00ae84dd88
Strip all 413 #[allow(dead_code)] annotations from 139 files and remove the actual dead code they were suppressing: unused struct fields (and their constructor sites), unused methods/functions, and entire dead structs. Key removals: - trading_engine compliance: ~50 dead structs/fields across audit, reporting, SOX modules - trading_service: dead execution engine fields, broker routing, paper trading methods - ml_training_service: dead TLS validation (~340 lines), GPU state, monitoring fields - backtesting_service: dead model cache, TLS validation, TradeSignal fields - risk: dead VaR engine fields, safety coordinator fields, position tracker fields - adaptive-strategy: dead ensemble methods, regime detection, sizing functions 147 files changed, -4264 net lines. Workspace compiles with 0 errors. 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.