Build trading strategy backtesting

Build event-driven backtesting engine with bias mitigation

Validating trading strategies when look-ahead bias, survivorship bias, and transaction costs must be accounted for.

• · strategy logic (entry/exit rules)
• · historical OHLCV data
• · execution model (slippage, commission)

• · portfolio equity curve
• · trade log (fills)
• · performance statistics (Sharpe, max drawdown, win%)

• · Python pandas
• · numpy

• · point-in-time data (no look-ahead)
• · strategy defines buy/sell signals

• · look-ahead bias if future data used in strategy
• · overfitting on training set
• · ignoring transaction costs masks real slippage

• · explicit discussion of 5 backtesting biases
• · walk-forward analysis pattern included
• · event-driven execution model
• · Fill dataclass with realistic costs