crypto-backtesting

What problem does it solve?

This Skill enables quantitative teams and solo traders to rigorously test trading ideas by simulating real-world execution on historical data, reducing guesswork and providing reproducible results.

Core Features & Use Cases

• Backtesting Engine: Evaluate strategies against OHLCV data with realistic entry/exit execution assumptions.
• Walk-Forward Analysis: Validate performance by rotating training and testing windows to detect overfitting.
• Risk & Metrics: Compute metrics such as total return, Sharpe ratio, maximum drawdown, win rate, and number of trades.
• Use Case: A trader wants to compare two momentum strategies across multiple assets and timeframes to select the most robust approach.

Quick Start

Prepare an OHLCV dataset, implement a strategy object with a generate_signal method, instantiate a Backtester with an initial capital, and call run() to obtain performance metrics.