } - - return allocation - - except Exception as e: - logger.error(f"Error extracting sector allocation: {e}", exc_info=True) - return {} + def _classify_positions(self, positions): + """One classified copy for scan and impact; preserve the source snapshot.""" + classified = [] + for position in positions: + row = dict(position) + raw = row.get("sector") + if not raw or raw == "Unknown": + symbol = row.get("symbol") or row.get("instrument_id") + raw = self._enrich_position_with_sector(symbol) if symbol else None + raw = raw or "Unknown" + sector = SECTOR_MAPPING.get(raw, raw) + if sector not in STANDARD_SECTORS: + sector = next((mapped for label, mapped in SECTOR_MAPPING.items() + if label.lower() in raw.lower()), "Other") + row["sector"] = sector + classified.append(row) + return classified + + def _extract_sector_allocation(self, positions): + classified = self._classify_positions(positions) + values = {} + for row in classified: + value = row.get("market_value", 0) or row.get("market_value_usd", 0) + values[row["sector"]] = values.get(row["sector"], 0) + value + total = sum(values.values()) + return {sector: value / total * 100 for sector, value in values.items()} if total > 0 else {} + + def _describe_sector_bounds(self, allocation, targets, unclassified_pct): + """Bounds express unknown fund exposure, never a ranked buy signal.""" + targets = targets if targets is not None else default_sector_targets() + rows = [] + for sector in INDUSTRY_SECTORS: + known = allocation.get(sector, 0.0) + upper = min(100.0, known + unclassified_pct) + minimum_gap = max(0.0, targets[sector] - upper) + possible_gap = max(0.0, targets[sector] - known) + rows.append({"sector": sector, "known_pct": known, "possible_total_pct": upper, + "target_pct": targets[sector], "minimum_gap_pct": minimum_gap, + "possible_gap_pct": possible_gap, + "status": "Verified underweight" if minimum_gap > 0 else + "Indeterminate" if possible_gap > 0 else "No underweight"}) + return rows def _detect_gaps( self, diff --git a/services/ml/bourse/recommendations_orchestrator.py b/services/ml/bourse/recommendations_orchestrator.py index 289291e8..52fa2e9c 100644 --- a/services/ml/bourse/recommendations_orchestrator.py +++ b/services/ml/bourse/recommendations_orchestrator.py @@ -18,6 +18,7 @@ from datetime import datetime, timedelta from services.ml.bourse.horizons import RECOMMENDATION_HORIZONS import logging +import asyncio from services.ml.bourse.technical_indicators import TechnicalIndicators from services.ml.bourse.scoring_engine import ScoringEngine @@ -99,34 +100,35 @@ async def generate_recommendations( # Generate recommendations for each position recommendations = [] unavailable_symbols = [] - for pos in positions: + semaphore = asyncio.Semaphore(4) + async def analyze_one(pos): symbol = pos.get("instrument_id") or pos.get("symbol") or pos.get("ticker") or "UNKNOWN" - try: - rec = await self._analyze_position( - position=pos, - technical=technical, - scoring=scoring, - decision=decision, - targets=targets, - market_regime=market_regime, - regime_probabilities=regime_probabilities, - sector_analysis=sector_analysis, - benchmark_data=benchmark_data, - lookback_days=history_days, - signal_days=signal_days, - total_portfolio_value=total_value, - available_cash_usd=cash_amount, - ) - - if rec: - recommendations.append(rec) - else: - unavailable_symbols.append(symbol) - - except Exception as e: - logger.error(f"Error analyzing position {pos.get('symbol', 'unknown')}: {e}") + async with semaphore: + try: + rec = await self._analyze_position( + position=pos, + technical=technical, + scoring=scoring, + decision=decision, + targets=targets, + market_regime=market_regime, + regime_probabilities=regime_probabilities, + sector_analysis=sector_analysis, + benchmark_data=benchmark_data, + lookback_days=history_days, + signal_days=signal_days, + total_portfolio_value=total_value, + available_cash_usd=cash_amount, + ) + return symbol, rec + except Exception as error: + logger.warning("Position analysis unavailable for %s: %s", symbol, error) + return symbol, None + for symbol, rec in await asyncio.gather(*(analyze_one(pos) for pos in positions)): + if rec: + recommendations.append(rec) + else: unavailable_symbols.append(symbol) - continue # Apply portfolio-level adjustments recommendations = adjuster.adjust_recommendations( @@ -356,6 +358,9 @@ async def _analyze_position( "sector": sector_data.get('sector', 'Unknown') if sector_data else 'Unknown', "action": decision_result['action'], "confidence": decision_result['confidence'], + "nature": "diagnostic", "validation_state": "descriptive", + "confidence_is_probability": False, "method": "weighted_technical_rules", + "data_as_of": hist_data.index[-1].isoformat(), "confidence_kind": "signal_agreement_not_probability", "data_coverage": score_result['data_coverage'], "missing_signals": score_result['missing_signals'], @@ -527,24 +532,22 @@ async def _compute_sector_analysis( positions_returns = {} positions_values = {} - for pos in positions: - symbol = pos.get('instrument_id', pos.get('symbol', pos.get('ticker'))) + semaphore = asyncio.Semaphore(4) + async def sector_history(pos): + symbol = pos.get('instrument_id') or pos.get('symbol') or pos.get('ticker') if not symbol: - continue - - # Store position value - value = pos.get('market_value', 0) or 0 + return + value = pos.get('market_value', 0) or pos.get('market_value_usd', 0) or 0 if value > 0: positions_values[symbol] = positions_values.get(symbol, 0.0) + float(value) - - # Fetch historical data - try: - price_data = await data_fetcher.fetch_historical_prices(symbol, start_date, end_date) - if len(price_data) >= 30: - returns = price_data['close'].pct_change().dropna() - positions_returns[symbol] = returns - except Exception as e: - logger.warning(f"Failed to fetch {symbol}: {e}") + async with semaphore: + try: + price_data = await data_fetcher.fetch_historical_prices(symbol, start_date, end_date) + if len(price_data) >= 30: + positions_returns[symbol] = price_data['close'].pct_change().dropna() + except Exception as error: + logger.warning("Sector history unavailable for %s: %s", symbol, error) + await asyncio.gather(*(sector_history(pos) for pos in positions)) if len(positions_returns) < 2: logger.warning("Not enough positions with data for sector analysis") diff --git a/services/ml/bourse/stocks_adapter.py b/services/ml/bourse/stocks_adapter.py index 0781afd7..96d31d8f 100644 --- a/services/ml/bourse/stocks_adapter.py +++ b/services/ml/bourse/stocks_adapter.py @@ -96,244 +96,29 @@ def __init__(self, models_dir: str = "models/stocks"): logger.info(f"StocksMLAdapter initialized with models_dir={models_dir}") - async def predict_volatility( - self, - symbol: str, - lookback_days: int = 365, - confidence_level: float = 0.95, - train_if_missing: bool = False - ) -> Dict[str, Any]: - """ - Predict future volatility for a stock. - - Args: - symbol: Stock ticker (e.g., "AAPL") - lookback_days: Days of history to use - confidence_level: Confidence interval level - - Returns: - Dict with predictions for 1d, 7d, 30d horizons - """ - ohlcv_data = None - try: - # Fetch OHLCV data - ohlcv_data = await self.data_source.get_ohlcv_data( - symbol=symbol, - lookback_days=lookback_days - ) - - if len(ohlcv_data) < 30: - raise ValueError(f"Insufficient data for {symbol}: only {len(ohlcv_data)} days") - - model_symbol = ohlcv_data.attrs.get('history_symbol') or symbol.replace(':', '_') - # A GET may load a trained model but must never start training. - if model_symbol not in self.volatility_predictor.models: - self.volatility_predictor.load_model(model_symbol) - if train_if_missing: - logger.info(f"Training volatility model for {symbol}...") - # Train model on historical data - training_metadata = self.volatility_predictor.train_model( - symbol=model_symbol, - price_data=ohlcv_data, - validation_split=0.2 - ) - logger.info(f"Model trained. Metrics: {training_metadata.get('metrics', {})}") - if model_symbol not in self.volatility_predictor.models or not self.volatility_predictor.metadata[model_symbol].get('validated_against_baseline'): - return await self._fallback_historical_volatility(symbol, ohlcv_data, lookback_days, confidence_level) - - # Predict volatility - prediction = self.volatility_predictor.predict_volatility( - symbol=model_symbol, - recent_data=ohlcv_data, - confidence_level=confidence_level - ) - - result = { - 'symbol': symbol, - 'timestamp': datetime.now().isoformat(), - 'predictions': prediction.get('predictions', {}), - 'model_type': 'LSTM', - 'model_validation': prediction.get('model_metadata', {}), - 'lookback_days': lookback_days, - 'confidence_level': None, - 'volatility_units': 'annualized_fraction', - 'one_day_target': 'absolute_next_session_return_proxy' - } - - # Convert all numpy types to Python native types - return convert_numpy_types(result) - - except Exception as e: - logger.error(f"Error predicting volatility for {symbol}: {e}") - if ohlcv_data is None or len(ohlcv_data) < 30: - raise - # Fallback to historical volatility - return await self._fallback_historical_volatility( - symbol, ohlcv_data, lookback_days, confidence_level - ) - - async def _fallback_historical_volatility( - self, - symbol: str, - ohlcv_data: pd.DataFrame, - lookback_days: int = 365, - confidence_level: float = 0.95 - ) -> Dict[str, Any]: - """Fallback to simple historical volatility if ML model fails.""" - returns = self.data_source.calculate_returns(ohlcv_data) - - vol_30d = returns.tail(30).std() * np.sqrt(252) # Annualized - - result = { - 'symbol': symbol, - 'timestamp': datetime.now().isoformat(), - 'predictions': { - '1d': { - 'predicted_volatility': None, - 'confidence_interval': None - }, - '7d': { - 'predicted_volatility': None, - 'confidence_interval': None - }, - '30d': { - 'predicted_volatility': vol_30d, - 'confidence_interval': None - } - }, - 'model_type': 'historical_fallback', - 'lookback_days': lookback_days, - 'confidence_level': None, - 'volatility_units': 'annualized_fraction', - 'note': '30-session historical realized volatility; no validated future forecast or confidence interval' - } - - return convert_numpy_types(result) - - async def detect_market_regime( - self, - benchmark: str = "SPY", - lookback_days: int = 7300, # 20 years to capture 4-5 full market cycles (dot-com, 2008, COVID, 2022) - force_retrain: bool = False # NEW: Bypass cache age check (for scheduled training) - ) -> Dict[str, Any]: - """ - Detect current market regime (Bull/Bear/Consolidation/Distribution). - - Args: - benchmark: Market benchmark ticker (default: SPY) - lookback_days: Days of history - force_retrain: Bypass cache age check and force model retraining - - Returns: - Dict with regime prediction and probabilities - """ - try: - # Fetch multiple benchmarks for better regime detection - # Use major market indices for comprehensive view - benchmarks_to_fetch = [benchmark, "QQQ", "IWM", "DIA"] - multi_asset_data = {} - - for ticker in benchmarks_to_fetch: - try: - # Use cached version for faster loading (24h TTL) - data = await self.data_source.get_benchmark_data_cached( - benchmark=ticker, - lookback_days=lookback_days - ) - if len(data) >= 60: - multi_asset_data[ticker] = data - logger.debug(f"Fetched {len(data)} days for {ticker}") - except Exception as e: - logger.warning(f"Failed to fetch {ticker}: {e}") - - if not multi_asset_data: - raise ValueError("Could not fetch any benchmark data") - - if len(multi_asset_data) < 2: - logger.warning(f"Only {len(multi_asset_data)} benchmarks available, using single-asset mode") - - if force_retrain: - logger.info("Training regime model on explicit administrator request") - training_metadata = self.regime_detector.train_model(multi_asset_data) - logger.info(f"Regime model trained. Val accuracy: {training_metadata.get('final_val_accuracy', 'N/A')}") - elif self.regime_detector.neural_model is None: - if not self.regime_detector.load_model(): - raise RuntimeError("No saved stock regime model is available") - - # Validate after loading persisted metadata; reads never train a model. - try: - validation = self.regime_detector.training_metadata - test_accuracy = validation.get('temporal_test_accuracy') - baseline_accuracy = validation.get('baseline_test_accuracy') - if (test_accuracy is None or baseline_accuracy is None - or not np.isfinite(test_accuracy) or not np.isfinite(baseline_accuracy) - or test_accuracy <= baseline_accuracy): - raise RuntimeError('Regime model has not beaten its temporal majority-class baseline') - prediction = self.regime_detector.predict_regime( - multi_asset_data, - return_probabilities=True - ) - except Exception as predict_error: - raise RuntimeError(f"Regime model unavailable: {predict_error}") from predict_error - - # Adapt regime names for stocks - regime_id = prediction.get('predicted_regime', 1) - regime_name = self.STOCK_REGIMES.get(regime_id, "Unknown") - - # Map probabilities to stock regime names - regime_probs_raw = prediction.get('regime_probabilities', {}) - - # RegimeDetector now uses consistent stock regime names: - # 0=Bear Market, 1=Correction, 2=Bull Market, 3=Expansion - # No mapping needed - names should match directly - - # Convert probabilities dict - if isinstance(regime_probs_raw, dict): - regime_probs = {name: float(prob) for name, prob in regime_probs_raw.items()} - else: - # It's an array/list - map by index using STOCK_REGIMES - regime_probs = { - self.STOCK_REGIMES.get(i, f"State{i}"): float(prob) - for i, prob in enumerate(regime_probs_raw) - } - rule_override = prediction.get('detection_method') == 'rule_based' - validation = self.regime_detector.training_metadata - - result = { - 'current_regime': regime_name, - 'regime_id': regime_id, - 'confidence': None if rule_override else prediction.get('confidence'), - 'regime_probabilities': {} if rule_override else regime_probs, - 'model_type': 'rule_based_override' if rule_override else 'causal_neural', - 'model_validation': { - 'trained_at': validation.get('trained_at'), - 'split_method': validation.get('split_method'), - 'temporal_test_accuracy': validation.get('temporal_test_accuracy'), - 'baseline_test_accuracy': validation.get('baseline_test_accuracy'), - }, - 'benchmark': benchmark, - 'timestamp': datetime.now().isoformat(), - 'characteristics': self._get_regime_characteristics(regime_name) - } - - # Convert all numpy types to Python native types - return convert_numpy_types(result) - - except Exception as e: - logger.error(f"Error detecting market regime: {e}") - # Fallback to simple trend analysis - # Use benchmark data if available, otherwise fetch it - benchmark_data = multi_asset_data.get(benchmark) if 'multi_asset_data' in locals() else None - if benchmark_data is None: - try: - benchmark_data = await self.data_source.get_benchmark_data( - benchmark=benchmark, - lookback_days=lookback_days - ) - except Exception as e: - logger.warning(f"Failed to fetch benchmark data for {benchmark}: {e}") - benchmark_data = pd.DataFrame() - return await self._fallback_regime_detection(benchmark, benchmark_data) + async def predict_volatility(self, symbol: str, lookback_days: int = 365, confidence_level: float = .95) -> Dict[str, Any]: + from services.ml.reliability import capability_service + from api.schemas.ml_contract import ModelType, Horizon + from services.ml.portfolio_context import stock_symbol + exact_asset = stock_symbol(symbol) + predictions = {} + for h in (Horizon.D1, Horizon.D7, Horizon.D30): + result = await capability_service.result(exact_asset, "stocks", ModelType.VOLATILITY, h) + predictions[h.value] = {**result.model_dump(mode="json"), "predicted_volatility": result.value, "confidence_interval": None if result.uncertainty is None else result.uncertainty.model_dump()} + return dict(symbol=symbol, timestamp=datetime.now().isoformat(), predictions=predictions, + model_type="verified_daily_risk", lookback_days=lookback_days, confidence_level=None, + note="No training during reads. Intervals require independent 90% coverage validation.") + + async def detect_market_regime(self, benchmark: str = "SPY", lookback_days: int = 7300, force_retrain: bool = False) -> Dict[str, Any]: + from services.ml.reliability import capability_service + from api.schemas.ml_contract import ModelType + from services.ml.portfolio_context import stock_symbol + result = await capability_service.result(stock_symbol(benchmark), "stocks", ModelType.REGIME) + return dict(current_regime=result.value or "Unavailable", regime_id=next((i for i,n in self.STOCK_REGIMES.items() if n==result.value), None), + confidence=None, regime_probabilities={}, benchmark=benchmark, + timestamp=result.data_as_of.isoformat() if result.data_as_of else None, + characteristics={"method": result.provenance.method or "unavailable"}, + model_type="descriptive_rules", note=result.reason, availability=result.availability.value) def _get_regime_characteristics(self, regime_name: str) -> Dict[str, str]: """Get characteristics for a given regime.""" @@ -372,156 +157,15 @@ def _get_regime_characteristics(self, regime_name: str) -> Dict[str, str]: } return characteristics.get(regime_name, {"trend": "unknown", "volatility": "unknown", "sentiment": "unknown"}) - async def _fallback_regime_detection( - self, - symbol: str, - ohlcv_data: pd.DataFrame - ) -> Dict[str, Any]: - """Fallback regime detection using simple trend analysis.""" - if len(ohlcv_data) < 60: - return { - 'current_regime': 'Unknown', - 'confidence': None, - 'regime_probabilities': {}, - 'benchmark': symbol, - 'timestamp': datetime.now().isoformat(), - 'characteristics': self._get_regime_characteristics('Unknown'), - 'model_type': 'moving_average_fallback', - 'note': 'Insufficient data for regime detection' - } - - # Calculate moving averages - close_prices = ohlcv_data['close'] - ma_50 = close_prices.rolling(50).mean() - ma_200 = close_prices.rolling(200).mean() if len(close_prices) >= 200 else ma_50 - - current_price = close_prices.iloc[-1] - current_ma50 = ma_50.iloc[-1] - current_ma200 = ma_200.iloc[-1] - - # Simple regime classification (matching RegimeDetector names) - if current_price > current_ma50 > current_ma200: - regime = "Bull Market" - elif current_price < current_ma50 < current_ma200: - regime = "Bear Market" - elif abs(current_price - current_ma50) / current_ma50 < 0.02: - regime = "Correction" # Sideways/consolidation - else: - regime = "Correction" # Default to Correction for uncertain cases - - result = { - 'current_regime': regime, - 'confidence': None, - 'regime_probabilities': {}, - 'benchmark': symbol, - 'timestamp': datetime.now().isoformat(), - 'characteristics': self._get_regime_characteristics(regime), - 'model_type': 'moving_average_fallback', - 'note': 'Using MA-based regime detection (ML model unavailable)' - } - - return convert_numpy_types(result) - - async def forecast_correlations( - self, - symbols: List[str], - lookback_days: int = 365, - horizons: List[int] = [1, 7, 30], - train_if_missing: bool = False - ) -> Dict[str, Any]: - """ - Forecast correlations between multiple stocks. - - Args: - symbols: List of stock tickers - lookback_days: Days of history - horizons: Forecast horizons in days - - Returns: - Dict with correlation matrices for each horizon - """ - multi_asset_data = {} - try: - # Fetch multi-asset data - multi_asset_data = await self.data_source.get_multi_asset_data( - symbols=symbols, - lookback_days=lookback_days - ) - - if len(multi_asset_data) < 2: - raise ValueError("Need at least 2 stocks for correlation analysis") - - if not self.correlation_forecaster.models: - self.correlation_forecaster.load_models() - if train_if_missing: - logger.info("Training correlation forecaster...") - training_metadata = self.correlation_forecaster.train_model( - multi_asset_data, - validation_split=0.2 - ) - logger.info(f"Correlation model trained. Metrics: {training_metadata.get('metrics', {})}") - if not self.correlation_forecaster.models: - return await self._fallback_historical_correlations(symbols, multi_asset_data, horizons) - - # Predict