Files

jgrusewski aa130e9554 feat(backtesting_service): wire equity curve total_count and add stop_backtest logging

- Replace hardcoded `total_count: 0` with `summaries.len() as u32` in list_backtests
- Add tracing::warn! in get_backtest_results when equity_curve is returned empty
- Add tracing::warn! in stop_backtest noting task cancellation is not yet implemented
- Import `warn` from tracing alongside existing debug/error/info imports

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-02-21 23:33:14 +01:00

benches

fix(clippy): Fix 6 unwrap_used violations in risk/data

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examples

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migrations

🔐 Wave 149 Phase 1-2: JWT Whitespace + Database Schema (Agents 411-412)

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results

fix(tests): Resolve remaining 13 test failures via parallel agents

2025-10-20 10:43:10 +02:00

src

feat(backtesting_service): wire equity curve total_count and add stop_backtest logging

2026-02-21 23:33:14 +01:00

tests

fix(ci): Fix GitLab CI YAML syntax and PPOConfig compilation errors

2025-10-31 00:20:00 +01:00

ADVANCED_QUERIES_QUICKREF.md

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build.rs

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Dockerfile

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README.md

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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.