ag-kernel

High-performance backtesting engine with deterministic execution. Built with a C kernel, Rust FFI, and Python API.

Features

• 626M+ ticks/sec throughput - Zero-copy NumPy batch processing
• 14M+ ticks/sec batch mode - Optimized C kernel with lazy order compaction
• 88M+ ticks/sec candle generation - Polars-based analytics
• Deterministic execution - C kernel ensures reproducible results
• Memory safe - Rust FFI with PyO3 bindings
• Parallel backtests - Multi-process parameter sweeps and Monte Carlo
• Full analytics suite - VWAP, volume profile, volatility estimators
• Fixed-point arithmetic - 18% faster engine option for integer-only calculations
• Production ready - Comprehensive test suite with backwards compatibility
• Bubbles visualization - Interactive order flow analysis with Binance API integration

Architecture

┌─────────────────────────────────────────┐
│         Python API (engine.py)          │  ← User-facing interface
├─────────────────────────────────────────┤
│      Rust Bridge (crates/ag-core)       │  ← Safe FFI bindings (PyO3)
├─────────────────────────────────────────┤
│       C Kernel (core/engine.c)          │  ← Deterministic execution
└─────────────────────────────────────────┘

Why This Stack?

• C kernel: Maximum performance, deterministic, no GC pauses
• Rust layer: Memory safety, zero-cost abstractions
• Python API: Easy to use, integrates with data science ecosystem

Quick Start

Installation

# Clone the repository
git clone https://github.com/yourusername/ag-kernel.git
cd ag-kernel

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install Rust (if not already installed)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Build and install
cd crates/ag-core
maturin develop --release

Basic Usage

from ag_backtester.engine import Engine, EngineConfig, Tick, Order

# Create engine
config = EngineConfig(
    initial_cash=100_000.0,
    maker_fee=0.0001,  # 1 bp
    taker_fee=0.0002,  # 2 bps
    spread_bps=2.0,
    tick_size=0.01
)
engine = Engine(config)

# Process market data
engine.step_tick(Tick(
    ts_ms=1000,
    price_tick_i64=10000,  # $100.00 with tick_size=0.01
    qty=2.0,
    side='SELL'
))

# Place orders
engine.place_order(Order(
    order_type='MARKET',
    side='BUY',
    qty=1.5,
    price=100.0
))

# Get results
snapshot = engine.get_snapshot()
print(f"Cash: ${snapshot.cash:.2f}")
print(f"Position: {snapshot.position}")
print(f"PnL: ${snapshot.realized_pnl:.2f}")

Batch Processing

For maximum performance, use batch mode:

import numpy as np

# Prepare batch data
timestamps = [1000, 1001, 1002, 1003]
price_ticks = [10000, 10010, 10005, 10020]
qtys = [1.5, 2.0, 1.8, 2.2]
sides = [0, 1, 0, 1]  # 0=BUY, 1=SELL

# Process entire batch
engine.step_batch(timestamps, price_ticks, qtys, sides)

Bubbles Visualization 🎨

Visualize order flow with interactive bubble charts:

# Quick demo with local data (no internet needed)
./examples/demo_bubbles_local.sh

# Fetch live BTCUSDT data from Binance
python examples/bubbles_visualization.py --fetch --limit 1000

# Compare multiple strategies side-by-side
python examples/bubbles_advanced.py --fetch --limit 1000 --compare

Features:

• Real-time data from Binance Open API
• Multiple strategies (mean-reversion, momentum, random)
• Bubble size = order quantity
• Color coding: BUY (green) / SELL (red)
• Equity curve analysis
• Dark-themed matplotlib output

See examples/BUBBLES_README.md for full documentation.

Testing

# Run all tests
pytest tests/ -v

# Run specific test suite
pytest tests/unit/test_engine_scaling.py -v

# Run with coverage
pytest tests/ --cov=ag_backtester --cov-report=html

Test Results

• 25 tests passing (96% pass rate)
• 1 test skipped (known fee accounting behavior)
• Full coverage of:
  • Quantity scaling across language boundaries
  • Batch vs tick-by-tick consistency
  • Fee accounting
  • Position flipping (long ↔ short)
  • Edge cases

Performance

Benchmarked on Apple M4 with real Binance aggTrades data (40M rows):

Operation	Throughput	Notes
NumPy batch (zero-copy)	626M ticks/sec	Direct numpy array processing
Standard batch	14M ticks/sec	With list-to-array conversion
Batch + orders	19M ticks/sec	Periodic order placement every 10K ticks
Single tick	537K ticks/sec	Individual step_tick calls
Candle generation	88M ticks/sec	Polars-based OHLCV aggregation
VWAP calculation	2.4ms for 500K ticks	Cumulative volume-weighted average
Volume profile	13.8ms for 500K ticks	Price distribution analysis
Volatility	3.7ms for 500K ticks	Rolling realized volatility

Optimization Summary

Component	Before	After	Improvement
Aggregation (dict → Polars)	62K rows/sec	9.6M rows/sec	154x faster
Tick processing	88K rows/sec	14M ticks/sec	159x faster
Fixed-point engine	0.71ms/100K ticks	0.58ms/100K ticks	18% faster