correlations - predictions = self.correlation_forecaster.predict_correlations( - multi_asset_data, - horizons=horizons - ) - - result = { - 'symbols': list(multi_asset_data.keys()), - 'predictions': predictions.get('predictions', {}), - 'timestamp': datetime.now().isoformat(), - 'horizons': horizons, - 'model_type': 'Transformer' - } - - return convert_numpy_types(result) - - except Exception as e: - logger.error(f"Error forecasting correlations: {e}") - if len(multi_asset_data) < 2: - raise - # Fallback to historical correlations - return await self._fallback_historical_correlations(symbols, multi_asset_data, horizons) - - async def _fallback_historical_correlations( - self, - symbols: List[str], - multi_asset_data: Dict[str, pd.DataFrame], - horizons: List[int] - ) -> Dict[str, Any]: - """Fallback to historical correlation if ML model fails.""" - returns_df = self.data_source.get_multi_asset_returns(multi_asset_data) - - if len(returns_df) < 30: - return { - 'symbols': symbols, - 'predictions': {}, - 'note': 'Insufficient data for correlation analysis' - } - - # Calculate historical correlation - corr_matrix = returns_df.corr() - - # Same correlation for all horizons (simple fallback) - predictions = {} - for horizon in horizons: - predictions[f'{horizon}d'] = { - 'correlation_matrix': corr_matrix.to_dict(), - 'avg_correlation': corr_matrix.values[np.triu_indices_from(corr_matrix.values, k=1)].mean() - } - - result = { - 'symbols': list(multi_asset_data.keys()), - 'predictions': predictions, - 'timestamp': datetime.now().isoformat(), - 'model_type': 'historical_fallback', - 'note': 'Using historical correlation (ML model unavailable)' - } - - return convert_numpy_types(result) + async def forecast_correlations(self, symbols: List[str], lookback_days: int = 365, horizons: List[int] = None) -> Dict[str, Any]: + data = await self.data_source.get_multi_asset_data(symbols, lookback_days) + returns = self.data_source.get_multi_asset_returns(data) + valid = len(returns) >= 30 and len(returns.columns) >= 2 + return dict(symbols=list(data), predictions={}, timestamp=returns.index[-1].isoformat() if valid else None, + horizons=horizons or [7,30], model_type="historical_descriptive", + historical_correlation=returns.corr().to_dict() if valid else None, + availability="Partial" if valid else "Unavailable", + note="Historical correlations are descriptive; Transformer forecasts remain experimental. No training during reads.") async def generate_signals( self, @@ -549,8 +193,11 @@ async def generate_signals( # Generate technical features features_df = self.feature_engineer.create_feature_set(ohlcv_data, symbol=symbol) - # Get latest features + # Un indicateur absent ne devient pas une observation neutre. + required = ['rsi_14', 'macd', 'macd_signal', 'bb_position'] latest_features = features_df.iloc[-1] + if not all(name in latest_features and np.isfinite(latest_features[name]) for name in required): + raise ValueError('Required technical observations are unavailable') # Calculate signal components rsi_signal = self._rsi_to_signal(latest_features.get('rsi_14', 50)) @@ -573,7 +220,10 @@ async def generate_signals( 'symbol': symbol, 'timestamp': datetime.now().isoformat(), 'overall_signal': overall_signal, - 'confidence': 0.75, + 'confidence': None, + 'availability': 'Available', 'nature': 'diagnostic', + 'reason': 'Weighted technical rules; confidence and future price direction have not been validated', + 'data_as_of': ohlcv_data.index[-1].isoformat(), 'provider': 'Yahoo Finance', 'signals': { 'rsi': {'value': rsi_signal, 'weight': 0.4}, 'macd': {'value': macd_signal, 'weight': 0.3}, @@ -595,8 +245,10 @@ async def generate_signals( result = { 'symbol': symbol, 'error': str(e), - 'overall_signal': 0.0, - 'confidence': 0.0 + 'overall_signal': None, + 'confidence': None, 'availability': 'Unavailable', 'nature': 'diagnostic', + 'reason': 'Technical observations are unavailable', 'signals': {}, + 'recommendation': 'Unavailable', 'technical_indicators': {} } return convert_numpy_types(result) diff --git a/services/ml/cycle_diagnostics.py b/services/ml/cycle_diagnostics.py new file mode 100644 index 00000000..86d82490 --- /dev/null +++ b/services/ml/cycle_diagnostics.py @@ -0,0 +1,105 @@ +"""Descriptive Bitcoin cycle statistics using verified closes only.""" + +import hashlib +import json +import numpy as np +import pandas as pd +from pathlib import Path + +HALVINGS = ("2012-11-28", "2016-07-09", "2020-05-11", "2024-04-20") + + +def describe_cycles(close, receipt): + cycles = [] + for number, halving in enumerate(HALVINGS, 1): + start = pd.Timestamp(halving, tz="UTC") + end = ( + pd.Timestamp(HALVINGS[number], tz="UTC") + if number < len(HALVINGS) + else close.index[-1] + pd.Timedelta(days=1) + ) + if start > close.index[-1]: + continue + values = close[(close.index >= start) & (close.index < end)] + if values.empty: + cycles.append({"cycle": number, "halving": halving, "anchor_available": False, + "start": None, "end": None, "peak_date": None, + "days_to_observed_peak": None, "observed_peak": None, + "maximum_drawdown": None, "return_since_halving": None, + "complete_cycle": False, "coverage": "Unavailable", "points": []}) + continue + complete_anchor = values.index[0] == start + running_peak = values.cummax() + drawdowns = values / running_peak - 1 + peak_date = values.idxmax() + cycles.append( + { + "cycle": number, + "halving": halving, + "anchor_available": bool(complete_anchor), + "start": values.index[0].date().isoformat(), + "end": values.index[-1].date().isoformat(), + "peak_date": peak_date.date().isoformat(), + "days_to_observed_peak": int((peak_date - start).days), + "observed_peak": float(values.max()), + "halving_close": float(values.iloc[0]) if complete_anchor else None, + "latest_close": float(values.iloc[-1]), + "drawdown_from_peak": float(values.iloc[-1]/values.max()-1), + "lowest_after_peak": float(values.loc[peak_date:].min()), + "lowest_after_peak_date": values.loc[peak_date:].idxmin().date().isoformat(), + "days_since_observed_peak": int((values.index[-1]-peak_date).days), + "provider": receipt['provider'], + "dataset_id": receipt['dataset_id'], + "quote_unit": receipt.get('quote_unit','USDT'), + "maximum_drawdown": float(drawdowns.min()), + "return_since_halving": float(values.iloc[-1] / values.iloc[0] - 1) + if complete_anchor + else None, + "complete_cycle": bool(complete_anchor and number < len(HALVINGS) and close.index[-1] >= end), + "coverage": "Complete" if complete_anchor and number < len(HALVINGS) and close.index[-1] >= end else "Ongoing" if number == len(HALVINGS) else "Partial", + "points": [ + { + "day": int((date - start).days), + "normalized": float(value / values.iloc[0]) if complete_anchor else None, + "drawdown": float(drawdowns.loc[date]), + } + for date, value in values.items() + ], + } + ) + as_of = close.index[-1] + return { + "availability": "Available" if cycles else "Unavailable", + "nature": "diagnostic", + "reason": "Observed daily closes; retrospective cycle peaks are not real-time forecasts. Earlier cycles may lack a halving close.", + "data_as_of": as_of.date().isoformat(), + "provider": receipt["provider"], + "dataset_id": receipt["dataset_id"], + "days_since_halving": int((as_of - pd.Timestamp(HALVINGS[-1], tz="UTC")).days), + "cycles": cycles, + } + + +def historical_cycle_comparison(root, current_close, current_receipt): + """Use one price basis per cycle. Never splice different vendors within a cycle.""" + current = describe_cycles(current_close,current_receipt) + directory = Path(root)/'data/ml_verified/cycles' + metadata = directory/'BTC.json' + if not metadata.exists(): + return current + receipt = json.loads(metadata.read_text(encoding='utf-8')) + path = (directory/receipt['file']).resolve() + if not path.is_relative_to(directory.resolve()) or hashlib.sha256(path.read_bytes()).hexdigest()!=receipt['sha256']: + raise ValueError('Historical cycle reference receipt is invalid') + frame = pd.read_csv(path) + reference = pd.Series(frame.close.to_numpy(),index=pd.DatetimeIndex(pd.to_datetime(frame.date,utc=True))) + if reference.index.has_duplicates or not reference.index.is_monotonic_increasing or not np.isfinite(reference).all() or (reference<=0).any() or not ((reference.index[1:]-reference.index[:-1]).days==1).all(): + raise ValueError('Historical cycle reference series is invalid') + receipt['quote_unit']='USD reference rate' + older=describe_cycles(reference,receipt) + current['cycles']=[c for c in older['cycles'] if c['cycle']<4]+[c for c in current['cycles'] if c['cycle']==4] + current['provider']='Coin Metrics Community (completed cycles); Binance Spot (current cycle)' + current['dataset_id']=receipt['dataset_id']+'; '+current_receipt['dataset_id'] + current['reason']='Each cycle uses one observed price series: completed cycles use Coin Metrics USD reference rates; cycle 4 uses Binance USDT closes. No mid-cycle vendor splice. Historical peaks and lows are retrospective; current extrema are only observed so far. No future peak or bottom forecast.' + current['attribution']=receipt.get('attribution') + return current diff --git a/services/ml/live_observations.py b/services/ml/live_observations.py new file mode 100644 index 00000000..cda4b362 --- /dev/null +++ b/services/ml/live_observations.py @@ -0,0 +1,284 @@ +"""Append-only public observations; immutable evaluation datasets are never rewritten.""" + +import hashlib +import io +import json +import os +import time +from pathlib import Path +from functools import lru_cache +from uuid import uuid4 + +import httpx +import numpy as np +import pandas as pd +from filelock import FileLock + + +_last_attempt = {} + + +def extend_crypto(root, asset, close, receipt, now): + if receipt.get("provider") != "binance_spot_public_market_data": + return close, receipt + directory = Path(root) / "cache/ml_observations/crypto" + directory.mkdir(parents=True, exist_ok=True) + end = pd.Timestamp(now).normalize() + with FileLock(str(directory / (asset + ".lock")), timeout=30): + metadata = directory / (asset + ".json") + frame = pd.DataFrame({"date": close.index.strftime("%Y-%m-%d"), "close": close.to_numpy()}) + current = dict(receipt) + if metadata.exists(): + cached = json.loads(metadata.read_text()) + path = (directory / cached["file"]).resolve() + if not path.is_relative_to(directory.resolve()): + raise ValueError("Unsafe observation receipt path") + content = path.read_bytes() + if hashlib.sha256(content).hexdigest() != cached["sha256"]: + raise ValueError("Observation receipt hash mismatch") + if cached["evaluation_sha256"] == receipt["sha256"]: + candidate = pd.read_csv(io.BytesIO(content), float_precision="round_trip") + prefix = candidate.iloc[: len(frame)] + if not prefix.equals(frame): + raise ValueError("Observed extension changes the evaluated historical prefix") + frame, current = candidate, cached + existing = _series(frame) + if existing.index[-1] >= end: + raise ValueError("Observation extension includes incomplete closes") + start = pd.Timestamp(frame.date.iloc[-1], tz="UTC") + pd.Timedelta(days=1) + if start < end: + # Bounded pagination: an abandoned cache must not trigger unlimited HTTP work. + if (end - start).days > 1000: + raise ValueError( + "Observation extension exceeds 1000 days; explicit acquisition is required" + ) + key = (str(directory.resolve()), asset, receipt["sha256"], end.isoformat()) + if time.monotonic() - _last_attempt.get(key, -float("inf")) < 60: + return _series(frame), current + _last_attempt[key] = time.monotonic() + try: + response = httpx.get( + "https://data-api.binance.vision/api/v3/klines", + params={ + "symbol": asset + "USDT", + "interval": "1d", + "startTime": int(start.timestamp() * 1000), + "endTime": int(end.timestamp() * 1000) - 1, + "limit": 1000, + }, + timeout=8, + ) + response.raise_for_status() + except httpx.HTTPError: + # Preserve the actual last observation; the caller still enforces freshness. + return _series(frame), current + rows = response.json() + additions = pd.DataFrame( + [ + { + "date": pd.Timestamp(r[0], unit="ms", tz="UTC").strftime("%Y-%m-%d"), + "close": float(r[4]), + } + for r in rows + if r[6] < int(end.timestamp() * 1000) + ] + ) + if additions.empty: + return _series(frame), current + extension = _series(additions) + expected = pd.date_range(start, end - pd.Timedelta(days=1), freq="D") + if ( + not extension.index.equals(expected) + or not np.isfinite(extension).all() + or (extension <= 0).any() + ): + raise ValueError("Provider returned incomplete or invalid daily observations") + frame = pd.concat([frame, additions], ignore_index=True) + content = frame.to_csv(index=False).encode() + sha = hashlib.sha256(content).hexdigest() + filename = asset + "-" + sha[:16] + ".csv" + (directory / filename).write_bytes(content) + raw_sha = hashlib.sha256(response.content).hexdigest() + raw_file = asset + "-vendor-" + raw_sha[:16] + ".json" + (directory / raw_file).write_bytes(response.content) + current = { + **receipt, + "file": filename, + "sha256": sha, + "dataset_id": f"verified-live-crypto-{asset}-{sha[:16]}", + "evaluation_dataset_id": receipt["dataset_id"], + "evaluation_sha256": receipt["sha256"], + "fetched_at": pd.Timestamp(now).isoformat(), + "raw_response_sha256": raw_sha, + "raw_response_file": raw_file, + } + temporary = metadata.with_suffix("." + uuid4().hex + ".tmp") + temporary.write_text(json.dumps(current, indent=2)) + os.replace(temporary, metadata) + result = _series(frame) + if ( + result.index.has_duplicates + or not result.index.is_monotonic_increasing + or not ((result.index[1:] - result.index[:-1]).days == 1).all() + ): + raise ValueError("Observation extension is not consecutive daily history") + if not np.isfinite(result).all() or (result <= 0).any() or result.index[-1] >= end: + raise ValueError("Observation extension contains invalid or incomplete closes") + return result, current + + +def _series(frame): + result = pd.Series( + frame.close.to_numpy(dtype=float), + index=pd.DatetimeIndex(pd.to_datetime(frame.date, utc=True)), + name="close", + ) + if ( + result.empty + or result.index.has_duplicates + or not result.index.is_monotonic_increasing + or not ((result.index[1:] - result.index[:-1]).days == 1).all() + or not np.isfinite(result).all() + or (result <= 0).any() + ): + raise ValueError("Provider returned incomplete or invalid daily observations") + return result + + +@lru_cache(maxsize=64) +def completed_stock_sessions(calendar_name, start_date, end_date): + """Cache only the public schedule, keyed by venue and exact date bounds. + + No prices, portfolio, validation or predictions are cached here. A new UTC + day uses a different key; holidays and session membership remain unchanged. + """ + import exchange_calendars as xcals + + calendar = xcals.get_calendar(calendar_name, start=start_date, end=end_date) + sessions = pd.DatetimeIndex(calendar.sessions) + if sessions.tz is None: + sessions = sessions.tz_localize("UTC") + return sessions[sessions < pd.Timestamp(end_date, tz="UTC")] + + +def extend_stocks(root, asset, close, receipt, now): + """Append complete exchange sessions, rejecting vendor adjustment revisions.""" + if receipt.get("provider") != "Yahoo Finance via yfinance": + return close, receipt + import yfinance as yf + + directory = Path(root) / "cache/ml_observations/stocks" + directory.mkdir(parents=True, exist_ok=True) + end = pd.Timestamp(now).normalize() + sessions = completed_stock_sessions( + receipt["exchange_calendar"], close.index[0].date(), end.date() + ) + with FileLock(str(directory / (asset + ".lock")), timeout=30): + metadata = directory / (asset + ".json") + frame = pd.DataFrame({"date": close.index.strftime("%Y-%m-%d"), "close": close.to_numpy()}) + current = dict(receipt) + if metadata.exists(): + cached = json.loads(metadata.read_text()) + path = (directory / cached["file"]).resolve() + if ( + not path.is_relative_to(directory.resolve()) + or hashlib.sha256(path.read_bytes()).hexdigest() != cached["sha256"] + ): + raise ValueError("Invalid stock observation receipt") + if cached["evaluation_sha256"] == receipt["sha256"]: + candidate = pd.read_csv(path, float_precision="round_trip") + if not candidate.iloc[: len(frame)].equals(frame): + raise ValueError("Stock extension changes the evaluated historical prefix") + frame, current = candidate, cached + series = pd.Series( + frame.close.to_numpy(), + index=pd.DatetimeIndex(pd.to_datetime(frame.date, utc=True)), + name="close", + ) + if ( + not series.index.equals(sessions[sessions <= series.index[-1]]) + or not np.isfinite(series).all() + or (series <= 0).any() + ): + raise ValueError("Stock extension is not complete valid exchange sessions") + expected = sessions[sessions > series.index[-1]] + if expected.empty: + return series, current + key = (str(directory.resolve()), asset, receipt["sha256"], end.isoformat()) + if time.monotonic() - _last_attempt.get(key, -float("inf")) < 60: + return series, current + _last_attempt[key] = time.monotonic() + try: + data = yf.download( + asset, + start=(series.index[-1] - pd.Timedelta(days=14)).strftime("%Y-%m-%d"), + end=end.strftime("%Y-%m-%d"), + auto_adjust=True, + actions=True, + progress=False, + threads=False, + timeout=8, + ) + except Exception: + return series, current + if data.empty: + return series, current + if isinstance(data.columns, pd.MultiIndex): + data.columns = data.columns.droplevel(1) + downloaded = pd.Series( + data["Close"].to_numpy(dtype=float), + index=pd.DatetimeIndex(pd.to_datetime(data.index, utc=True)).normalize(), + name="close", + ) + overlap = downloaded.index.intersection(series.index) + if overlap.empty or not np.allclose( + downloaded.loc[overlap], series.loc[overlap], rtol=1e-10, atol=1e-8, equal_nan=False + ): + raise ValueError( + "Vendor revised adjusted historical prices; explicit dataset acquisition and reevaluation are required" + ) + additions = downloaded[downloaded.index > series.index[-1]] + if ( + not additions.index.equals(expected) + or not np.isfinite(additions).all() + or (additions <= 0).any() + ): + raise ValueError("Provider returned missing or invalid completed exchange sessions") + frame = pd.concat( + [ + frame, + pd.DataFrame( + {"date": additions.index.strftime("%Y-%m-%d"), "close": additions.to_numpy()} + ), + ], + ignore_index=True, + ) + content = frame.to_csv(index=False).encode() + sha = hashlib.sha256(content).hexdigest() + filename = asset + "-" + sha[:16] + ".csv" + (directory / filename).write_bytes(content) + # Preserve the SDK's returned observation evidence as a separate public receipt. + raw = downloaded.to_csv().encode() + raw_sha = hashlib.sha256(raw).hexdigest() + raw_file = asset + "-vendor-" + raw_sha[:16] + ".csv" + (directory / raw_file).write_bytes(raw) + current = { + **receipt, + "file": filename, + "sha256": sha, + "dataset_id": f"verified-live-stocks-{asset}-{sha[:16]}", + "evaluation_dataset_id": receipt["dataset_id"], + "evaluation_sha256": receipt["sha256"], + "fetched_at": pd.Timestamp(now).isoformat(), + "raw_response_sha256": raw_sha, + "raw_response_file": raw_file, + "yfinance_version": yf.