436ddbd589
Applied Agent W4 patterns to services (api_gateway, trading_service, backtesting_service, ml_training_service): Fixed Patterns: - Pattern 1: current_dir().unwrap() → expect() (1 fix) - Pattern 2: duration_since().unwrap() → expect() (2 fixes) - Pattern 3: Collection.first/last().unwrap() → expect() (5 fixes) - Pattern 5: serde_json operations → expect() (3 fixes) - Pattern 6: Duration::from_std().unwrap() → expect() (2 fixes) - Pattern 7: handle.join().unwrap() → expect() (1 fix) - Pattern 8: .first()/.last() → expect() (11 fixes) - Pattern 16: String::from_utf8() → expect() (8 fixes) - Pattern 19: partial_cmp().unwrap() → unwrap_or(Equal) (9 fixes) - Pattern 22: SystemTime operations → expect() (1 fix) Total: 43 violations fixed All services compile successfully with zero errors Agent: W17 Phase: Clippy Bulk Fixes (Services) Related: AGENT_W4_CLIPPY_PATTERNS.md
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.