7f53baff8f
DQN changes: improved attention, ensemble networks, hindsight replay, mixed precision, noisy layers, prioritized replay, RMSNorm, hyperopt adapter updates, and trainer enhancements with weight_decay support. Fix downstream crates broken by DQNConfig changes: - trading_service: import agent::DQNConfig directly, add weight_decay field - backtesting_service: update feature vector size 54 -> 51 - ml_training_service: convert compile-time sqlx macro to runtime query_as - pre-commit hook: add SQLX_OFFLINE=true for DB-free compilation 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.