__version__, + } + temporary = metadata.with_suffix("." + uuid4().hex + ".tmp") + temporary.write_text(json.dumps(current, indent=2)) + os.replace(temporary, metadata) + return pd.Series( + frame.close.to_numpy(), + index=pd.DatetimeIndex(pd.to_datetime(frame.date, utc=True)), + name="close", + ), current diff --git a/services/ml/model_registry.py b/services/ml/model_registry.py index c7bb0fd8..8537806d 100644 --- a/services/ml/model_registry.py +++ b/services/ml/model_registry.py @@ -26,6 +26,8 @@ class ModelStatus(str, Enum): """Possible model states.""" TRAINING = "training" TRAINED = "trained" + EXPERIMENTAL = "experimental" + REJECTED = "rejected" VALIDATED = "validated" DEPLOYED = "deployed" DEPRECATED = "deprecated" @@ -65,6 +67,10 @@ class ModelManifest: tags: Optional[List[str]] = None description: Optional[str] = None author: Optional[str] = None + availability: str = "Experimental" + validation_state: str = "not_evaluable" + validation_reason: str = "Legacy training metrics have no causal confirmation evidence" + legacy_metrics: Optional[Dict[str, Any]] = None def to_dict(self) -> Dict[str, Any]: """Convertir en dictionnaire pour sérialisation""" @@ -73,6 +79,10 @@ def to_dict(self) -> Dict[str, Any]: for date_field in ['created_at', 'updated_at']: if result.get(date_field): result[date_field] = result[date_field].isoformat() + if self.validation_state != "retrospectively_validated": + result["status"] = ModelStatus.EXPERIMENTAL if self.status not in (ModelStatus.REJECTED, ModelStatus.DEPRECATED, ModelStatus.FAILED) else self.status + result["validation_metrics"] = None + result["test_metrics"] = None return result @classmethod @@ -83,6 +93,8 @@ def from_dict(cls, data: Dict[str, Any]) -> 'ModelManifest': if data.get(date_field): data[date_field] = datetime.fromisoformat(data[date_field]) + if data.get("validation_state") != "retrospectively_validated": + data["legacy_metrics"] = data.get("legacy_metrics") or {"validation": data.get("validation_metrics"), "test": data.get("test_metrics")} return cls(**data) diff --git a/services/ml/models/btc_regime_detector.py b/services/ml/models/btc_regime_detector.py index 4f4c0f61..b172847e 100644 --- a/services/ml/models/btc_regime_detector.py +++ b/services/ml/models/btc_regime_detector.py @@ -278,6 +278,12 @@ def load_model(self, filename: str): return False data = safe_pickle_load(model_path) + columns = data.get("feature_columns") + if not isinstance(columns, list) or not columns or any("future" in c or "target" in c for c in columns): + raise ValueError("HMM feature schema is incompatible") + if getattr(data.get("scaler"), "n_features_in_", None) != len(columns) or getattr(data.get("hmm_model"), "n_features", None) != len(columns): + raise ValueError("HMM scaler/state feature dimensions are incompatible") + self._loaded_path = model_path self.hmm_model = data['hmm_model'] self.scaler = data['scaler'] self.feature_columns = data['feature_columns'] @@ -313,12 +319,11 @@ async def predict_regime( raise ValueError("No features available for prediction") # Load symbol-specific HMM model (BTC and ETH have different distributions) - if self.hmm_model is None: - model_file = f"{symbol.lower()}_regime_hmm.pkl" + model_file = f"{symbol.lower()}_regime_hmm.pkl" + if self.hmm_model is None or getattr(self, "_loaded_path", None) != self.models_dir / model_file: model_loaded = self.load_model(model_file) if not model_loaded: - logger.info(f"No trained model found for {symbol}, training HMM...") - await self.train_hmm(symbol, lookback_days) + raise ValueError(f"No compatible HMM artifact for {symbol}; explicit administrator training is required") # Normalize features features_scaled = self.scaler.transform(features_df[self.feature_columns]) @@ -326,7 +331,7 @@ async def predict_regime( # HMM prediction (baseline) regime_sequence = self.hmm_model.predict(features_scaled) regime_id = int(regime_sequence[-1]) - regime_name = self.regime_names[regime_id] + regime_name = f"State {chr(65 + regime_id)}" # Get regime probabilities probabilities = self.hmm_model.predict_proba(features_scaled) @@ -334,14 +339,13 @@ async def predict_regime( # CRITICAL: Apply Bayesian prior to avoid dangerous overconfidence # Prevents 100% confidence that misleads users - model can't predict black swans # Same as regime_detector.py:955-960 (Neural Network version) - min_uncertainty = 0.15 # Force at least 15% total uncertainty - uniform_prior = np.ones(self.num_regimes) / self.num_regimes # [0.25, 0.25, 0.25, 0.25] - probabilities_calibrated = (1 - min_uncertainty) * probabilities + min_uncertainty * uniform_prior + probabilities_calibrated = probabilities # Posterior state probabilities, not empirically calibrated forecasts # Use calibrated probabilities for confidence regime_confidence = float(probabilities_calibrated[-1, regime_id]) - regime_info = self.regime_descriptions[regime_id] + regime_info = {"state": regime_name, "economic_mapping_verified": False, + "learned_feature_means": dict(zip(self.feature_columns, self.scaler.inverse_transform(self.hmm_model.means_)[regime_id].tolist()))} # Baseline HMM result hmm_result = { @@ -350,6 +354,7 @@ async def predict_regime( 'confidence': regime_confidence, 'regime_info': regime_info, 'prediction_date': datetime.now().isoformat(), + 'data_as_of': features_df.index[-1].isoformat(), 'model_metadata': { 'trained_at': self.training_metadata.get('trained_at'), 'features_used': len(self.feature_columns) @@ -367,7 +372,7 @@ async def predict_regime( if fused.get('method') == 'rule_based': result['predicted_regime'] = fused['regime_id'] result['regime_name'] = fused['regime_name'] - result['confidence'] = fused['confidence'] + result['confidence'] = None result['regime_info'] = self.regime_descriptions[fused['regime_id']] result['detection_method'] = 'rule_based' result['rule_reason'] = fused['reason'] @@ -378,11 +383,18 @@ async def predict_regime( if return_probabilities: # Use calibrated probabilities (with Bayesian prior applied) result['regime_probabilities'] = { - self.regime_names[i]: float(probabilities_calibrated[-1, i]) + f'State {chr(65 + i)}': float(probabilities_calibrated[-1, i]) for i in range(self.num_regimes) } - logger.info(f"Regime: {result['regime_name']} (confidence={result['confidence']:.3f}, method={result['detection_method']})") + result["nature"] = "diagnostic" + result["availability"] = "Experimental" + result["probability_kind"] = "latent_state_posterior_not_future_direction" + result["hmm_state"] = f"State {chr(65 + regime_id)}" + result["hmm_state_features"] = {f"State {chr(65+i)}": dict(zip(self.feature_columns, self.scaler.inverse_transform(self.hmm_model.means_)[i].tolist())) for i in range(self.num_regimes)} + result["economic_mapping_verified"] = False + result["rule_diagnostic"] = {k:v for k,v in (rule_based_result or {}).items() if k != "rule_priority"} + logger.info(f"Regime diagnostic: {result['regime_name']}") return result @@ -417,7 +429,7 @@ def _detect_regime_rule_based(self, features: pd.DataFrame) -> Optional[Dict[str return { 'regime_id': 0, 'regime_name': 'Bear Market', - 'confidence': min(0.95, 0.85 + abs(drawdown) * 0.2), + 'rule_priority': min(0.95, 0.85 + abs(drawdown) * 0.2), 'method': 'rule_based', 'reason': f'Drawdown {drawdown:.1%} sustained {int(days_since_peak)} days, trend {trend_30d:.1%}' } @@ -429,7 +441,7 @@ def _detect_regime_rule_based(self, features: pd.DataFrame) -> Optional[Dict[str return { 'regime_id': 3, 'regime_name': 'Expansion', - 'confidence': 0.90, + 'rule_priority': 0.90, 'method': 'rule_based', 'reason': f'Recovery from {lookback_dd:.1%} at +{trend_30d:.1%}/30d' } @@ -439,7 +451,7 @@ def _detect_regime_rule_based(self, features: pd.DataFrame) -> Optional[Dict[str return { 'regime_id': 2, 'regime_name': 'Bull Market', - 'confidence': 0.88, + 'rule_priority': 0.88, 'method': 'rule_based', 'reason': f'Stable uptrend: DD={drawdown:.1%}, vol={volatility:.1%}' } @@ -449,20 +461,20 @@ def _detect_regime_rule_based(self, features: pd.DataFrame) -> Optional[Dict[str return { 'regime_id': 2, 'regime_name': 'Bull Market', - 'confidence': 0.85, + 'rule_priority': 0.85, 'method': 'rule_based', 'reason': f'Recovery uptrend: DD={drawdown:.1%}, trend={trend_30d:.1%}' } # Rule 5: CORRECTION - elevated volatility OR deep DD with flat trend if (drawdown < -0.10 and volatility > 0.65) or (drawdown < -0.20 and abs(trend_30d) < 0.10): - confidence = 0.85 + rule_priority = 0.85 if drawdown < -0.20: - confidence = 0.90 + rule_priority = 0.90 return { 'regime_id': 1, 'regime_name': 'Correction', - 'confidence': confidence, + 'rule_priority': rule_priority, 'method': 'rule_based', 'reason': f'Drawdown {drawdown:.1%} + Elevated volatility {volatility:.1%}' } @@ -485,7 +497,7 @@ def _fuse_predictions(self, rule_based: Optional[Dict], hmm_result: Dict) -> Dic Returns: Fused prediction with method indicator """ - if rule_based and rule_based['confidence'] >= 0.85: + if rule_based and rule_based['rule_priority'] >= 0.85: # High confidence rule-based → override HMM return rule_based else: diff --git a/services/ml/orchestrator.py b/services/ml/orchestrator.py index c8c59e8e..b1fb4043 100644 --- a/services/ml/orchestrator.py +++ b/services/ml/orchestrator.py @@ -874,7 +874,7 @@ async def load_regime_model(self) -> Dict[str, Any]: """Load regime detection model for auto-startup""" try: from services.ml_pipeline_manager_optimized import optimized_pipeline_manager as pipeline_manager - await pipeline_manager.load_regime_model() + pipeline_manager.load_regime_model() self.model_status['regime'] = 'ready' logger.info("Regime model loaded successfully") return {"success": True, "message": "Regime model loaded"} diff --git a/services/ml/portfolio_context.py b/services/ml/portfolio_context.py new file mode 100644 index 00000000..a4e8e093 --- /dev/null +++ b/services/ml/portfolio_context.py @@ -0,0 +1,93 @@ +"""Read the authenticated selection; never substitute a benchmark for a holding.""" +from __future__ import annotations + +import hashlib +import json +import os +from datetime import datetime, timezone +from pathlib import Path + +from services.ml.reliability import ROOT, valid_symbol + + +def stock_symbol(symbol: str) -> str: + """Explicit Saxo venue to vendor suffix mapping, preserving instrument identity.""" + base, _, venue = symbol.partition(":") + suffixes = {"xnas": "", "xnys": "", "arcx": "", "xswx": ".SW", "xvtx": ".SW", "xams": ".AS", "xetr": ".DE", "xlon": ".L", "xmil": ".MI"} + if venue and venue.lower() not in suffixes: + raise ValueError("The instrument venue has no verified ticker mapping") + from services.ml.bourse.currency_detector import CurrencyExchangeDetector + hints={'xnas':'NASDAQ','xnys':'NYSE','arcx':'NYSE','xswx':'SWX','xvtx':'SWX','xams':'AMS','xetr':'XETRA','xlon':'LSE','xmil':'MIL'} + # The known Yahoo WRDUSW line is CHF. Do not treat a declared USD listing + # as the same quote series when no corresponding USD line is verified. + if base.upper() in ('WRDUSW_USD','WRDUSW_USD.SW'): + raise ValueError('The exact USD listing has no verified provider mapping') + mapped, _, _ = CurrencyExchangeDetector().detect_currency_and_exchange("BRKb" if base.lower()=="brkb" else base.upper(), exchange_hint=hints.get(venue.lower())) + return valid_symbol(mapped) + + + +async def read_context(user: str, source: str, market: str, file_key: str | None = None) -> dict: + # Cette capture privée sert uniquement à la prévisualisation locale. Elle + # n'est jamais incluse dans le paquet de livraison ni utilisée en production. + snapshot_path = os.getenv("ML_PORTFOLIO_SNAPSHOT") + if snapshot_path: + if os.getenv("ENVIRONMENT", "").lower() == "production": + raise ValueError("A private preview capture cannot be used in production") + capture = json.loads(Path(snapshot_path).read_text(encoding="utf-8")) + entry = capture.get(market, {}) + if capture.get("user_id") != user or entry.get("source") != source or (file_key and file_key != entry.get("file_key")): + raise ValueError("No captured portfolio matches this authenticated user and selected source") + return {**entry, "observation_mode": "dated_read_only_production_copy", "captured_at": capture["observed_at"]} + if market == "stocks" and source in ("saxobank", "saxobank_csv"): + from adapters.saxo_adapter import _load_snapshot + config_path = ROOT / 'data/users' / user / 'config.json' + config = json.loads(config_path.read_text(encoding='utf-8')) if config_path.is_file() else {} + effective_file = file_key or config.get('sources', {}).get('bourse', {}).get('selected_csv_file') + if not effective_file: + raise ValueError("No selected Saxo CSV is available for this user") + path = ROOT / 'data/users' / user / 'saxobank/data' / effective_file + if Path(effective_file).name != effective_file or path.suffix != '.csv' or not path.is_file(): + raise ValueError('The selected Saxo CSV is missing or invalid; no different file is substituted') + snapshot = _load_snapshot(user_id=user, file_key=effective_file) + items = [] + dates = [] + for portfolio in snapshot.get("portfolios", []): + dates.append(portfolio.get("last_updated") or portfolio.get("updated_at")) + for item in portfolio.get("positions", []): + items.append({"symbol": item.get("symbol") or item.get("instrument_id"), "value_usd": item.get("market_value_usd")}) + path = ROOT / "data/users" / user / "saxobank/data" / effective_file + return dict(source=source, source_used="saxobank_csv", file_key=effective_file, source_sha256=hashlib.sha256(path.read_bytes()).hexdigest(), items=items, data_as_of=max((d for d in dates if d), default=None), observation_mode="selected_user_source") + from services.balance_service import balance_service + if source.startswith("stub") or source in ("auto", "all", "category_based"): + raise ValueError("Select an explicit portfolio source before requesting personal ML results") + result = await balance_service.resolve_current_balances(user_id=user, source=source) + if result.get("error") or result.get("source_used") in ("none", None): + raise ValueError("The selected authenticated portfolio source is unavailable") + expected = {"cointracking_csv": "cointracking"} + actual = result["source_used"].removesuffix("_cached") + if actual != expected.get(source, source): + raise ValueError("The resolved source differs from the selected source; no fallback is displayed") + return dict(source=source, source_used=result["source_used"], items=result.get("items", []), data_as_of=result.get("asof") or result.get("timestamp"), consulted_at=datetime.now(timezone.utc).isoformat(), observation_mode="selected_user_source") + + +def select_assets(context: dict, market: str, limit: int) -> tuple[list[str], dict]: + items = context.get("items", []) + ranked = sorted(items, key=lambda r: float(r.get("value_usd") or 0), reverse=True) + mappings = [] + for item in ranked: + symbol = item.get("symbol") + if not symbol: + continue + try: + asset = stock_symbol(symbol) if market == "stocks" else valid_symbol(symbol) + except ValueError: + mappings.append(dict(source_symbol=symbol, asset=None, reason="Unverified instrument mapping; no proxy substitution")) + continue + if not any(row.get("asset") == asset for row in mappings): + mappings.append(dict(source_symbol=symbol, asset=asset, reason="Exact source symbol" if asset == symbol else "Explicit Saxo venue mapping")) + selected = [row["asset"] for row in mappings if row["asset"]][:limit] + metadata = {k: v for k, v in context.items() if k != "items"} + metadata.update(held_positions=len(items), selected_assets=len(selected), omitted_assets=max(0, len([m for m in mappings if m["asset"]])-len(selected)), limit=limit, instrument_mapping=mappings, reason="Selected holdings ordered by source value; no benchmark, wrapped-token or ETF proxy substitution. No portfolio-level forecasting validation.") + metadata["scope_sha256"] = hashlib.sha256(json.dumps(dict(context=context, selected=selected), sort_keys=True).encode()).hexdigest() + return selected, metadata diff --git a/services/ml/reliability.py b/services/ml/reliability.py new file mode 100644 index 00000000..699bc604 --- /dev/null +++ b/services/ml/reliability.py @@ -0,0 +1,380 @@ +"""Read-only ML capabilities. No training, governance or synthetic fallback here. + +Evaluation and HTTP adapters use the same daily-close calculations and JSON +artifacts. Dataset receipts are required; the generic mixed-provider price cache +does not qualify as a verified forecasting dataset. +""" +from __future__ import annotations + +import asyncio +import hashlib +import json +import re +import os +import sqlite3 +from datetime import datetime, timedelta, timezone +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd +import httpx + +from api.schemas.ml_contract import ( + Availability, Horizon, ModelType, Provenance, UnifiedPrediction, ValidationStatus, +) + +PROTOCOL_ID = "ml-risk-2026-09-30-v1" +FEATURES = ["rv7", "rv30", "rv90", "ewma", "abs_return"] +ROOT = Path(__file__).resolve().parents[2] + + +def code_version() -> str: + paths = [Path(__file__), ROOT / "services/ml/risk_evaluation.py", ROOT / "api/schemas/ml_contract.py"] + return hashlib.sha256(b"".join(path.read_bytes() for path in paths)).hexdigest() + + +def digest(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def valid_symbol(symbol: str) -> str: + symbol = symbol.upper() + if not re.fullmatch(r"[A-Z0-9.^=_-]{1,24}", symbol): + raise ValueError("Invalid asset symbol") + return symbol + + +def daily_features(close: pd.Series, market: str) -> pd.DataFrame: + """Causal, trailing-only features, shared by research and inference.""" + annualization = 365 if market == "crypto" else 252 + returns = np.log(close / close.shift(1)) + frame = pd.DataFrame(index=close.index) + for window in (7, 30, 90): + frame[f"rv{window}"] = returns.rolling(window, min_periods=window).std(ddof=0) * np.sqrt(annualization) + frame["ewma"] = np.sqrt(returns.pow(2).ewm(alpha=.06, adjust=False, min_periods=30).mean() * annualization) + frame["abs_return"] = returns.abs() + return frame + + +def future_targets(close: pd.Series, market: str, horizon: int) -> pd.DataFrame: + """Decision close excluded; target close included. Never substitute horizons.""" + if horizon not in (7, 30): + raise ValueError("Only 7/30 calendar-day forecasts are supported") + annualization = 365 if market == "crypto" else 252 + returns = np.log(close / close.shift(1)).to_numpy() + rows = [] + for i, decision in enumerate(close.index): + target = decision + pd.Timedelta(days=horizon) + j = close.index.searchsorted(target) + # Crypto requires every daily close; stocks use the next observed session + # in retrospective evaluation. Live dates require a verified calendar. + if j >= len(close) or (market == "crypto" and (j - i != horizon or close.index[j] != target)): + rows.append((np.nan, pd.NaT)) + continue + values = returns[i + 1:j + 1] + variance = float(np.var(values, ddof=0) * annualization) if len(values) > 1 else np.nan + rows.append((np.sqrt(variance), close.index[j])) + return pd.DataFrame(rows, columns=["target", "target_end"], index=close.index) + + +def infer_estimator(model: dict, features: pd.DataFrame, horizon: int) -> np.ndarray: + if model["method"] == "persistence": + return features[f"rv{horizon}"].to_numpy() + if model["method"] == "ewma": + return features["ewma"].to_numpy() + if model["method"] == "ridge": + if model.get("features") != FEATURES: + raise ValueError("Artifact feature schema is incompatible") + transformed = (features[FEATURES].to_numpy() - np.array(model["mean"])) / np.array(model["scale"]) + return np.maximum(0, transformed @ np.array(model["coef"]) + model["intercept"]) + if model["method"] == "lstm": + from services.ml.risk_evaluation import lstm_network, sequences + import torch + network = lstm_network() + network.load_state_dict({key: torch.tensor(value, dtype=torch.float32) for key, value in model["weights"].items()}) + network.eval() + x = (features[FEATURES].to_numpy()-np.array(model["mean"])) / np.array(model["scale"]) + with torch.no_grad(): + return np.maximum(0, network(torch.tensor(sequences(x), dtype=torch.float32)).numpy().ravel()) + raise ValueError("Unsupported or incompatible estimator") + + +class CapabilityService: + def __init__(self, root: Path = ROOT, now=None, refresh_observations=None): + self.root = Path(root) + self.now = now or (lambda: datetime.now(timezone.utc)) + self.refresh_observations = (os.getenv("ML_DATA_REFRESH", "0") == "1") if refresh_observations is None else refresh_observations + # No cross-tenant result cache. Receipts and artifact hashes are verified + # on each read, so a replaced dataset cannot reuse an old prediction. + self.loaded: dict[str, str] = {} + self.successful: dict[str, str] = {} + self.errors: dict[str, str] = {} + + def registry(self) -> dict: + path = self.root / "config/ml_capability_registry.json" + if not path.exists(): + return {} + registry = json.loads(path.read_text(encoding="utf-8")) + return registry.get("capabilities", {}) + + def history(self, market: str, asset: str) -> tuple[pd.Series, dict]: + if market not in ("crypto", "stocks"): + raise ValueError("Unsupported market") + directory = self.root / "data/ml_verified" / market + receipt = json.loads((directory / f"{valid_symbol(asset)}.json").read_text(encoding="utf-8")) + path = (directory / receipt["file"]).resolve() + if not path.is_relative_to(directory.resolve()) or digest(path) != receipt["sha256"]: + raise ValueError("Dataset receipt hash mismatch or unsafe path") + if not receipt.get("provider") or not receipt.get("dataset_id"): + raise ValueError("Dataset provenance is incomplete") + if market == "stocks" and not receipt.get("adjustment_policy"): + raise ValueError("Stock split/dividend adjustment policy is missing") + frame = pd.read_csv(path) + dates = pd.DatetimeIndex(pd.to_datetime(frame["date"], utc=True)).normalize() + values = pd.to_numeric(frame["close"], errors="raise").to_numpy() + if dates.has_duplicates or not dates.is_monotonic_increasing or not np.isfinite(values).all() or (values <= 0).any(): + raise ValueError("Daily close dataset is invalid") + # Exclude an incomplete UTC day; stocks receipts carry completed dates. + complete = dates < pd.Timestamp(self.now()).normalize() + close = pd.Series(values[complete], index=dates[complete], name="close") + if market == "crypto" and len(close) > 1 and not ((close.index[1:] - close.index[:-1]).days == 1).all(): + raise ValueError("Daily crypto history contains missing closes") + if market == "stocks": + if not receipt.get("exchange_calendar"): + raise ValueError("Stock exchange session calendar is missing") + import exchange_calendars as xcals + expected = xcals.get_calendar(receipt["exchange_calendar"], start=close.index[0].date(), end=close.index[-1].date()).sessions + expected = pd.DatetimeIndex(expected).tz_localize("UTC") if expected.tz is None else expected.tz_convert("UTC") + if not close.index.equals(expected): + raise ValueError("Stock history contains missing or unexpected exchange sessions") + if market == "crypto" and self.refresh_observations: + from services.ml.live_observations import extend_crypto + close, receipt = extend_crypto(self.root, asset, close, receipt, self.now()) + if market == "stocks" and self.refresh_observations: + from services.ml.live_observations import extend_stocks + close, receipt = extend_stocks(self.root, asset, close, receipt, self.now()) + return close, receipt + + def artifact(self, market: str, asset: str, horizon: int) -> tuple[dict, Path]: + path = self.root / "models/validated_risk" / f"{market}_{valid_symbol(asset)}_{horizon}d.json" + model = json.loads(path.read_text(encoding="utf-8")) + registry = json.loads((path.parent / "registry.json").read_text(encoding="utf-8")) + publication = registry.get(path.name, {}) + if publication.get("sha256") != digest(path): + raise ValueError("Artifact fingerprint mismatch or incompatible publication record") + required = ("method", "dataset_id", "code_version", "training_start", "training_end", "validation") + if any(not model.get(key) for key in required) or model.get("schema_version") != 1: + raise ValueError("Artifact metadata is incomplete or incompatible") + if model.get("asset") != asset or model.get("market") != market or model.get("horizon") != horizon: + raise ValueError("Artifact target is incompatible") + validation = model["validation"] + if validation.get("state") != "retrospectively_validated" or validation.get("protocol_id") != PROTOCOL_ID: + raise ValueError("Artifact has no accepted confirmation evaluation") + if model.get("features") != FEATURES or model.get("annualization") != (365 if market == "crypto" else 252): + raise ValueError("Artifact features or annualization are incompatible") + if model.get("code_version") != code_version(): + raise ValueError("Artifact was evaluated against a different calculation version") + self.loaded[str(path)] = digest(path) + return model, path + + async def result(self, asset: str, market: str, model_type: ModelType, horizon: Horizon | None = None) -> UnifiedPrediction: + return await asyncio.to_thread(self._result, asset, market, model_type, horizon) + + def _result(self, asset: str, market: str, model_type: ModelType, horizon: Horizon | None = None) -> UnifiedPrediction: + asset = valid_symbol(asset) + diagnostic = model_type == ModelType.REGIME + result = UnifiedPrediction(asset=asset, market=market, horizon=horizon, + nature="diagnostic" if diagnostic else "forecast", + target="economic_rule_regime" if diagnostic else "future_realized_volatility" if model_type == ModelType.VOLATILITY else model_type.value, + unit="category" if diagnostic else f"annualized fraction ({365 if market == 'crypto' else 252} days/year)" if model_type == ModelType.VOLATILITY else None, + provenance=Provenance(code_version=code_version())) + if model_type == ModelType.VOLATILITY and horizon not in (Horizon.D7, Horizon.D30): + result.reason = "Only validated 7/30 calendar-day volatility forecasts are supported" + return result + if model_type not in (ModelType.VOLATILITY, ModelType.REGIME): + result.reason = "No validated forecast adapter is connected for this target" + return result + try: + close, receipt = self.history(market, asset) + if len(close) < 200: + raise ValueError("At least 200 complete daily closes are required") + result.data_as_of = close.index[-1].to_pydatetime() + result.provenance = Provenance(provider=receipt["provider"], dataset_id=receipt["dataset_id"], code_version=code_version(), adjustment_policy=receipt.get("adjustment_policy")) + age = (self.now() - result.data_as_of).total_seconds() / 86400 + result.quality.data_freshness = age * 24 + if model_type == ModelType.REGIME: + drawdown = float(close.iloc[-1] / close.iloc[-200:].max() - 1) + trend = float(close.iloc[-1] / close.iloc[-200:].mean() - 1) + result.value = "Bear Market" if drawdown <= -.2 else "Correction" if drawdown <= -.1 or trend < 0 else "Expansion" if trend > .2 else "Bull Market" + result.nature = "diagnostic" + result.target = "economic_rule_regime" + result.unit = "category" + result.availability = Availability.PARTIAL if age > 4 else Availability.AVAILABLE + result.reason = "Descriptive rules: 200-close drawdown <=-20% bear; <=-10% or below MA200 correction; >20% above MA200 expansion; otherwise bull. No future-direction probability." + (" Observations are stale." if age > 4 else "") + result.provenance.method = "trailing_drawdown_ma200_rules_v1" + result.validation = ValidationStatus(state="descriptive", reason="Economic thresholds are heuristic diagnostics") + return result + days = int(horizon.value[:-1]) + if age > (2 if market == "crypto" else 4): + raise ValueError("Verified daily closes are stale; refresh their provider receipt before forecasting") + model, path = self.artifact(market, asset, days) + if (receipt.get("evaluation_dataset_id", receipt["dataset_id"]) != model["dataset_id"] or + receipt.get("evaluation_sha256", receipt["sha256"]) != model["dataset_sha256"]): + raise ValueError("Dataset identity does not match the validated artifact") + target = close.index[-1] + pd.Timedelta(days=days) + if market == "stocks": + calendar = receipt.get("exchange_calendar") + if not calendar: + raise ValueError("Stock target session calendar is not verified") + import exchange_calendars as xcals + target = xcals.get_calendar(calendar).date_to_session(target.tz_localize(None), direction="next") + features = daily_features(close, market).dropna().tail(14) + value = float(infer_estimator(model, features, days)[-1]) + if not np.isfinite(value) or value < 0: + raise ValueError("Inference returned an invalid value") + result.value = value + result.target = "future_realized_volatility" + if target.tzinfo is None: + target = target.tz_localize("UTC") + result.target_date = target.to_pydatetime() + result.unit = f"annualized fraction ({model['annualization']} days/year)" + result.availability = Availability.AVAILABLE + result.reason = "Retrospectively validated point estimate; future performance is not guaranteed" + result.validation = ValidationStatus(**model["validation"]) + result.validation.evaluated_at = datetime.fromtimestamp(path.stat().st_mtime, timezone.utc) + result.provenance = Provenance(provider=receipt["provider"], dataset_id=receipt["dataset_id"], evaluation_dataset_id=model["dataset_id"], observation_sha256=receipt["sha256"], code_version=model["code_version"], artifact_sha256=digest(path), training_start=model["training_start"], training_end=model["training_end"], adjustment_policy=receipt.get("adjustment_policy"), method=model["method"]) + self.successful[str(path)] = digest(path) + # Intervals are deliberately omitted unless the separate calibration + # and untouched confirmation record certify their empirical coverage. + interval = model.get("interval") + if interval and .85 <= interval.get("confirmation_coverage", -1) <= .95 and interval.get("level") == .9: + from api.schemas.ml_contract import UncertaintyMeasures + width = interval["width"] + result.uncertainty = UncertaintyMeasures(lower_bound=max(0, value-width), upper_bound=value+width, nominal_coverage=.9, confirmation_coverage=interval['confirmation_coverage']) + else: + result.reason += "; uncertainty interval is not validated" + except Exception as exc: + result.value = None + result.availability = Availability.UNAVAILABLE + result.reason = "Verified dataset or artifact is missing" if isinstance(exc, FileNotFoundError) else str(exc) + self.errors[f"{market}/{asset}/{horizon}"] = result.reason + return result + + def catalog(self) -> list[dict]: + """Files present does not imply a loaded model or successful inference.""" + entries = [] + for base in ("models", "cache/ml_pipeline/models"): + directory = self.root / base + if not directory.exists(): + continue + for path in sorted(directory.rglob("*")): + if path.name == "registry.json": + continue + if path.suffix not in (".pth", ".pkl", ".keras") and not (path.suffix == ".json" and path.parent.name == "validated_risk"): + continue + entries.append(dict(path=str(path.relative_to(self.root)), sha256=digest(path), present=True, loaded=self.loaded.get(str(path)) == digest(path), inference_succeeded=self.successful.get(str(path)) == digest(path), availability="Available" if self.successful.get(str(path)) == digest(path) else "Experimental", reason="File presence does not certify compatibility, validation or inference")) + return entries + + def journal(self, user: str, source: str, results: list[UnifiedPrediction]) -> None: + """Opt-in after deployment: record consultations only, never train or trade.""" + if os.getenv("ML_INFERENCE_JOURNAL", "0") != "1": + return + if not re.fullmatch(r"[A-Za-z0-9_-]{1,64}", user): + raise ValueError("Invalid authenticated user identity") + directory = self.root / "data/users" / user / "ml" + directory.mkdir(parents=True, exist_ok=True) + with sqlite3.connect(directory / "inferences.sqlite") as connection: + connection.execute("CREATE TABLE IF NOT EXISTS inferences (identity TEXT PRIMARY KEY, consulted_at TEXT NOT NULL, result_json TEXT NOT NULL)") + for result in results: + if result.nature != "forecast" or result.value is None: + continue + payload = dict(user_id=user, source=source, **result.model_dump(mode="json")) + key = {name: payload[name] for name in ("user_id", "source", "asset", "market", "horizon", "data_as_of", "target_date", "provenance")} + identity = hashlib.sha256(json.dumps(key, sort_keys=True).encode()).hexdigest() + connection.execute("INSERT OR IGNORE INTO inferences VALUES (?,?,?)", (identity, self.now().isoformat(), json.dumps(payload, allow_nan=False))) + + def historical_correlation(self, market: str, assets: list[str]) -> UnifiedPrediction: + result = UnifiedPrediction(asset="PORTFOLIO_UNIVERSE", market=market, nature="diagnostic", target="historical_correlation", unit="Pearson correlation [-1,1]") + histories = {} + receipts = {} + for asset in assets: + try: + close, receipt = self.history(market, asset) + histories[asset] = np.log(close / close.shift(1)).tail(90) + receipts[asset] = receipt + except (ValueError, FileNotFoundError, KeyError): + continue + if len(histories) < 2: + result.reason = "At least two verified daily histories are required for a descriptive correlation" + return result + frame = pd.DataFrame(histories).dropna() + if len(frame) < 30 or (frame.std(ddof=0) == 0).any(): + result.reason = "Insufficient common observations or constant returns; correlation is undefined" + return result + result.value = frame.corr().to_dict() + result.data_as_of = frame.index[-1].to_pydatetime() + age = (self.now()-result.data_as_of).total_seconds()/86400 + complete = len(histories) == len(assets) and age <= 4 + result.availability = Availability.AVAILABLE if complete else Availability.PARTIAL + result.reason = f"Descriptive correlation on {len(frame)} common trailing daily returns; no forecast." + (" Some assets are missing or observations are stale." if not complete else "") + result.validation = ValidationStatus(state="descriptive", reason="Historical Pearson correlation, not a forecasting validation") + result.provenance = Provenance(provider="; ".join(sorted({r['provider'] for r in receipts.values()})), dataset_id="; ".join(sorted(r["dataset_id"] for r in receipts.values())), code_version=code_version(), method="trailing_90_observations_Pearson_complete_cases") + return result + + async def external_sentiment(self) -> UnifiedPrediction: + result = UnifiedPrediction(asset="CRYPTO_MARKET", market="crypto", nature="diagnostic", target="external_fear_greed", unit="index [0,100]") + result.provenance = Provenance(provider="Alternative.me", code_version=code_version(), method="external_provider_index_not_ML") + result.validation = ValidationStatus(state="descriptive", reason="External sentiment index; no predictive validation") + try: + async with httpx.AsyncClient(timeout=4) as client: + response = await client.get("https://api.alternative.me/fng/", params={"limit": 1}) + response.raise_for_status() + observation = response.json()["data"][0] + value = float(observation["value"]) + observed = datetime.fromtimestamp(int(observation["timestamp"]), tz=timezone.utc) + if not 0 <= value <= 100 or observed > self.now(): + raise ValueError("External provider observation is invalid") + result.value = value + result.data_as_of = observed + result.availability = Availability.AVAILABLE if self.now()-observed <= timedelta(days=2) else Availability.PARTIAL + result.reason = "External Fear & Greed observation; not an ML prediction" + except Exception: + result.reason = "External Fear & Greed provider observation is unavailable" + return result + + async def overview(self, user: str, source: str, assets: list[str], market: str) -> dict[str, Any]: + results = [] + for asset in assets: + results.append(await self.result(asset, market, ModelType.REGIME)) + for horizon in (Horizon.D7, Horizon.D30): + results.append(await self.result(asset, market, ModelType.VOLATILITY, horizon)) + correlation = await asyncio.to_thread(self.historical_correlation, market, assets) + results.append(correlation) + sentiment = await self.external_sentiment() if market == "crypto" else None + if sentiment: + results.append(sentiment) + self.journal(user, source, results) + catalog = await asyncio.to_thread(self.catalog) + forecasts = [r for r in results if r.nature == "forecast"] + available_forecasts = sum(r.value is not None for r in forecasts) + volatility_state = "Available" if forecasts and available_forecasts == len(forecasts) else "Partial" if available_forecasts else "Unavailable" + capabilities = [ + dict(id="volatility", label="Volatility forecast", availability=volatility_state, reason=f"{available_forecasts}/{len(forecasts)} requested forecasts are available. Only confirmed 7/30-day estimates are published; legacy intraday forecasts are unavailable"), + dict(id="regime", label="Economic regime rules", availability="Partial" if any(r.value is not None for r in results if r.target == "economic_rule_regime") else "Unavailable", reason="Descriptive trailing-price rules, separate from HMM state probabilities"), + dict(id="hmm", label="HMM latent states", availability="Experimental", reason="States A–D require documented economic mapping; state probabilities are not probabilities of a future rise"), + dict(id="correlation", label="Historical correlations", availability=correlation.availability.value, reason=correlation.reason+" Transformer forecasts remain experimental."), + dict(id="cycle", label="Market cycle", availability="Experimental", reason="Heuristic cycle diagnostic, not a validated forecast"), + dict(id="sentiment", label="Fear & Greed", availability=sentiment.availability.value if sentiment else "Unavailable", reason=sentiment.reason if sentiment else "External crypto indicator is not applicable to stocks"), + dict(id="direction", label="Directional prediction", availability="Rejected", reason="Existing causal research failed the economic gates; experiments remain closed"), + dict(id="alerts", label="ML alert predictor", availability="Rejected", reason="Disabled; reactivation is outside this implementation"), + ] + registry = self.registry() + for capability in capabilities: + entry = registry.get(capability["id"]) + if entry: + capability.update({key: entry[key] for key in ("availability", "reason", "validation_state") if key in entry}) + return dict(user_id=user, source=source, market=market, scope="explicit_market_universe", code_version=code_version(), observed_at=self.now().isoformat(), protocol_id=PROTOCOL_ID, results=[r.model_dump(mode="json") for r in results], capabilities=capabilities, artifacts=catalog, counts=dict(files_present=len(catalog), models_loaded=sum(a["loaded"] for a in catalog), successful_inferences=sum(r.value is not None and r.nature == "forecast" for r in results)), governance_integration=False) + + +capability_service = CapabilityService() diff --git a/services/ml/risk_evaluation.py b/services/ml/risk_evaluation.py new file mode 100644 index 00000000..69d73a09 --- /dev/null +++ b/services/ml/risk_evaluation.py @@ -0,0 +1,164 @@ +"""Frozen chronological volatility comparison; explicit actions only.""" +from __future__ import annotations + +import json +import os +from uuid import uuid4 +from pathlib import Path + +import numpy as np +import pandas as pd +from filelock import FileLock +from sklearn.linear_model import Ridge + +from services.ml.reliability import FEATURES, PROTOCOL_ID, CapabilityService, daily_features, future_targets, infer_estimator, digest, code_version + + +def sequences(x: np.ndarray) -> np.ndarray: + padded = np.pad(x, ((13, 0), (0, 0)), mode="edge") + return np.stack([padded[i:i+14] for i in range(len(x))]) + + +def lstm_network(): + import torch + class Network(torch.nn.Module): + def __init__(self): + super().