a51fe0d30d
Phase 1: ML pipeline verification (checkpoint roundtrip tests, feature pipeline tests, DQN VarMap bug fix, deleted 585 lines dead code) Phase 2: Service production logic (real portfolio metrics, VaR positions, proto population, safetensors loading, shutdown handling) Phase 3: Backtesting & data (equity curve, DBN metadata, progress callbacks, cross-symbol validation, event filtering) Phase 4: ML crate TODOs (statrs t-distribution, quantization savings, safetensors header, microstructure features, 45-action masking, confidence EMA, AttentionMask) Phase 5: Infrastructure/cleanup (TLS/OCSP docs, compliance roadmap, metrics docs, regime features, execution roadmap, auth #[ignore], chaos docs) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Backtesting Service
Overview
The backtesting_service offers an independent and isolated environment for rigorously testing and validating trading strategies against historical market data. It provides a robust platform for simulating trading performance, analyzing strategy efficacy, and generating comprehensive performance reports before live deployment.
Features
- Independent Backtesting Service: Operates autonomously, allowing for parallel and isolated strategy evaluations.
- gRPC API for Backtest Execution: Exposes a clear API for submitting and managing backtesting jobs.
- Strategy Testing and Validation: Enables comprehensive testing of various trading strategies under different market conditions.
- Performance Reporting: Generates detailed reports including metrics like P&L, Sharpe ratio, drawdown, and win rate.
- Data Replay Engine: Accurately replays historical market data, simulating real-world order book dynamics and trade execution.
- Results Persistence: Stores backtesting results and reports for historical analysis and comparison.
gRPC API
The backtesting_service exposes a gRPC API for initiating and retrieving backtest results. Key endpoints include:
RunBacktest- Submit backtest configuration and strategyGetBacktestResults- Retrieve results for completed backtestsListAvailableStrategies- List registered strategiesGetBacktestReport- Get detailed performance report
Running the service
To run the backtesting_service binary:
cargo run --bin backtesting_service
Data Requirements
The service requires historical market data in Parquet format:
- Data should be stored in the configured data directory
- Supports tick data, order book snapshots, and OHLCV candles
- Data must include instrument, timestamp, and price/quantity fields
Testing
To run the tests for the backtesting_service crate:
cargo test --package backtesting_service
Documentation
Comprehensive API documentation is available at docs.rs/backtesting_service.