d16fa83cf4
## Critical Production Fixes (2/7 blockers resolved) ### ✅ Fixed: Performance Metric Stubs - **File**: backtesting/src/metrics.rs - **Before**: calculate_monthly_performance() → Ok(Vec::new()) // stub - **After**: Full implementation with BTreeMap grouping, trade counts, win rates - **Lines**: +74 lines (monthly), +82 lines (yearly) - **Impact**: Enables monthly/yearly performance reporting ### ✅ Fixed: Connection Pool Panic - **File**: storage/src/model_helpers.rs:101 - **Before**: panic!("Connection pool is empty") - **After**: StorageResult<Arc<dyn ObjectStore>> with proper error handling - **Impact**: Service resilience on connection pool exhaustion ### 📝 Documentation Update - **File**: CLAUDE.md - **Status**: Updated to Wave 104 (89.5% → 90%+ target) - **Progress**: Waves 102-103 achievements documented **Next**: Wave 104 Part 2 - Launch 12 agents for final push to 90%+ 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
Backtesting Crate
Overview
The backtesting crate provides a robust and configurable engine for simulating trading strategies against historical market data. It enables quantitative analysts and developers to evaluate strategy performance, optimize parameters, and validate hypotheses before live deployment.
Features
- Historical Data Replay: Efficiently replays market data from Parquet files, supporting various data granularities (ticks, order book snapshots, candles).
- Comprehensive Performance Metrics: Calculates key performance indicators such as Sharpe Ratio, Maximum Drawdown, Alpha, Beta, Sortino Ratio, and more.
- Realistic Slippage Modeling: Configurable slippage models (e.g., fixed, percentage, volume-based) to accurately reflect real-world execution costs.
- Commission Modeling: Supports various commission structures (e.g., fixed per trade, percentage of value, per share/contract) for accurate P&L calculation.
- Detailed Trade Analytics: Generates in-depth reports on individual trades, cumulative P&L, win/loss ratios, and trade duration analysis.
- Pluggable Strategy Interface: Defines a clear interface for users to implement and integrate their custom trading strategies seamlessly.
Usage
use backtesting::{Backtester, BacktestConfig};
use common::types::InstrumentId;
use std::path::PathBuf;
let config = BacktestConfig {
start_time: "2023-01-01T00:00:00Z".parse().unwrap(),
end_time: "2023-01-02T00:00:00Z".parse().unwrap(),
data_path: PathBuf::from("./historical_data/"),
instruments: vec![InstrumentId::new("BTCUSD".to_string())],
// ... other configuration like slippage, commissions
};
// let mut backtester = Backtester::new(config);
// let strategy = MySimpleStrategy::new(); // Initialize your strategy
// backtester.run(&strategy).expect("Backtest failed");
// let results = backtester.get_results();
// println!("Sharpe Ratio: {}", results.sharpe_ratio);
// println!("Max Drawdown: {}", results.max_drawdown);
Testing
cargo test --package backtesting
Documentation
Full API documentation is available at docs.rs/backtesting.