zuytan/rustrade

Overview

This skill provides a structured benchmarking and performance workflow for algorithmic trading strategies. It focuses on backtesting, multi-period validation, risk metrics, and practical commands to run quick and advanced benchmarks. The goal is to validate profitability, detect overfitting, and produce repeatable performance reports before moving to live trading.

How this skill works

The skill runs historical backtests across symbols, periods, and parameter matrices, then computes core profitability and risk metrics (Sharpe, Sortino, Profit Factor, Max Drawdown, Win Rate). It supports parallel execution, sequential comparisons, and stress tests on specific market regimes. Results are saved for comparison and used to drive a validation checklist before production.

When to use it

• After adding or modifying a strategy
• To validate that a strategy is profitable before live trading
• When comparing different parameter sets or strategy variants
• To stress-test strategies across bull, bear, and volatile periods
• Before promoting from paper trading to production

Best practices

• Require positive backtests on multiple years and regimes (bull/bear/sideways)
• Use train/test splits and out-of-sample validation to avoid overfitting
• Monitor both profitability (Total Return, Profit Factor) and risk (Sharpe, Max Drawdown)
• Include stress periods (e.g., COVID crash, 2022 bear market) in tests
• Prefer simpler strategies and limit parameter tuning to avoid suspiciously high Sharpe

Example use cases

• Quick benchmark: run a 1-year backtest for a single symbol to sanity-check changes
• Multi-period validation: test the same strategy across different calendar years
• Parameter matrix sweep: evaluate combinations of indicator thresholds in parallel
• Cross-asset consistency: run the strategy on multiple symbols to ensure robustness
• Stress testing: validate resilience during known crash windows and high volatility