8b9abcc3c1
Eliminate ~4,260 clippy deny-level errors that blocked workspace-wide clippy runs. Errors cascaded: upstream crate failures (ctrader-openapi, risk-data) hid thousands of downstream errors in ml, tli, backtesting. Key changes: - ctrader-openapi: fix shadow_unrelated/shadow_reuse (renamed vars) - risk-data/risk: replace non-ASCII em dashes with ASCII equivalents - tli: allow deny lints on prost-generated proto code, fix shadows - trading_engine: fix let_underscore_must_use, wildcard matches, shadows - broker_gateway_service: allow dead_code on unused redis_client field - ml (4030 errors): remove local deny overrides for unwrap/expect/indexing (workspace warn level sufficient), add crate-level allows for non-safety mass-violation lints (non_ascii_literal, shadow_*, str_to_string, etc.), batch-fix em dashes, unseparated literal suffixes, format_push_string, wildcard matches, impl_trait_in_params, mutex_atomic, and more - backtesting: replace unwrap() on first()/last() with match destructure - tests: simplify loop-that-never-loops, fix mutex unwrap Co-Authored-By: Claude Opus 4.6 <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.