__init__() + self.lstm = torch.nn.LSTM(len(FEATURES), 8, batch_first=True) + self.output = torch.nn.Linear(8, 1) + def forward(self, x): + values, _ = self.lstm(x) + return torch.nn.functional.softplus(self.output(values[:, -1])) + return Network() + + +def predict_rows(model, frame, rows, horizon): + # Include past context at fold boundaries; every sequence ends at its own + # decision row. Later rows never enter an earlier prediction. + prediction = pd.Series(infer_estimator(model, frame, horizon), index=frame.index) + return prediction.loc[rows.index].to_numpy() + + +def fit_estimator(method: str, train: pd.DataFrame) -> dict: + if method in ("persistence", "ewma"): + return {"method": method, "features": FEATURES} + x = train[FEATURES] + mean = x.mean().to_numpy() + scale = x.std(ddof=0).replace(0, 1).to_numpy() + if method == "lstm": + import torch + torch.set_num_threads(1) + torch.manual_seed(1729) + torch.use_deterministic_algorithms(True) + network = lstm_network() + inputs = torch.tensor(sequences((x.to_numpy()-mean)/scale), dtype=torch.float32) + target = torch.tensor(train.target.to_numpy()[:, None], dtype=torch.float32) + optimizer = torch.optim.Adam(network.parameters(), lr=.01) + for _ in range(12): + optimizer.zero_grad() + loss = torch.nn.functional.mse_loss(network(inputs), target) + loss.backward() + optimizer.step() + return dict(method="lstm", features=FEATURES, mean=mean.tolist(), scale=scale.tolist(), weights={k:v.detach().tolist() for k,v in network.state_dict().items()}, architecture="lstm_5_8_sequence14", seed=1729, epochs=12) + fitted = Ridge(alpha=1).fit((x.to_numpy()-mean)/scale, train["target"]) + return dict(method="ridge", features=FEATURES, mean=mean.tolist(), scale=scale.tolist(), coef=fitted.coef_.tolist(), intercept=float(fitted.intercept_)) + + +def metrics(actual: np.ndarray, predicted: np.ndarray) -> dict: + variance = np.maximum(np.square(predicted), 1e-10) + truth = np.maximum(np.square(actual), 1e-10) + # QLIKE in the nonnegative, scale-free ratio form. + ratio = truth / variance + return dict(qlike=float(np.mean(ratio-np.log(ratio)-1)), mae=float(np.mean(np.abs(actual-predicted))), rows=len(actual)) + + +def partitions(frame: pd.DataFrame, calibration_start, test_start, test_end): + train = frame[(frame.index < calibration_start) & (frame.target_end < calibration_start)] + calibration = frame[(frame.index >= calibration_start) & (frame.index < test_start) & (frame.target_end < test_start)] + test = frame[(frame.index >= test_start) & (frame.index <= test_end) & (frame.target_end <= test_end)] + if train.empty or calibration.empty or test.empty: + raise ValueError("Insufficient rows after target-end purge") + if (train.index[-1]-train.index[0]).days < 730: + raise ValueError("Training span is shorter than two years after purge") + return train, calibration, test + + +def evaluate(service: CapabilityService, market: str, asset: str, horizon: int, *, publish=False) -> dict: + close, receipt = service.history(market, asset) + frame = daily_features(close, market).join(future_targets(close, market, horizon)).dropna() + report = dict(asset=asset, market=market, horizon=horizon, protocol_id=PROTOCOL_ID, dataset_id=receipt["dataset_id"], dataset_sha256=receipt["sha256"], code_version=code_version(), data_end=close.index[-1].isoformat(), annualization=365 if market == "crypto" else 252) + # Final 12 calendar months are untouched until all development folds and + # the candidate choice are complete. No confirmation-based switching. + confirmation_start = close.index[-1] - pd.Timedelta(days=364) + # A stock boundary can fall on a weekend/holiday. Leave enough slack to + # guarantee a full 730-day observed training span after target-end purge. + calibration_start = frame.index[0] + pd.Timedelta(days=740 + horizon) + folds = [] + methods = ["persistence", "ewma", "ridge"] + lstm_reason = None + try: + import torch + methods.append("lstm") + except ImportError: + lstm_reason = "PyTorch runtime is unavailable for the corrected causal candidate" + while calibration_start + pd.Timedelta(days=366) < confirmation_start: + test_start = calibration_start + pd.Timedelta(days=183) + test_end = test_start + pd.Timedelta(days=182) + train, calibration, test = partitions(frame, calibration_start, test_start, test_end) + scores = {} + for method in methods: + model = fit_estimator(method, train) + prediction = predict_rows(model, frame, test, horizon) + scores[method] = metrics(test.target.to_numpy(), prediction) + folds.append(dict(train_start=train.index[0].isoformat(), train_end=train.index[-1].isoformat(), calibration_start=calibration.index[0].isoformat(), calibration_end=calibration.index[-1].isoformat(), test_start=test.index[0].isoformat(), test_end=test.index[-1].isoformat(), scores=scores)) + calibration_start += pd.Timedelta(days=183) + if len(folds) < 3: + return {**report, "state": "not_evaluable", "reason": "Fewer than three development windows plus reserved confirmation", "folds": folds, "published": False} + means = {method: float(np.mean([f["scores"][method]["qlike"] for f in folds])) for method in methods} + reference = min(("persistence", "ewma"), key=lambda m: means[m]) + candidate = min([m for m in methods if m not in ("persistence", "ewma")], key=lambda m: means[m]) + wins = sum(f["scores"][candidate]["qlike"] < f["scores"][reference]["qlike"] for f in folds) + improvement = 1-means[candidate]/max(means[reference], 1e-10) + mae_control = np.mean([f["scores"][candidate]["mae"] for f in folds]) <= np.mean([f["scores"][reference]["mae"] for f in folds]) + learned_pass = improvement >= .05 and wins/len(folds) >= 2/3 and mae_control + chosen = candidate if learned_pass else reference + # Fit once on pre-calibration rows, then evaluate the chosen frozen estimator. + train, calibration, confirmation = partitions(frame, confirmation_start-pd.Timedelta(days=183), confirmation_start, close.index[-1]) + models = {method: fit_estimator(method, train) for method in methods} + confirmation_scores = {method: metrics(confirmation.target.to_numpy(), predict_rows(models[method], frame, confirmation, horizon)) for method in methods} + confirm_improvement = 1-confirmation_scores[candidate]["qlike"]/max(confirmation_scores[reference]["qlike"], 1e-10) + confirmed = chosen in ("persistence", "ewma") or (confirm_improvement >= .05 and confirmation_scores[candidate]["mae"] <= confirmation_scores[reference]["mae"]) + # A failed learned confirmation cannot choose another learned candidate. + if not confirmed: + chosen = reference + fitted = models[chosen] + residuals = np.abs(calibration.target.to_numpy()-predict_rows(fitted, frame, calibration, horizon)) + width = float(np.quantile(residuals, .9, method="higher")) + prediction = predict_rows(fitted, frame, confirmation, horizon) + coverage = float(np.mean(np.abs(confirmation.target.to_numpy()-prediction) <= width)) + interval = dict(level=.9, width=width, confirmation_coverage=coverage, calibration_rows=len(calibration), confirmation_rows=len(confirmation)) if .85 <= coverage <= .95 else None + conclusion = dict(state="retrospectively_validated", reason=f"Frozen walk-forward protocol; selected {chosen}. Learned model gates: QLIKE >=5%, folds >=2/3, MAE control and reserved confirmation.", protocol_id=PROTOCOL_ID, metrics=dict(development_qlike=means, learned_candidate=candidate, learned_improvement=improvement, learned_wins=wins, windows=len(folds), confirmation=confirmation_scores, learned_confirmation_improvement=confirm_improvement, interval_coverage=coverage)) + artifact = {**fitted, **report, "schema_version": 1, "features": FEATURES, "training_start": train.index[0].isoformat(), "training_end": train.index[-1].isoformat(), "validation": conclusion, "interval": interval} + report.update(state="retrospectively_validated", selected=chosen, reference=reference, folds=folds, validation=conclusion, interval=interval, lstm=dict(state="evaluated" if "lstm" in methods else "not_evaluable", reason=lstm_reason or "Corrected trailing sequences, train-only normalization, fixed seed 1729, 12 epochs; no legacy checkpoint reuse"), published=False) + if publish: + directory = service.root / "models/validated_risk" + directory.mkdir(parents=True, exist_ok=True) + path = directory / f"{market}_{asset}_{horizon}d.json" + temporary = path.with_suffix(f".{uuid4().hex}.json.tmp") + temporary.write_text(json.dumps(artifact, indent=2, allow_nan=False), encoding="utf-8") + # Verify serialized fields and inference before atomically making visible. + serialized = json.loads(temporary.read_text(encoding="utf-8")) + verified = predict_rows(serialized, frame, confirmation, horizon) + if not np.array_equal(verified, prediction): + raise ValueError("Artifact inference is not reproducible") + with FileLock(str(directory / "publication.lock")): + registry_path = directory / "registry.json" + registry = json.loads(registry_path.read_text(encoding="utf-8")) if registry_path.exists() else {} + registry[path.name] = dict(sha256=digest(temporary), dataset_id=receipt["dataset_id"], code_version=artifact["code_version"], validation_state="retrospectively_validated", governance_eligible=False) + registry_tmp = registry_path.with_suffix(".json.tmp") + registry_tmp.write_text(json.dumps(registry, indent=2, allow_nan=False), encoding="utf-8") + os.replace(temporary, path) + os.replace(registry_tmp, registry_path) + service.artifact(market, asset, horizon) + report.update(published=True, artifact=str(path.relative_to(service.root)), artifact_sha256=digest(path)) + return report diff --git a/services/ml/training_executor.py b/services/ml/training_executor.py index 7c115a6c..817dc44f 100644 --- a/services/ml/training_executor.py +++ b/services/ml/training_executor.py @@ -312,6 +312,7 @@ def _run_real_training( Returns: Dict with training metrics """ + raise ValueError("Legacy training is unavailable: use /api/ml/train for validated daily-risk evaluation. No unvalidated artifact is published.") try: # Import training functions from scripts import sys diff --git a/services/ml_pipeline_manager_optimized.py b/services/ml_pipeline_manager_optimized.py index 07683e4a..78386b78 100644 --- a/services/ml_pipeline_manager_optimized.py +++ b/services/ml_pipeline_manager_optimized.py @@ -3,6 +3,7 @@ Gestion intelligente des modèles ML avec lazy loading, gestion mémoire et cache LRU """ +import asyncio import logging from pathlib import Path from typing import Dict, List, Optional, Any, Union @@ -248,12 +249,12 @@ def _get_regime_status(self) -> Dict[str, Any]: "metadata_exists": metadata_file.exists(), "model_loaded": "regime" in self.model_cache.cache, "model_cached": "regime" in self.model_cache.cache, - "last_updated": self._get_last_modified(self.regime_path) + "last_updated": datetime.fromtimestamp(model_file.stat().st_mtime).isoformat() if model_file.exists() else None } def _get_correlation_status(self) -> Dict[str, Any]: """Statut des modèles de corrélation""" - model_files = list(self.correlation_path.glob("*.pkl")) + model_files = list(self.correlation_path.glob("*correlation*.pth")) cached_count = len([k for k in self.model_cache.cache.keys() if k.startswith("correlation_")]) return { @@ -343,9 +344,12 @@ async def load_volatility_model_async(self, symbol: str) -> bool: metadata = safe_pickle_load(metadata_path) scaler = safe_pickle_load(scaler_path) + feature_names = metadata.get("feature_columns") or metadata.get("features_used") + if not isinstance(feature_names, list) or not feature_names or getattr(scaler, "n_features_in_", None) != len(feature_names): + raise ValueError("Artifact feature schema or scaler compatibility is unverified") # Charger le modèle PyTorch avec compatibilité maximum - model = safe_torch_load(model_path, map_location='cpu', weights_only=False) + model = safe_torch_load(model_path, map_location='cpu', weights_only=True) if hasattr(model, 'eval'): model.eval() @@ -423,9 +427,12 @@ def load_volatility_model(self, symbol: str) -> bool: metadata = safe_pickle_load(metadata_path) scaler = safe_pickle_load(scaler_path) + feature_names = metadata.get("feature_columns") or metadata.get("features_used") + if not isinstance(feature_names, list) or not feature_names or getattr(scaler, "n_features_in_", None) != len(feature_names): + raise ValueError("Artifact feature schema or scaler compatibility is unverified") # Charger le modèle PyTorch avec compatibilité maximum - model = safe_torch_load(model_path, map_location='cpu', weights_only=False) + model = safe_torch_load(model_path, map_location='cpu', weights_only=True) if hasattr(model, 'eval'): model.eval() @@ -464,99 +471,8 @@ def load_volatility_model(self, symbol: str) -> bool: return False async def load_regime_model_async(self) -> bool: - """Charger le modèle de régime de façon optimisée""" - model_key = "regime" - - # Vérifier le cache - if self.model_cache.get(model_key) is not None: - self.stats["cache_hits"] += 1 - logger.info("Regime model already cached") - return True - - self.stats["cache_misses"] += 1 - - with self.loading_lock: - if self.model_cache.get(model_key) is not None: - return True - - self.loading_status[model_key] = "loading" - - try: - start_time = time.time() - - model_path = self.regime_path / "regime_neural_best.pth" - metadata_path = self.regime_path / "regime_metadata.pkl" - scaler_path = self.regime_path / "regime_scaler.pkl" - features_path = self.regime_path / "regime_features.pkl" - - if not model_path.exists(): - logger.warning("Regime model file not found") - self.loading_status[model_key] = "failed" - return False - - # Estimer la taille - model_size_mb = self._estimate_model_size(model_path) - - # Charger tous les composants avec gestion d'erreur robuste (safe loading) - try: - metadata = safe_pickle_load(metadata_path) - except Exception as e: - logger.warning(f"Failed to load metadata, using fallback: {e}") - metadata = {"model_type": "regime_classifier", "version": "2.0.0", "accuracy": 0.78} - - try: - scaler = safe_pickle_load(scaler_path) - except Exception as e: - logger.warning(f"Failed to load scaler with safe pickle: {e}") - from sklearn.preprocessing import StandardScaler - scaler = StandardScaler() + return await asyncio.to_thread(self.load_regime_model) - try: - features = safe_pickle_load(features_path) - except Exception as e: - logger.warning(f"Failed to load features, using fallback: {e}") - features = ["price_change_1d", "price_change_7d", "volatility_7d", "volatility_30d", "rsi"] - - # Charger le modèle avec compatibilité maximum - model = safe_torch_load(model_path, map_location='cpu', weights_only=False) - - if hasattr(model, 'eval'): - model.eval() - - load_time = time.time() - start_time - - # Stocker dans le cache - model_data = { - "model": model, - "scaler": scaler, - "features": features, - "metadata": metadata, - "loaded_at": datetime.now().isoformat(), - "load_time_seconds": load_time, - "type": "regime", - "is_mock": False, - "size_mb": model_size_mb - } - - self.model_cache.put(model_key, model_data, model_size_mb) - - # Mettre à jour les stats - self.stats["models_loaded_total"] += 1 - self.stats["average_load_time"] = ( - self.stats["average_load_time"] * (self.stats["models_loaded_total"] - 1) + load_time - ) / self.stats["models_loaded_total"] - - self.loading_status[model_key] = "loaded" - - logger.info(f"Regime model loaded successfully in {load_time:.2f}s (size: {model_size_mb:.1f}MB)") - return True - - except Exception as e: - logger.error(f"Failed to load regime model: {e}") - self.loading_status[model_key] = "failed" - self.stats["loading_errors"] += 1 - return False - def load_regime_model(self) -> bool: """Charger le modèle de régime de façon optimisée (version synchrone)""" model_key = "regime" @@ -594,12 +510,14 @@ def load_regime_model(self) -> bool: scaler = safe_pickle_load(scaler_path) features = safe_pickle_load(features_path) + if not isinstance(features, list) or not features or getattr(scaler, "n_features_in_", None) != len(features): + raise ValueError("Regime artifact feature schema is incompatible") # Charger le modèle avec compatibilité PyTorch model = safe_torch_load( model_path, map_location='cpu', - weights_only=False, + weights_only=True, ) if hasattr(model, 'eval'): @@ -629,58 +547,11 @@ def load_regime_model(self) -> bool: return True except Exception as load_error: - logger.warning(f"Failed to load real regime model, using fallback mock: {load_error}") - - # Fallback to mock model (comme dans l'original) - mock_metadata = { - "model_type": "regime_classifier", - "version": "1.0.0", - "accuracy": 0.75, - "classes": ["Bear Market", "Correction", "Bull Market", "Expansion"] - } - - class MockScaler: - def transform(self, X): - return X - def inverse_transform(self, X): - return X - - mock_features = ["price_change_1d", "volume_change_1d", "rsi", "macd", "bollinger_position"] - - class MockRegimeModel: - def __init__(self): - self.classes = ["Bear Market", "Correction", "Bull Market", "Expansion"] - - def eval(self): - pass - - def predict(self, X): - import random - return random.choice(self.classes) - - mock_model = MockRegimeModel() - - model_data = { - "model": mock_model, - "scaler": MockScaler(), - "features": mock_features, - "metadata": mock_metadata, - "loaded_at": datetime.now().isoformat(), - "type": "regime", - "is_mock": True - } - - # Ajouter au cache - self.model_cache.put(model_key, model_data, 10) # Mock model très léger - self.loading_status[model_key] = "loaded" - - load_time = time.time() - start_time - self.stats["total_loading_time"] += load_time - self.stats["models_loaded"] += 1 - - logger.info(f"Mock regime model loaded successfully in {load_time:.2f}s") - return True - + logger.warning("Regime artifact loading failed: %s", load_error) + self.loading_status[model_key] = "failed" + self.stats["loading_errors"] += 1 + return False + except Exception as e: logger.error(f"Failed to load regime model: {e}") self.loading_status[model_key] = "failed" diff --git a/services/monitoring/phase3_health_monitor.py b/services/monitoring/phase3_health_monitor.py index 2fc03f38..ac420b76 100644 --- a/services/monitoring/phase3_health_monitor.py +++ b/services/monitoring/phase3_health_monitor.py @@ -419,7 +419,10 @@ async def _check_explainable_ai_health(self, component: ExplainableAIEngine) -> metrics = { "explanation_time_ms": explanation_time_ms, - "last_explanation_confidence": explanation.confidence if explanation else 0, + "last_explanation_confidence": None, + "probe_explanation_confidence": explanation.confidence if explanation else None, + "probe_scope": "synthetic_connectivity_only", + "validation_state": "unvalidated", "explanation_methods": ["shap", "lime", "custom"], "explanations_generated": 1 } diff --git a/services/risk/bourse/data_fetcher.py b/services/risk/bourse/data_fetcher.py index 8357eaf1..d84748eb 100644 --- a/services/risk/bourse/data_fetcher.py +++ b/services/risk/bourse/data_fetcher.py @@ -158,8 +158,8 @@ async def fetch_historical_prices( ) # A currency-qualified Saxo line must never use another trading currency. - expected_currency = (base_symbol.rsplit('_', 1)[1].upper() - if re.search(r'_[A-Z]{3}$', base_symbol.upper()) else None) + qualified_currency = re.search(r'_([A-Z]{3})(?:\.[A-Z]{1,3})?$', base_symbol.upper()) + expected_currency = qualified_currency.group(1) if qualified_currency else None # Verified 1:1 corporate action: ROG was replaced by ROP on SIX. # Source: https://www.roche.com/investors/updates/inv-update-2026-03-16 diff --git a/services/risk/bourse/specialized_analytics.py b/services/risk/bourse/specialized_analytics.py index 4d1ada23..460b3904 100644 --- a/services/risk/bourse/specialized_analytics.py +++ b/services/risk/bourse/specialized_analytics.py @@ -125,24 +125,10 @@ def _get_sector_for_ticker(self, ticker: str) -> str: try: import yfinance as yf - # Convert exchange suffix to Yahoo format if needed - # :xlon -> .L (London), :xpar -> .PA (Paris), :xetr -> .DE (Frankfurt) - exchange_suffix = '' - if ':' in ticker: - exchange = ticker.split(':')[1].lower() - exchange_map = { - 'xlon': '.L', # London - 'xpar': '.PA', # Paris - 'xetr': '.DE', # Frankfurt - 'xams': '.AS', # Amsterdam - 'xswx': '.SW', # Swiss - 'xmil': '.MI', # Milan - 'xnas': '', # NASDAQ (no suffix needed) - 'xnys': '', # NYSE (no suffix needed) - } - exchange_suffix = exchange_map.get(exchange, '') - - yf_ticker = clean_ticker + exchange_suffix + from services.ml.bourse.currency_detector import CurrencyExchangeDetector + base, _, venue = ticker.partition(':') + hints = {'xswx':'SWX','xvtx':'SWX','xlon':'LSE','xams':'AMS','xmil':'MIL','xetr':'XETRA','xnas':'NASDAQ','xnys':'NYSE'} + yf_ticker, _, _ = CurrencyExchangeDetector().detect_currency_and_exchange(base.upper(), exchange_hint=hints.get(venue.lower())) logger.debug(f"[sector-lookup] Trying yfinance for {yf_ticker} (from {ticker})") stock = yf.Ticker(yf_ticker) diff --git a/static/admin-dashboard.html b/static/admin-dashboard.html index 1b0f19a9..25cadbfd 100644 --- a/static/admin-dashboard.html +++ b/static/admin-dashboard.html @@ -714,7 +714,7 @@

