Files
foxhunt/services/backtesting_service
jgrusewski 633435fc6f fix(ml): Fix varmap scale/zero_point preservation test
- Add .get(0)? before .to_scalar() for scale extraction (line 605)
- Add .get(0)? before .to_scalar() for zero_point extraction (line 624)
- Handles [1] shape tensors from Tensor::new(&[value], device)
- Fixes test_quantization_preserves_scale_and_zero_point
- Ensures reliable SafeTensors save/load round-trip
2025-10-23 13:36:34 +02:00
..

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.