backtesting-sim

What problem does it solve?

This Skill provides a fast, reproducible framework for evaluating trading ideas by performing vectorized backtesting, generating comprehensive performance metrics, and enabling robust validation.

Core Features & Use Cases

• Vectorized backtesting: fast, scalable evaluation of signals against OHLCV data with costs and slippage.
• Performance metrics: automatic computation of Sharpe, Sortino, Calmar, max drawdown, win rate, and more.
• Walk-forward validation: anchored out-of-sample testing to assess robustness across regimes.
• Strategy comparison: compare multiple strategies on a common dataset to identify the best performers.
• Use cases include evaluating new indicators, testing rule changes, and stress-testing strategies over historical data.

Quick Start

Prepare your OHLCV data with a signal column and run the backtester to obtain full results.