// ===== AUTH GUARD (Dec 2025) ===== - import { checkAuth } from './core/auth-guard.js'; + import { checkAuth, getAuthHeaders } from './core/auth-guard.js'; await checkAuth(); // Vérifie authentification + redirect si nécessaire // ================================= @@ -802,12 +802,12 @@

Access denied. Admin role required.

Current user: "${activeUser}"

Please switch to an admin user (e.g., "jack") using the user selector in the top right corner.`; + container.innerHTML = `
Access denied. Admin role required.

Current user: "${activeUser}"

Sign in with an account that has the admin role.
`; return; } throw new Error(`HTTP ${response.status}`); @@ -997,7 +997,7 @@

+ import { ensureSelectedSource } from './core/selected-source.js'; // ====== Phase 3A: Advanced Risk Analysis ====== async function loadAdvancedRiskComponents() { @@ -116,6 +117,11 @@

Advanced Risk Analysis

} const advancedContent = document.getElementById('advanced-risk-content'); + const source = await ensureSelectedSource(); + if (!source) { + advancedContent.innerHTML = '
Unavailable: no authenticated portfolio source is selected. Choose a source in Settings.
'; + return; + } advancedContent.innerHTML = ''; // Create Advanced Risk Analysis Panel @@ -245,7 +251,8 @@

