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## Final Metrics (Wave 99) - Compilation errors: 672 → 0 ✅ (100% resolution) - Test compilation: 489 → 0 ✅ (100% resolution) - Warnings: 313 → 124 (60% reduction, target was <50) ## Wave Timeline Wave 82-87: Source code errors (183→0) Wave 88-94: Test compilation (489→0) Wave 95: Import cleanup experiment Wave 96: Import restoration (26 errors fixed) Wave 97: Warning phase 1 (313→188, -40%) Wave 98: Warning phase 2 (188→124, -34%) Wave 99: Warning phase 3 (124→124, target not met) ## Major API Migrations (73+ files) - NewsEvent: 18-field structure with full metadata - ExecutionReport: filled_quantity→executed_quantity - Position: 16-field modernization (avg_cost, market_value, etc) - TradingOrder: account_id field added - TimeInForce: Abbreviated variants (GTC, IOC, FOK) ## Remaining Work - 124 warnings (non-critical: unused variables, dead code, deprecated APIs) - Most are cleanup/style issues, not correctness problems - Recommendation: Accept current state, prioritize test coverage (95% target) ## Production Status ✅ Wave 79 certified: 87.8% production ready ✅ Zero compilation errors maintained ✅ All services compile and tests runnable 🔄 Next: Test coverage measurement (95% target - CLAUDE.md requirement) Co-authored-by: Wave 82-99 Agents (40+ parallel agents deployed)
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.