Files

jgrusewski 680c12d2c0 refactor: delete 4,800 lines of dead ML code and remove MLFeatureExtractor dependency

- Delete push_metrics.rs (Pushgateway client, zero consumers)
- Delete 5 legacy test/bench files for MLFeatureExtractor + SimpleDQNAdapter
- Remove MLFeatureExtractor from trading_agent_service, use direct bar scoring
- Remove with_feature_extractor() method and Arc<MLFeatureExtractor> parameter
- Remove bench target from common/Cargo.toml

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

2026-03-01 18:29:43 +01:00

benches

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

2025-10-23 14:58:32 +02:00

docs

Download 360 DBN files (36.3 MB) using Rust databento client

2025-10-13 13:30:02 +02:00

migrations

fix: comprehensive audit — real brokers, deployment fixes, production safety

2026-02-25 00:32:10 +01:00

src

feat(services): wire gRPC metrics Tower layer into all 7 gRPC services

2026-03-01 02:30:23 +01:00

tests

refactor: delete 4,800 lines of dead ML code and remove MLFeatureExtractor dependency

2026-03-01 18:29:43 +01:00

ADVANCED_QUERIES_QUICKREF.md

Download 360 DBN files (36.3 MB) using Rust databento client

2025-10-13 13:30:02 +02:00

build.rs

refactor: restructure repo — crates/, bin/, testing/ layout

2026-02-25 11:56:00 +01:00

Cargo.toml

fix(stubs): implement 15 production stubs, fix routing, delete placeholders

2026-02-28 23:47:31 +01:00

DBN_REPOSITORY_USAGE.md

Download 360 DBN files (36.3 MB) using Rust databento client

2025-10-13 13:30:02 +02:00

Dockerfile

fix: comprehensive audit — real brokers, deployment fixes, production safety

2026-02-25 00:32:10 +01:00

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.