Stress Testing

const metrics = data.risk_metrics; const exposureByGroup = metrics.exposure_by_group || {}; - const gri = metrics.group_risk_index || 0; + const gri = metrics.group_risk_index; + if (typeof gri !== 'number' || !Number.isFinite(gri)) throw new Error('The GRI measure is unavailable'); const topGroups = Object.entries(exposureByGroup) .sort(([, a], [, b]) => b - a) @@ -318,9 +325,9 @@
- ${gri < 3 ? 'Portfolio faible risque' : - gri < 6 ? 'Portfolio risque modere' : - 'Portfolio haut risque - Considerer diversifier'} + ${gri < 3 ? 'Low risk portfolio' : + gri < 6 ? 'Moderate risk portfolio' : + 'High risk portfolio - consider diversification'}
`; diff --git a/static/ai-dashboard.html b/static/ai-dashboard.html index 80c3ab8a..e7d4c392 100644 --- a/static/ai-dashboard.html +++ b/static/ai-dashboard.html @@ -64,6 +64,7 @@ + @@ -73,6 +74,18 @@ -
+
diff --git a/static/core/bourse-source-context.js b/static/core/bourse-source-context.js index 38de49dc..5fd1d79d 100644 --- a/static/core/bourse-source-context.js +++ b/static/core/bourse-source-context.js @@ -16,3 +16,16 @@ window.resolveBourseSourceSelection = async function (source) { if (!selected) throw new Error('The selected stock source is unavailable'); return { type: 'csv', fileKey: selected.file_path?.split(/[\\/]/).pop() || null }; }; + +/** Restore the authenticated account's existing source without persisting settings. */ +window.readConfiguredBourseSource = async function () { + const user = localStorage.getItem('activeUser'); + if (!user) return null; + const { getAuthHeaders } = await import('./auth-guard.js'); + const response = await fetch(window.getApiBase() + '/api/sources/v2/bourse/active', {headers:getAuthHeaders()}); + if (!response.ok || localStorage.getItem('activeUser') !== user) return null; + const raw = await response.json(); const data = raw.data || raw; + const source = data.active_source; + if (data.category !== 'bourse' || data.status === 'not_found' || !['saxobank_csv','saxobank_api','manual_bourse'].includes(source)) return null; + return source; +}; diff --git a/static/core/risk-data-orchestrator.js b/static/core/risk-data-orchestrator.js index 0ad010ad..ee6b02d3 100644 --- a/static/core/risk-data-orchestrator.js +++ b/static/core/risk-data-orchestrator.js @@ -1,3 +1,4 @@ +import { riskRequestScope, riskRequestParams } from './risk-request.js'; // static/core/risk-data-orchestrator.js // Orchestrateur centralisé pour hydrater le risk store avec toutes les métriques calculées // Utilisé par rebalance.html, analytics-unified.html, execution.html pour parité avec risk-dashboard.html @@ -80,15 +81,7 @@ export async function hydrateRiskStore() { debugLogger.debug(`hydrateRiskStore - fetching risk data with source: '${currentSource}', _csv_hint: '${cacheBuster}'`); const riskData = await window.globalConfig.apiRequest('/api/risk/dashboard', { - params: { - source: currentSource, // FIX: Pass source parameter for multi-tenant isolation - min_usd: 1.0, - price_history_days: 365, - lookback_days: 90, - use_dual_window: true, // Cohérent avec risk-dashboard-main-controller.js - risk_version: 'v2_active', - _csv_hint: cacheBuster // Invalide cache backend quand CSV change - } + params: riskRequestParams(riskRequestScope()) }); return riskData; } catch (err) { diff --git a/static/core/risk-request.js b/static/core/risk-request.js new file mode 100644 index 00000000..2af4ddaf --- /dev/null +++ b/static/core/risk-request.js @@ -0,0 +1,10 @@ +// Même périmètre que le Risk Dashboard; aucune métrique n'est recalculée ici. +export function riskRequestScope() { + return {user: localStorage.getItem('activeUser'), source: window.globalConfig?.get('data_source') || localStorage.getItem('data_source'), file: window.userSettings?.csv_selected_file || 'latest', min_usd: window.globalConfig?.get('min_usd_threshold') ?? 1}; +} +export function riskRequestParams(scope = riskRequestScope()) { + if (!scope.user || !scope.source) throw new Error('Authenticated portfolio source required'); + return {source: scope.source, min_usd: Number.isFinite(Number(scope.min_usd)) && Number(scope.min_usd) >= 0 ? Number(scope.min_usd) : 1, price_history_days: 365, lookback_days: 90, + use_dual_window: true, risk_version: 'v2_active', _csv_hint: scope.file}; +} +export const formatRiskScore = value => Number.isFinite(value) ? `${Math.round(value)}/100` : 'Unavailable'; diff --git a/static/core/role-policy.js b/static/core/role-policy.js new file mode 100644 index 00000000..0969d3ae --- /dev/null +++ b/static/core/role-policy.js @@ -0,0 +1 @@ +export const hasAdminNavigation = (user, userId) => Boolean(userId && user?.id === userId && Array.isArray(user.roles) && user.roles.includes('admin')); diff --git a/static/core/selected-source.js b/static/core/selected-source.js new file mode 100644 index 00000000..811b0b0f --- /dev/null +++ b/static/core/selected-source.js @@ -0,0 +1,32 @@ +import { getAuthHeaders } from './auth-guard.js'; + +let pending = null; +let pendingUser = null; + +/** Restore only the authenticated user's configured source; never pick a fallback. */ +export async function ensureSelectedSource() { + const config = window.globalConfig; + const selected = config?.get?.('data_source'); + if (selected) return selected; + const user = localStorage.getItem('activeUser'); + if (!user || !config) return null; + if (pending && pendingUser === user) return pending; + pendingUser = user; + const request = (async () => { + const response = await fetch('/api/users/sources', { headers: getAuthHeaders() }); + if (!response.ok) return null; + const data = await response.json(); + if (data.user !== user || localStorage.getItem('activeUser') !== user) return null; + const current = config.get('data_source'); + if (current) return current; + const candidate = data.current_source; + const listed = new Set((data.sources || []).map(item => item.key)); + const equivalent = candidate === 'cointracking' ? 'cointracking_csv' : candidate; + if (!candidate || !listed.has(equivalent) || candidate.startsWith('stub')) return null; + config.set('data_source', candidate); + return candidate; + })(); + pending = request; + try { return await request; } + finally { if (pending === request) { pending = null; pendingUser = null; } } +} diff --git a/static/css/bourse-analytics.css b/static/css/bourse-analytics.css index 8865e0e5..d3ec9d5a 100644 --- a/static/css/bourse-analytics.css +++ b/static/css/bourse-analytics.css @@ -261,3 +261,20 @@ font-size: 1.25rem; } } + + +.dashboard-header { flex-wrap:wrap; gap:1rem; } +.dashboard-header > div { min-width:0; max-width:100%; } +@media (max-width:768px) { + .dashboard-header { flex-direction:column; align-items:flex-start; } + .dashboard-header > div { flex-wrap:wrap; } + .dashboard-header h1 { font-size:1.5rem; overflow-wrap:anywhere; } +} +/* Mobile controls and grids keep their content inside the page. */ +.dashboard-card { min-width: 0; } +@media (max-width: 768px) { + .custom-stress-control { flex-wrap: wrap; } + .custom-stress-control > label { flex: 0 0 100% !important; } + #customStressSlider { min-width: 0; } + #customStressValue { flex: 0 0 auto !important; } +} diff --git a/static/css/bourse-recommendations.css b/static/css/bourse-recommendations.css index 60168fba..0fd61a2c 100644 --- a/static/css/bourse-recommendations.css +++ b/static/css/bourse-recommendations.css @@ -290,3 +290,124 @@ gap: var(--space-sm); } } +.dashboard-card { min-width: 0; } +#impactSimulator { max-width: 100%; overflow-x: auto; } +@media (max-width: 768px) { + .dashboard-header { flex-wrap: wrap; gap: 1rem; } + .dashboard-header > div { min-width: 0; max-width: 100%; } + .dashboard-header h1 { font-size: 1.5rem; overflow-wrap: anywhere; } +} + +/* Market Opportunities: scoped to preserve the Recommendations tab. */ +#opportunities .card-header { flex-wrap: wrap; gap: 0.75rem; } +#opportunities .mo-content { min-width: 0; } +#opportunities p { line-height: 1.55; } +#opportunities .mo-metrics { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(min(100%, 180px), 1fr)); + gap: 0.75rem; + margin-bottom: 1rem; +} +#opportunities .mo-metric { + padding: 1rem; + background: var(--theme-bg-subtle, var(--theme-surface)); + border: 1px solid var(--theme-border); + border-radius: var(--radius-md); + display: flex; + flex-direction: column; + gap: 0.35rem; +} +#opportunities .mo-metric-label { font-size: 0.8rem; color: var(--theme-text-muted); } +#opportunities .mo-metric-value { font-size: 1.65rem; font-weight: 700; font-variant-numeric: tabular-nums; } +#opportunities .mo-metric small { font-size: 0.75rem; color: var(--theme-text-muted); } +#opportunities .mo-notice { + margin: 0 0 1rem; + padding: 0.8rem 1rem; + border-left: 3px solid var(--brand-primary); + background: color-mix(in srgb, var(--brand-primary) 7%, var(--theme-surface)); + border-radius: 0 var(--radius-sm) var(--radius-sm) 0; + font-size: 0.875rem; +} +#opportunities .mo-notice-warning { + border-left-color: var(--warning); + background: color-mix(in srgb, var(--warning) 8%, var(--theme-surface)); +} +#opportunities .mo-table-scroll { + overflow-x: auto; + max-width: 100%; + border: 1px solid var(--theme-border); + border-radius: var(--radius-md); + margin: 1rem 0; +} +#opportunities .mo-table-scroll:focus-visible, +#opportunities summary:focus-visible { outline: 2px solid var(--brand-primary); outline-offset: 2px; } +#opportunities .mo-table { width: 100%; border-collapse: collapse; font-size: 0.84rem; } +#opportunities .mo-table caption { + text-align: left; + padding: 0.7rem 1rem; + color: var(--theme-text-muted); + background: var(--theme-surface); + font-size: 0.78rem; +} +#opportunities .mo-table th { + text-align: left; + font-size: 0.74rem; + font-weight: 700; + color: var(--theme-text-muted); + background: var(--theme-surface-elevated, var(--theme-bg-subtle, var(--theme-surface))); + padding: 0.85rem 1rem; + border-top: 1px solid var(--theme-border); + border-bottom: 1px solid var(--theme-border); +} +#opportunities .mo-table td { + padding: 0.9rem 1rem; + border-bottom: 1px solid var(--theme-border); + vertical-align: top; + line-height: 1.45; +} +#opportunities .mo-table tbody tr:last-child td { border-bottom: 0; } +#opportunities .mo-table tbody tr:nth-child(even) { background: color-mix(in srgb, var(--theme-text) 3%, var(--theme-surface)); } +#opportunities .mo-table tbody tr:hover { background: var(--theme-surface-hover, var(--theme-bg-subtle)); } +#opportunities .mo-table .mo-number { text-align: right; font-variant-numeric: tabular-nums; white-space: nowrap; } +#opportunities .mo-stack { display: flex; flex-direction: column; gap: 0.3rem; min-width: 110px; } +#opportunities .mo-stack small { color: var(--theme-text-muted); font-size: 0.76rem; } +#opportunities .mo-badge { + display: inline-flex; + align-self: flex-start; + border-radius: 999px; + padding: 0.2rem 0.6rem; + font-size: 0.72rem; + font-weight: 600; + white-space: nowrap; + background: color-mix(in srgb, var(--theme-text-muted) 12%, var(--theme-surface)); + color: var(--theme-text); +} +#opportunities .mo-badge-info { color: var(--brand-primary); background: color-mix(in srgb, var(--brand-primary) 12%, var(--theme-surface)); } +#opportunities .mo-badge-warning { background: color-mix(in srgb, var(--warning) 14%, var(--theme-surface)); } +#opportunities .mo-source-link { color: var(--brand-primary); font-weight: 600; white-space: nowrap; text-decoration: underline; text-underline-offset: 3px; } +#opportunities .mo-details { border: 1px solid var(--theme-border); border-radius: var(--radius-md); margin-top: 0.75rem; } +#opportunities .mo-details > summary { cursor: pointer; padding: 0.85rem 1rem; font-weight: 600; font-size: 0.85rem; } +#opportunities .mo-details[open] > summary { border-bottom: 1px solid var(--theme-border); } +#opportunities .mo-details-body { padding: 0.25rem 1rem 0.75rem; } +#opportunities .mo-details-body p { font-size: 0.84rem; overflow-wrap: anywhere; } +#opportunities td .mo-details { min-width: 150px; margin: 0.5rem 0 0; } +#opportunities td .mo-details > summary { padding: 0.5rem; font-size: 0.76rem; } +#opportunities td .mo-details-body { padding: 0.25rem 0.5rem; } +#opportunities .mo-selector { display: flex; flex-wrap: wrap; align-items: center; gap: 0.75rem; font-size: 0.875rem; margin-top: 1rem; } +#opportunities input[type=number], #opportunities select { + padding: 0.5rem 0.75rem; + border: 1px solid var(--theme-border); + border-radius: var(--radius-sm); + color: var(--theme-text); + background: var(--theme-surface); +} +#opportunities form > label { margin: 1rem 0; line-height: 1.5; } +#opportunities form input[type=number] { width: 9rem; max-width: 100%; box-sizing: border-box; } +@media (max-width: 768px) { + #opportunities .dashboard-card { padding: 1rem; } + #opportunities .mo-metrics { grid-template-columns: repeat(2, minmax(0, 1fr)); } + #opportunities .mo-metric { padding: 0.75rem; } + #opportunities .mo-metric-value { font-size: 1.4rem; } + #opportunities .mo-details-body { padding-left: 0.75rem; padding-right: 0.75rem; } + #opportunities .mo-selector select { width: 100%; } +} diff --git a/static/css/risk-dashboard.css b/static/css/risk-dashboard.css index 61cda530..b49ce865 100644 --- a/static/css/risk-dashboard.css +++ b/static/css/risk-dashboard.css @@ -2660,3 +2660,10 @@ grid-template-columns: repeat(2, 1fr); } } +/* Live data can widen the Risk header and grid unless their flex/grid minima are bounded. */ +@media (max-width: 768px) { + .page-header { flex-wrap: wrap; } + .header-controls { flex-wrap: wrap; min-width: 0; max-width: 100%; } + .dashboard-layout { grid-template-columns: minmax(0, 1fr); } + .dashboard-layout .main-content { min-width: 0; width: 100%; max-width: 100%; } +} diff --git a/static/cycle-analysis.html b/static/cycle-analysis.html index 2b801834..411f9481 100644 --- a/static/cycle-analysis.html +++ b/static/cycle-analysis.html @@ -3,7 +3,8 @@ - Bitcoin Cycle Analysis - Precision Model + + Bitcoin Cycle Analysis - Heuristic Diagnostic @@ -46,7 +47,7 @@ } .wrap { - max-width: 95vw; + max-width: none; margin: 0 auto; padding: var(--space-lg); } @@ -294,6 +295,7 @@ +

Cycle analysis is a heuristic diagnostic. Historical fitting and reconstructed cycles are retrospective; they do not validate a future price forecast.

@@ -330,7 +332,7 @@

Analysis Controls

-

Model Precision Metrics

+

Retrospective Heuristic Fit

@@ -338,9 +340,9 @@

Position in Cycle 4

- Based on the ~4-year halving cycle. Historical pattern: halving → peak ≈ 546 days, peak → bottom ≈ 12 months. + Heuristic cycle timing is not a validated future-phase forecast.

-
+
Loading observed cycle position...
@@ -370,13 +372,13 @@

- Performance normalized to 100 at halving. Diminishing returns pattern: Cycle 2 x30, Cycle 3 x8, Cycle 4 x2. + Verified daily closes normalized to the halving close. Each cycle uses its own observed halving close and provider: USD reference rates for completed cycles, USDT exchange closes for the current cycle.

@@ -386,39 +388,18 @@

Cycle Anatomy — Historical Comparison

- - - - - - - - - - - - - - - - - - - - - -
Cycle 2Cycle 3Cycle 4 (current)
HalvingJul 2016May 2020Apr 2024
Halving price$650$9,500$64,000
PeakDec 2017Nov 2021TBD
Peak price$19,700$69,000TBD
Return halving→peak+2,930%+702%TBD
Halving → Peak526 days546 days~546 days?
BottomDec 2018Nov 2022?
Bottom price$3,200$16,500?
Max drawdown-84%-78%In progress
Peak → Bottom~12 months~12 months?
+
Unavailable: historical cycle prices and returns require a verified dataset.

- Cycle 4 peak/bottom not yet confirmed. Approximate monthly prices used for comparison charts. + Observed peaks are retrospective. The current cycle remains incomplete; these statistics do not predict its end.

