Releases: vrrgithub1/stock-risk-engine
Release 6.0: Institutional Risk & AI Governance Framework
Title: AI Model Transparency Report: Stock Risk Engine (v6.0.0)
Reporting Period: Q1 2026
Status: ✅ CERTIFIED (In-Tolerance)
Model Health Score: 2.7% (Target: < 5.0%)
- Governance Framework Alignment
This model is developed and governed under the NIST Artificial Intelligence Risk Management Framework (AI RMF) and ISO/IEC 42001 standards. It prioritizes the core principles of Validity, Reliability, and Transparency.
• Accountability: 21+ years of institutional finance expertise applied to algorithmic risk oversight.
• Transparency: Full data provenance via Medallion Architecture (Bronze-Silver-Gold). - Performance Metric: The 95% VaR Health Score
The primary validation metric is the Violation Rate—the frequency at which actual market losses exceed the model's predicted floor.
• Target: < 5.0% (Industry Standard)
• Model Health Score: 2.7% (Target: < 5.0%)
• Conclusion: The model is currently "conservatively calibrated," providing a reliable safety margin for daily capital oversight.
Full Changelog: v5.0.0...v6.0.0
Stock Risk Engine v5.0 - Certification Complete: Validating Portfolio Risk in a Volatile Market
### Institutional Risk Engine - Phase V Performance Certification
Model Validation Results for 95% Confidence Value-at-Risk (VaR)
1. Executive Commentary
As of the March 18th launch, the engine has successfully completed its validation cycle. The primary performance metric—the Violation Rate—stands at 2.82%. This result is highly favorable, sitting safely below our 5.0% maximum risk tolerance. This confirms that the Monte Carlo simulation is effectively capturing market volatility and providing reliable downside protection across the portfolio.
2. Model Health Gauge
Violation Rate: 2.82%
Target Threshold: < 5.0%
Status: ✅ PASS – Optimal Calibration
3. Portfolio Coverage Summary
Total Observations: 51+ (Calculated based on the new 2.82% rate)
Asset Universe: 6 Key Tickers across 4 Sectors.
Data Reliability: Consistent performance across disparate volatility regimes.
v4.0.0 - Phase IV: Predictive Intelligence & Triple-Engine VaR
Release Title: v4.0.0 - Phase IV: Predictive Intelligence & Triple-Engine VaR
Description:
This release marks the transition from Phase III (MLOps Infrastructure) to Phase IV (The Brain). The engine now delivers institutional-grade risk analytics by shifting from raw data processing to predictive intelligence.
Key Implementations:
Triple-Engine VaR Framework:
Integrated Historical Simulation (252-day lookback), Parametric (CVaR), and Monte Carlo engines to mitigate model risk.
Predictive Beta Engine:
Deployed market sensitivity forecasting to identify idiosyncratic risk outliers.
Medallion Architecture (Gold Layer):
Established an immutable persistence layer for Gold Risk Inference and Gold Risk VaR Summary to enable regulatory-ready audit trails.
Performance Optimization:
Optimized Monte Carlo simulations to execute 1,000+ iterations in <70 seconds via GitHub Actions.
Production Parity:
Full Dockerization ensures 100% environment parity between local development and autonomous production runs.
Stock Risk Engine v3.0
Phase III: Stateless Containerized MLOpsPipeline
Automated Risk Orchestration & Scenario-Based Stress Testing
The objective of Phase III was to transition the Stock Risk Engine from a locally executed analytical script into a production-grade, Stateless Containerized MLOps Pipeline. This evolution ensures that risk insights are delivered with high reliability, absolute environment reproducibility, and rigorous data governance.
Key Technical Achievements:
•Full Pipeline Orchestration: Implemented a zero-touch automation framework using GitHub Actions, enabling scheduled daily execution (Cron) and event-driven triggers.
•Containerized Portability: Engineered a Docker-based execution model that isolates dependencies within a stateless Linux environment, eliminating "configuration drift" and ensuring 100% execution parity between development and production.
•Automated Scenario Analysis: Developed a parameterized reporting module that generates both baseline risk profiles and adversarial stress-tests ("Panic" scenarios) to quantify tail-risk sensitivity during market volatility.
•Immutable Data Governance: Established a robust audit trail by synchronizing over 6,800 historical inferences into a centralized database, supported by automated data retention and optimization policies.
•Dynamic Artifact Persistence: Optimized the CI/CD workflow to archive daily risk intelligence as immutable HTML assets, providing stakeholders with instant access to historical and predictive risk dashboards.
Architecture Flow
- Trigger: GitHub Actions (via cron or push).
- Infrastructure (The Wrapper): A Docker Container (Ubuntu/Python Image) that spins up.
- The Logic (Inside the Container):
• Python Engine: Processes data and runs ML models.
• SQLite DB: Ephemerally initialized within the container volume. - The Persistence (The Output): Reports are extracted from the container and saved as GitHub Artifacts.
Fig-1: ML-Ops Automation Pipeline Architecture
Stock Risk Engine v2.0
Stock Engine v2.0
Key Technical Milestones added in v2.0:
• 🏗️ Medallion Pipeline: Developed a structured ETL flow (Bronze/Silver/Gold) using Python and DuckDB/SQLite to ensure data lineage and integrity.
• 🤖 ML Intelligence Layer: Integrated a Random Forest Regressor to forecast Beta Drift, specifically identifying idiosyncratic risks that standard benchmarks miss.
• 📊 Contextual Analysis: Built a dynamic Market Regime classifier (Quiet | Standard | Stress) that automatically adjusts model feature weighting based on the VIX.
• ✅ Automated Quality Gates: Added a validation layer to perform sanity checks on financial logic before the final executive report is generated.
Initial Core Launch Release
Stock Risk Engine – Initial Release Notes
Overview
The Stock Risk Engine is a professional-grade financial data pipeline and analytics platform designed to analyze market volatility and asset sensitivity. Built with a robust Medallion Architecture on SQLite, it empowers quantitative analysts and portfolio managers to assess risk, forecast market behavior, and automate data workflows.
Key Features
Data Architecture
Three-Layer Data Lake: Implements Bronze, Silver, and Gold layers for structured data lineage and mathematical integrity.
Quantitative Analytics
Rolling Volatility & Beta: Calculates 30-day rolling volatility and rolling market beta (β) relative to the S&P 500.
Historical Stress Testing: Simulates “Maximum 5-Day Drawdown” for custom-weighted portfolios.
Pipeline Orchestration
Automated Data Ingestion: Seamlessly ingests equities (e.g., NVDA, TSLA) and macro indicators (e.g., VIX, Treasury Yields) via Python and yfinance API.
Tech Stack
Languages & Libraries: Python (Pandas, NumPy, SQLAlchemy, SciPy, StatsModels), SQLite, YAML-based configuration.
Getting Started
Clone the repository and review the README for setup instructions.
Configure your data sources and portfolio weights in the YAML config files.
Run the pipeline to generate risk metrics and forecasts.