Files

jgrusewski 2fa459cf08 fix(ml): replace unwrap() with ok_or/? in DQN IQN paths

Replace 6 unwrap() calls with safe error handling in DQN IQN code:
- Production: 3 unwrap() on iqn_network/iqn_target_network replaced with
  ok_or_else returning MLError::ModelError for clear diagnostics
- Tests: 2 result.unwrap() replaced with ?, 2 DQN::new().unwrap() replaced
  with ? after changing test signatures to return anyhow::Result<()>

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

2026-02-21 19:12:16 +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

examples

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

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

migrations

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

2025-10-12 19:57:35 +02:00

results

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

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

src

fix(ml): replace unwrap() with ok_or/? in DQN IQN paths

2026-02-21 19:12:16 +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

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

2025-10-13 13:30:02 +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 10: ML Model Integration Complete (6 Agents, TDD)

2025-10-16 00:01:19 +02:00

DBN_REPOSITORY_USAGE.md

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

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

Dockerfile

feat(wave9-11): Complete 225-feature integration and service migration

2025-10-20 21:54:39 +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.