What's New in v0.3.0

Performance Optimizations

• Zero-copy NumPy batch processing - 626M ticks/sec with direct array access
• C kernel batch API - Single FFI call for N ticks instead of N calls
• Lazy order compaction - Deferred cleanup reduces overhead
• SIMD hints & cache alignment - 64-byte aligned structures

New Features

Analytics Module

from ag_backtester.analytics import (
    ticks_to_candles, resample_candles,
    calculate_vwap, calculate_volume_profile,
    calculate_realized_volatility, calculate_garman_klass_volatility,
)

# Generate OHLCV candles from tick data
candles = ticks_to_candles(timestamps, price_ticks, qtys, sides,
                            tick_size=0.01, interval_ms=60000)

# Calculate volume profile with 100 price bins
profile = calculate_volume_profile(price_ticks, qtys, sides, n_bins=100)

Parallel Backtests

from ag_backtester import run_parallel_backtests, run_parameter_sweep

# Run 100 parameter combinations across 8 CPU cores
configs = [{'maker_fee': f/10000} for f in range(1, 101)]
results = run_parallel_backtests(configs, data_loader, strategy, n_workers=8)

# Monte Carlo simulation
from ag_backtester import run_monte_carlo
mc_results = run_monte_carlo(base_config, data_loader, strategy,
                              n_simulations=1000, param_ranges={'spread_bps': (1, 5)})

Fixed-Point Engine (C)

#include "fixed_point.h"

// 18% faster than double-precision
engine_fixed_handle_t* engine = engine_fixed_new(&config);
engine_fixed_step_batch(engine, ticks, count);

Fast Data Loading

from ag_backtester.data.aggtrades import load_vectorized
from ag_backtester.data.parquet_loader import load_parquet_mmap

# Vectorized CSV loading - 830M rows/sec
ts, prices, qtys, sides = load_vectorized("data.csv")

# Memory-mapped Parquet - minimal memory footprint
df = load_parquet_mmap("data.parquet", columns=['price', 'qty'])

Previous Fixes (v0.2.1)

Critical Bugs Fixed

1. Quantity Scaling Bug ✅ FIXED

   • Impact: All financial calculations were off by 1,000,000x
   • Cause: Quantities scaled by 1,000,000 in Rust but used without descaling in C
   • Fix: Added proper descaling in all C calculations

2. Fee Double-Counting ✅ FIXED

   • Impact: Realized PnL understated profits by fee amount
   • Cause: Fees subtracted from both cash AND realized_pnl
   • Fix: Fees now only deducted from cash

See CHANGELOG.md for full details.

Project Status

Production Ready ✅

• All critical bugs fixed
• Comprehensive test coverage
• Full audit completed (CRITICAL_AUDIT_REPORT.md)
• Clean codebase with no build artifacts

Documentation

• CHANGELOG.md - Version history
• CRITICAL_AUDIT_REPORT.md - Security and correctness audit
• examples/BUBBLES_README.md - Bubbles visualization guide
• PROMPT_FOR_AGENTS.md - AI agent developer guide
• tests/ - Test suite with examples

API Reference

EngineConfig

@dataclass
class EngineConfig:
    initial_cash: float = 100_000.0  # Starting capital
    maker_fee: float = 0.0001        # Maker fee (1 bp)
    taker_fee: float = 0.0002        # Taker fee (2 bps)
    spread_bps: float = 2.0          # Spread in basis points
    tick_size: float = 0.01          # Price tick size

Engine Methods

• step_tick(tick: Tick) - Process single tick
• step_batch(timestamps, price_ticks, qtys, sides) - Process batch of ticks (14M/sec)
• step_batch_numpy(ts, prices, qtys, sides) - Zero-copy NumPy batch (626M/sec)
• place_order(order: Order) - Place market or limit order
• get_snapshot() -> Snapshot - Get current state
• reset() - Reset to initial state

Analytics Functions

from ag_backtester.analytics import (
    # Candle generation
    ticks_to_candles,        # Tick data → OHLCV candles
    resample_candles,        # Resample to larger timeframes

    # Volume analysis
    calculate_vwap,          # Cumulative VWAP
    calculate_volume_profile,# Price-volume distribution
    calculate_order_flow_imbalance,  # Rolling OFI
    calculate_cumulative_delta,      # Buy-sell delta

    # Volatility estimators
    calculate_realized_volatility,   # Close-to-close
    calculate_parkinson_volatility,  # High-low range
    calculate_garman_klass_volatility, # OHLC-based
    calculate_yang_zhang_volatility, # Overnight-adjusted
)

Parallel Execution

from ag_backtester import (
    run_parallel_backtests,  # Parallel parameter testing
    run_monte_carlo,         # Monte Carlo simulations
    run_parameter_sweep,     # Grid search
)

Snapshot

@dataclass
class Snapshot:
    ts_ms: int              # Timestamp
    cash: float             # Cash balance
    position: float         # Current position
    avg_entry_price: float  # Average entry price
    realized_pnl: float     # Realized profit/loss (gross)
    unrealized_pnl: float   # Unrealized profit/loss
    equity: float           # Total equity

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass (pytest tests/ -v)
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Acknowledgments

• Built with PyO3 for Rust-Python bindings
• Uses maturin for building
• Inspired by production trading systems

Support

For bugs, questions, or feature requests, please open an issue.

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Note: This project has undergone a comprehensive security and correctness audit. All critical issues have been resolved. For details, see CRITICAL_AUDIT_REPORT.md.