: The logic that identifies entry and exit signals using technical indicators (e.g., RSI, Bollinger Bands) or AI models.

This is where strategy moves from logic to Machine Learning.

, calculating Moving Averages and the Relative Strength Index (RSI) to give his bot "eyes" to see the trend. The Brain: Enter Machine Learning

# Generate predictions data['Predicted_Signal'] = model.predict(X) # Calculate strategy returns data['Strategy_Returns'] = data['Predicted_Signal'] * data['Returns'] # Cumulative performance data['Cumulative_Market'] = (1 + data['Returns']).cumprod() data['Cumulative_Strategy'] = (1 + data['Strategy_Returns']).cumprod() Use code with caution. Critical Backtesting Pitfalls

Add a slippage_model function.

A profitable model is worthless without strict risk management. Common strategies include:

: For fetching historical market data. TA-Lib : For calculating technical analysis indicators.