WAVE 100: Test Coverage Expansion (8/10 agents, 308 tests added) ├─ Agent 4: Execution error path tests (trading_service) ├─ Agent 5: ML training pipeline timeout analysis ├─ Agent 6: Audit persistence comprehensive tests ├─ Agent 7: ML pipeline coverage tests + rate limiting ├─ Agent 8: Algorithm comprehensive tests (adaptive-strategy) ├─ Agent 9: Coverage measurement analysis └─ Result: 308 new tests across 8 components WAVE 101: Compilation Error Fixes (14 errors → 0) ├─ Fixed backtesting_comprehensive.rs (6 compilation errors) │ ├─ Added `use rust_decimal::MathematicalOps;` import │ ├─ Removed 3 invalid `?` operators from void methods │ └─ Fixed 4 i64 type casting issues for ChronoDuration::days() ├─ performance_tracking_comprehensive.rs: Already fixed (38/38 tests pass) └─ algorithm_comprehensive.rs: Already fixed (38/40 tests pass) WAVE 102: Runtime Test Failure Analysis (10 failures documented) ├─ Issue #1: Benchmark comparison stub (backtesting/metrics.rs:657-669) │ └─ Always returns None, needs beta/alpha/tracking error implementation ├─ Issue #2: Daily returns calculation edge cases (3 tests affected) │ └─ Returns empty Vec for < 2 snapshots, triggers "No daily returns calculated" ├─ Issue #3: Timestamp offsets in replay tests (1 hour, 60 day differences) │ └─ Possible timezone/DST issue or Utc::now() non-determinism ├─ Issue #4: Monthly performance calculation (< 11 months generated) └─ Issue #5: Max drawdown peak-to-trough assertion TEST RESULTS: ├─ Compilation: ✅ 100% (all 3 Wave 100 test files compile) ├─ Test Pass Rate: 108/118 tests (91.5%) │ ├─ algorithm_comprehensive: 38/40 (95%) │ ├─ backtesting_comprehensive: 32/40 (80%) │ └─ performance_tracking: 38/38 (100%) └─ Coverage Impact: Estimated +5-10 points toward 95% target FILES CHANGED: ├─ New Tests: 11 files (algorithm, backtesting, performance tracking, etc.) ├─ Fixed: backtesting_comprehensive.rs (6 compilation errors resolved) ├─ Documentation: 8 new agent reports (Wave 100-101) └─ Analysis: wave102_test_failures_analysis.txt TIMELINE: ├─ Wave 100: 308 tests added (90% completion, 2 agents hit timeout) ├─ Wave 101: All compilation errors resolved (100% success) ├─ Wave 102: Root cause analysis complete (10 failures documented) └─ Next: Wave 103 to fix 10 runtime test failures (5-10 hours estimated) 🤖 Generated with [Claude Code](https://claude.com/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.