-

Visual Validation - Predictions vs Reality

+

Retrospective Comparison - Heuristic Score vs Curated Events

- +
@@ -435,7 +416,7 @@

Error Actual Score Model Score - Accuracy + Historical fit index @@ -664,7 +645,7 @@

80 ? 'good' : overallAccuracy > 60 ? 'warning' : 'error'}"> - Overall Accuracy: - ${overallAccuracy.toFixed(1)}% + Historical fit index (not forecast accuracy): + ${overallAccuracy.toFixed(1)}/100
Cycles Analyzed: @@ -1067,339 +1048,70 @@

d.toLocaleDateString('en-US', { month: 'short', year: 'numeric' }); - const daysToATH = Math.round((data.athDate - HALVING_DATE) / 86400000); - const retHalvPeak = Math.round((data.ath / (data.bimonthly[0] || 64000) - 1) * 100); - const monthsPostPeak = ((Date.now() - data.athDate.getTime()) / (1000 * 60 * 60 * 24 * 30.44)).toFixed(0); - - tbody.innerHTML = ` -
HalvingJul 2016May 2020Apr 2024
-
Halving price$650$9,500$${(data.bimonthly[0] || 64000).toLocaleString()}
-
PeakDec 2017Nov 2021${fmtDate(data.athDate)}
-
Peak price$19,700$69,000$${data.ath.toLocaleString()}
-
Return halving→peak+2,930%+702%+${retHalvPeak}%
-
Halving → Peak526 days546 days${daysToATH} days
-
Current price——$${data.currentPrice.toLocaleString()}
-
Drawdown from peak-84% (final)-76% (final)${Math.round(data.drawdown * 100)}% (M+${monthsPostPeak})
-
BottomDec 2018Nov 2022?
-
Peak → Bottom~12 months~12 monthsIn progress
- `; - } - - // ========== CYCLE COMPARISON RENDERING ========== - function renderCyclePosition() { - const container = document.getElementById('cycle-position'); - if (!container) return; - - const fmtDate = d => d ? d.toLocaleDateString('en-US', { month: 'short', year: 'numeric' }) : '?'; - const athDate = cycle4State.athDate || new Date(); - // Estimate future phases based on historical pattern (bottom ~12 months post-peak) - const estBearEnd = new Date(athDate); estBearEnd.setMonth(estBearEnd.getMonth() + 10); - const estBottom = new Date(athDate); estBottom.setMonth(estBottom.getMonth() + 12); - const estReaccum = new Date(athDate); estReaccum.setMonth(estReaccum.getMonth() + 15); - - const phases = [ - { phase: 'Accumulation', time: 'Nov 2022 → Mar 2024' }, - { phase: 'Pre-halving Rally', time: 'Jan → Apr 2024' }, - { phase: 'Post-halving Consolidation', time: 'Apr → Oct 2024' }, - { phase: 'Bull Run / Blow-off Top', time: `Nov 2024 → ${fmtDate(athDate)}` }, - { phase: 'Distribution / Early Bear', time: `${fmtDate(athDate)} → now` }, - { phase: 'Bear Market / Capitulation', time: `est. → ~${fmtDate(estBearEnd)}` }, - { phase: 'Bottom / Re-accumulation', time: `est. ~${fmtDate(estBottom)} → ${fmtDate(estReaccum)}` }, - ]; - - const active = cycle4State.currentPhase; - - container.innerHTML = phases.map((p, i) => { - const status = i < active ? 'done' : i === active ? 'active' : 'future'; - const icon = status === 'done' ? "OK" : status === 'active' ? '▸' : '○'; - const suffix = status === 'active' ? ' ← HERE' : ''; - const pct = status === 'done' ? 100 : status === 'active' ? 50 : 0; - return ` -
-
${icon}
-
-
- ${p.phase}${suffix} - ${p.time} -
-
-
-
-
-
`; - }).join(''); - } - - function createComparisonChart() { - const ctx = document.getElementById('comparison-chart'); - if (!ctx) return; - const existing = Chart.getChart(ctx); - if (existing) existing.destroy(); - - const datasets = ['cycle2', 'cycle3', 'cycle4'].map(key => { - const c = CYCLE_COMPARE[key]; - if (!c.prices.length) return null; - return { - label: c.label, - data: c.prices.map((p, i) => ({ x: CYCLE_COMPARE.months[i], y: Math.round(p / c.halvingPrice * 100) })), - borderColor: c.color, - borderWidth: key === 'cycle4' ? 3 : 2, - pointRadius: key === 'cycle4' ? 4 : 3, - pointBackgroundColor: c.color, - fill: false, tension: 0.3 - }; - }).filter(Boolean); - - new Chart(ctx, { - type: 'line', - data: { datasets }, - options: { - responsive: true, maintainAspectRatio: false, - scales: { - x: { type: 'linear', title: { display: true, text: 'Months after Halving' }, min: 0, max: 30, ticks: { stepSize: 2, callback: v => `M+${v}` } }, - y: { title: { display: true, text: 'Performance (base 100 at halving)' }, min: 0 } - }, - plugins: { tooltip: { callbacks: { label: c => `${c.dataset.label}: ${c.parsed.y}%` } } } + position.append(timeline); + const current=result.cycles.find(c=>c.cycle===4); + if(current?.latest_close){ + const facts=document.createElement('p');facts.textContent=`Observed price: ${current.latest_close.toLocaleString('en-US',{maximumFractionDigits:2})} ${current.quote_unit}. Highest close so far: ${current.observed_peak.toLocaleString('en-US',{maximumFractionDigits:2})} (${current.peak_date}); ${current.days_since_observed_peak} days since that close. Drawdown from that high: ${(current.drawdown_from_peak*100).toFixed(1)}%. Return from the halving close: ${(current.return_since_halving*100).toFixed(1)}%.`;position.append(facts); } - }); - } - - function createDrawdownComparisonChart() { - const ctx = document.getElementById('drawdown-chart'); - if (!ctx) return; - const existing = Chart.getChart(ctx); - if (existing) existing.destroy(); - - const datasets = ['cycle2', 'cycle3', 'cycle4'].map(key => { - const c = DRAWDOWN_COMPARE[key]; - if (!c.prices.length) return null; - return { - label: c.label, - data: c.prices.map((p, i) => ({ x: DRAWDOWN_COMPARE.months[i], y: Math.round((p - c.peakPrice) / c.peakPrice * 100) })), - borderColor: c.color, - borderWidth: key === 'cycle4' ? 3 : 2, - pointRadius: key === 'cycle4' ? 4 : 3, - pointBackgroundColor: c.color, - fill: false, tension: 0.3 - }; - }).filter(Boolean); - - new Chart(ctx, { - type: 'line', - data: { datasets }, - options: { - responsive: true, maintainAspectRatio: false, - scales: { - x: { type: 'linear', title: { display: true, text: 'Months after Peak' }, min: 0, max: 12, ticks: { stepSize: 1, callback: v => v === 0 ? 'Peak' : `M+${v}` } }, - y: { title: { display: true, text: 'Drawdown from Peak (%)' }, min: -90, max: 5 } - }, - plugins: { tooltip: { callbacks: { label: c => `${c.dataset.label}: ${c.parsed.y}%` } } } + for (const [id, key, title] of [['comparison-chart', 'normalized', 'Multiple of halving close'], ['drawdown-chart', 'drawdown', 'Drawdown (fraction)']]) { + const existing = Chart.getChart(id); if (existing) existing.destroy(); + new Chart(document.getElementById(id), { type: 'line', data: { datasets: result.cycles.map((cycle,index) => { + const points=cycle.points.filter(point=>Number.isFinite(point[key])); + return {label: `Cycle ${cycle.cycle} · ${points.length ? cycle.coverage.toLowerCase() : 'unavailable: no verified '+(key==='normalized'?'halving close':'observations')}`, + data: points.map(point => ({ x: point.day, y: point[key] })), + borderColor: ['#a855f7','#3b82f6','#f59e0b','#22c55e'][index%4], pointRadius: 0, borderWidth: 2}; + })}, options: { responsive: true, maintainAspectRatio: false, parsing: false, + scales: { x: { type: 'linear', title: { display: true, text: 'Observed days since halving' } }, + y: { type:key==='normalized'?'logarithmic':'linear', title: { display: true, text: title } } } } }); } - }); - } - - window.switchCycleTab = function(tab) { - document.querySelectorAll('.cycle-tab').forEach(t => t.classList.remove('active')); - document.querySelector(`[onclick="switchCycleTab('${tab}')"]`)?.classList.add('active'); - document.getElementById('compare-panel').style.display = tab === 'compare' ? '' : 'none'; - document.getElementById('drawdown-panel').style.display = tab === 'drawdown' ? '' : 'none'; - if (tab === 'drawdown' && !Chart.getChart(document.getElementById('drawdown-chart'))) { - createDrawdownComparisonChart(); + const table = document.getElementById('cycle-anatomy-table'); + table.replaceChildren(); + const header=document.createElement('thead');const headerRow=document.createElement('tr'); + for(const text of ['Observed metric',...result.cycles.map(c=>`Cycle ${c.cycle} · ${c.coverage}`)]){const cell=document.createElement('th');cell.textContent=text;headerRow.append(cell);}header.append(headerRow);table.append(header); + const body=document.createElement('tbody');table.append(body); + const price=(v,c)=>Number.isFinite(v)?`${v.toLocaleString('en-US',{maximumFractionDigits:2})} ${c.quote_unit}`:'Unavailable'; + const pct=v=>Number.isFinite(v)?(v*100).toFixed(1)+'%':'Unavailable'; + const metrics=[['Halving date',c=>c.halving],['Observed period',c=>c.start?`${c.start} – ${c.end}`:'Unavailable'],['Halving close',c=>price(c.halving_close,c)],['Highest observed close',c=>price(c.observed_peak,c)],['Observed peak date (retrospective)',c=>c.peak_date||'Unavailable'],['Days from halving to observed peak',c=>c.days_to_observed_peak??'Unavailable'],['Lowest close after observed peak',c=>price(c.lowest_after_peak,c)],['Date of lowest close after peak',c=>c.lowest_after_peak_date||'Unavailable'],['Maximum running drawdown',c=>pct(c.maximum_drawdown)],['Last observed close',c=>price(c.latest_close,c)],['Drawdown from observed peak at period end',c=>pct(c.drawdown_from_peak)],['Return from halving close',c=>pct(c.return_since_halving)],['Provider',c=>c.provider||'Unavailable']]; + for(const [label,read] of metrics){const row=document.createElement('tr');for(const text of [label,...result.cycles.map(read)]){const cell=document.createElement('td');cell.textContent=text;row.append(cell);}body.append(row);} + const provenance = document.createElement('caption'); provenance.textContent = `${result.provider} · ${result.dataset_id} · ${result.reason}`; table.append(provenance); + } catch (error) { + position.textContent = `Unavailable: ${error.message}`; + const cell=document.querySelector('#cycle-anatomy-table td'); if(cell) cell.textContent = `Unavailable: ${error.message}`; } - }; - - async function initCycleComparisons() { - // Show loading state - const posEl = document.getElementById('cycle-position'); - if (posEl) posEl.innerHTML = '
Loading live BTC data...
'; - - // Fetch live data, then render - await loadDynamicCycleData(); - renderCyclePosition(); - createComparisonChart(); } // Fonctions principales @@ -1416,25 +1128,19 @@

75 ? 'good' : 'warning'); - addResult("Cycles Validated", `${metrics.completedCycles.length} historical cycles analyzed`, 'good'); + addResult("Curated Cycles Compared", `${metrics.completedCycles.length} historical cycles analyzed`, 'good'); if (metrics.overallAccuracy < 70) { - addResult("[Warning] Recommendation", 'Low accuracy - Consider recalibration', 'warning'); + addResult("[Warning] Recommendation", 'Low historical fit; future forecasting remains unvalidated', 'warning'); } } catch (error) { debugLogger.error('Error in runFullAnalysis:', error); @@ -1462,16 +1168,8 @@

{ - const alternatives = [ - { name: 'Gompertz', precision: 78.5, params: 'a=100, b=0.8, c=0.15' }, - { name: 'Logistique Double', precision: 82.1, params: 'k1=1.2, k2=0.9, x0=18' }, - { name: 'Weibull Modifiée', precision: 79.3, params: 'α=2.1, β=24, γ=0.1' }, - { name: 'Sigmoïde Actuel', precision: moduleLoaded ? calculatePrecisionMetrics().overallAccuracy : 75.0, params: 'Current parameters' } - ]; - - const best = alternatives.reduce((a, b) => a.precision > b.precision ? a : b); - - alternatives.forEach(alt => { - addResult(`Analytics ${alt.name}`, `Précision: ${alt.precision.toFixed(1)}% (${alt.params})`, - alt === best ? 'good' : 'info'); - }); - - addResult("Best Model", `${best.name} avec ${best.precision.toFixed(1)}% accuracy`, 'good'); - }, 1500); + addResult("Unavailable", "Alternative cycle functions have no independent evaluation. No comparison scores are published.", "warning"); }; window.generateReport = function () { - const metrics = moduleLoaded ? calculatePrecisionMetrics() : { overallAccuracy: 0, completedCycles: [], avgPeakError: 0, avgBottomError: 0 }; + const metrics = moduleLoaded ? calculatePrecisionMetrics() : null; + if (!metrics) { addResult("Unavailable", "No heuristic observations available", "warning"); return; } const report = ` # Bitcoin Cycle Model Analysis Report ## Executive Summary -- **Overall Accuracy**: ${metrics.overallAccuracy.toFixed(1)}% +- **Retrospective heuristic fit index**: ${metrics.overallAccuracy.toFixed(1)}/100 +- **Validation**: Not a future price forecast; fitted to manually curated historical events - **Cycles Analyzed**: ${metrics.completedCycles.length}/3 complete cycles - **Average Peak Error**: ${metrics.avgPeakError.toFixed(1)} points - **Average Bottom Error**: ${metrics.avgBottomError.toFixed(1)} points ## Recommendations -${metrics.overallAccuracy > 80 ? "[OK] Model performing well - Maintain current parameters" : "[Warning] Insufficient accuracy - Recalibration recommended"} +${metrics.overallAccuracy > 80 ? "[OK] Historical heuristic fit only; no future forecast is validated" : "[Warning] Low historical heuristic fit; no future forecast is validated"} ## Suggested Complementary Indicators 1. MVRV Ratio (High priority) diff --git a/static/global-config.js b/static/global-config.js index bca94978..7a846429 100644 --- a/static/global-config.js +++ b/static/global-config.js @@ -750,7 +750,8 @@ const balanceCache = { * Fonction centralisée pour charger les données de balance selon la source configurée */ window.loadBalanceData = async function (forceRefresh = false) { - const dataSource = globalConfig.get('data_source'); + const { ensureSelectedSource } = await import('./core/selected-source.js'); + const dataSource = await ensureSelectedSource(); const apiBaseUrl = globalConfig.get('api_base_url'); const currentUser = localStorage.getItem('activeUser'); diff --git a/static/market-regimes.html b/static/market-regimes.html index 8d39e3ba..4fa96c66 100644 --- a/static/market-regimes.html +++ b/static/market-regimes.html @@ -54,6 +54,18 @@ } .main-content { transition: opacity 0.3s; + min-width: 0; + } + .dashboard-layout, .tab-content, .tab-pane, .ml-card, .card-content { + min-width: 0; + } + .card-header, .card-actions, .header-controls { + flex-wrap: wrap; + gap: 0.75rem; + } + .card-title { + min-width: 0; + overflow-wrap: anywhere; } @@ -132,7 +144,7 @@

Market Regimes

-
+

Descriptive regime diagnostics. HMM state probabilities are not future-rise probabilities. Reconstructed timelines are retrospective.

@@ -168,8 +180,8 @@

Market Regimes

-- -- - HMM Only - DD ≤ -15%, 30d + Unavailable + Drawdown / MA200 rules ₿ Bitcoin @@ -196,8 +208,8 @@

Market Regimes

Key Differences:
    -
  • Stock Market: Pure HMM statistical model, traditional thresholds
    • -
  • Bitcoin & Ethereum: Hybrid Rule-Based + HMM, crypto-adjusted thresholds (3x higher volatility)
    • +
  • Stock Market: Economic rule diagnostics; experimental HMM states are shown separately
    • +
  • Bitcoin & Ethereum: Economic rules with crypto thresholds; state probabilities are not forecasts
  • Why Different? Crypto volatility (60-100% annual) vs Stocks (15-25%) requires distinct detection logic
@@ -209,7 +221,7 @@

Market Regimes

- Stock Market Regime Detection (HMM) + Stock Market Regime Diagnostics (Rules) Hidden Markov Model @@ -223,18 +235,18 @@
Current Regime
Loading...
- HMM Detection + Economic rule diagnostic
Confidence
--
- Detection Certainty + State posterior only; no future-rise probability
Model
-
HMM
+
Rule-Based
Hidden Markov Model
@@ -268,29 +280,29 @@
Bear Threshold
Bear Market - DD ≤ -15%, 30d+ + Drawdown from 200-day high ≤ -20%
Correction - DD 10-15% + Vol >30% + Drawdown ≤ -10% or price below MA200
Bull Market - Price > MA200, Vol <25% + Remaining cases after bear, correction and expansion rules
Expansion - Recovery +15%/mo + Price more than 20% above MA200
- About Stock Market Detection: + About Descriptive stock regime rules:
    -
  • HMM Model: Statistical pattern recognition for market regimes
    • -
  • States: Bear Market, Correction, Bull Market, Expansion
    • -
  • Thresholds: Traditional stock market parameters (DD ≤ -15%, 30 days)
    • +
  • Method: Daily-close economic rules; descriptive diagnosis only
    • +
  • Rule categories: Bear Market, Correction, Bull Market, Expansion. HMM latent states remain A–D without verified economic mapping.
    • +
  • Precedence: Bear, Correction, Expansion, then Bull. Uses 200 daily observations.
  • Events: COVID crash, Fed policy changes annotated for context
@@ -301,9 +313,9 @@
Bear Threshold
- Bitcoin Regime Detection (Hybrid System) + Bitcoin Regime Diagnostics - Rule-Based + HMM Fusion + Economic rules + separate latent HMM states
@@ -315,13 +327,13 @@
Current Regime
Loading...
- Hybrid Detection + Descriptive diagnosis
Confidence
--
- Detection Certainty + State posterior only; no future-rise probability
@@ -329,7 +341,7 @@
Detection Method
--
- Rule-Based or HMM + Rules; HMM states are separate