ml-strategy

To generate predictive trading signals from OHLCV market data, you apply machine learning models like RandomForest and GradientBoosting to historical price action. This approach identifies potential bullish or bearish trends using engineered features.

Walk-forward training prevents overfitting by implementing expanding or sliding window training to test models strictly on unseen historical data. This ensures predictive signals remain objective and avoids future data leakage during model evaluation.

Yes, you need scikit-learn, pandas, and numpy to build automated machine learning strategies. These dependencies are required to perform feature engineering and walk-forward model training on OHLCV financial data for quantitative analysis.

Technical indicators used as features for machine learning in finance include momentum, volatility, RSI, and Bollinger Band positions. This feature engineering automatically builds multi-dimensional factors from OHLCV data for model training.

The best way to avoid future data leakage when developing quantitative trading models is using walk-forward training. This expanding or sliding window approach ensures machine learning models are always tested on unseen historical market data.