Strategy Backtest — Historical Performance & Optimization

Overview

Supports systematic trading strategy workflows: backtest rules on history, optimize parameters (e.g. grid search), and report results. Typical building blocks include moving-average crosses, MACD, RSI, and custom signals—implemented with libraries such as Backtrader or similar.

Trigger keywords: backtest, trading strategy, quant, algorithmic trading, Sharpe, drawdown, optimize parameters, walk-forward

Prerequisites

pip install pandas numpy backtrader matplotlib

Capabilities

1. Backtest engine — run strategies on historical OHLCV (or vendor-specific) data.
2. Performance analytics — annualized return, max drawdown, Sharpe-like ratios, win rate (definitions must match your implementation).
3. Parameter search — grid or bounded search over strategy parameters with out-of-sample caution (see references/strategy_backtest_guide.md).