Files

jgrusewski d88eaf0a7e 🐛 Fix Docker builds: Update Rust 1.75→1.83 for edition2024 support

- Rust 1.75 (Nov 2023) too old for base64ct-1.8.0 dependency
- base64ct requires edition2024 features not in Cargo 1.75
- Local system uses Rust 1.89, need Docker parity
- Updated all 6 Dockerfile variants across 3 services

Fixes:
- ML training service Docker build
- Trading service Docker build
- Backtesting service Docker build

Related: Wave 125 Phase 3B Docker deployment

2025-10-07 22:01:06 +02:00

migrations

🚀 PRODUCTION READY: Foxhunt HFT Trading System v1.0

2025-09-24 23:47:21 +02:00

src

🚀 Wave 122 Complete: Deployment Readiness Validated

2025-10-07 14:25:46 +02:00

tests

🚀 Wave 122 Complete: Deployment Readiness Validated

2025-10-07 14:25:46 +02:00

build.rs

🚀 Wave 119 Complete: 11 Agents - 202 Tests Added, 58-60% Coverage

2025-10-07 00:42:57 +02:00

Cargo.toml

🚀 Wave 122 Complete: Deployment Readiness Validated

2025-10-07 14:25:46 +02:00

Dockerfile

fix: Remove hardcoded CUDA features from Docker builds

2025-10-07 20:23:40 +02:00

Dockerfile.dev

🐛 Fix Docker builds: Update Rust 1.75→1.83 for edition2024 support

2025-10-07 22:01:06 +02:00

Dockerfile.production

🐛 Fix Docker builds: Update Rust 1.75→1.83 for edition2024 support

2025-10-07 22:01:06 +02:00

README.md

🔧 Tonic 0.14 Upgrade: Auto-generated and build system changes

2025-10-03 07:34:26 +02:00

README.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 strategy
GetBacktestResults - Retrieve results for completed backtests
ListAvailableStrategies - List registered strategies
GetBacktestReport - 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.