Files

jgrusewski 41effb1450 fix: Remove hardcoded CUDA features from Docker builds

- Make candle-core CUDA features optional (not hardcoded) in ml/Cargo.toml
- Add CUDARC_CUDA_VERSION=13000 to skip nvcc detection in Dockerfiles
- Add CUDA_COMPUTE_CAP=86 to skip nvidia-smi GPU detection
- Remove invalid --features cuda from ml_training_service build

FIXES:
- Trading Service: nvidia-smi failed (candle-kernels build)
- Backtesting Service: nvidia-smi failed (candle-kernels build)
- ML Training Service: Wrong feature flag (cuda doesn't exist on service)

IMPACT:
- Services build without CUDA toolchain requirements
- CUDA still available at runtime via nvidia/cuda base images
- GPU auto-detected by candle when running with --gpus all

BUILD RESULTS:
- API Gateway: ✅ 119MB
- Trading Service: ✅ 119MB (3m 36s build)
- Backtesting Service: ✅ 120MB (3m 31s build)
- ML Training Service: 🟡 IN PROGRESS (CUDA base image ~1.6GB)

Wave 121 - Docker CUDA Build Fixes

2025-10-07 20:23:40 +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

🚀 MAJOR UPDATE: Multi-Agent System Analysis & Infrastructure Improvements

2025-09-26 11:02:46 +02:00

Dockerfile.production

🚀 MAJOR UPDATE: Multi-Agent System Analysis & Infrastructure Improvements

2025-09-26 11:02:46 +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.