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**Mission**: Close gap between Wave 126 "theoretical 100%" and operational readiness **Agent 118: Database Schema** ✅ - Created migration 020_create_executions_table.sql - Added executions table with 9 columns, 5 indexes - Foreign key to orders table with CASCADE - UNBLOCKED load testing (Agent 123) **Agent 119: GPU Docker Configuration** ✅ (USER PRIORITY) - Updated docker-compose.yml with NVIDIA runtime - Configured GPU environment variables for ML service - Verified RTX 3050 Ti accessible (nvidia-smi working) - CUDA 13.0 enabled in container - SATISFIED user requirement: "Ensure GPU is working in docker" **Agent 120: Prometheus HTTP Exporters** ⚠️ PARTIAL - Added Prometheus dependencies to all 4 services - Implemented /metrics endpoints with Axum HTTP servers - Services compiled and running healthy - ISSUE: HTTP endpoints not responding (needs investigation) **Agent 121: Test Fixes** ⚠️ PARTIAL - Fixed timing test in trading_engine (TSC availability check) - Trading engine: 100% pass rate (298/298) - NEW ISSUE: PPO continuous policy test failing (log probabilities) - Overall: 99.83% pass rate (574/575 in ml crate) **Wave 1 Results**: - Critical path: ✅ Database schema unblocked load testing - User requirement: ✅ GPU working in Docker - Monitoring: ❌ Prometheus needs fix - Testing: ⚠️ 99.83% pass rate (1 new failure) **Files Modified** (11): - migrations/020_create_executions_table.sql (new) - docker-compose.yml (GPU runtime) - services/*/src/main.rs (4 files - Prometheus exporters) - services/*/Cargo.toml (3 files - dependencies) - trading_engine/src/timing.rs (test fix) **Next**: Wave 2 - Execution Validation (6 agents) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <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.