machine-learning-strategy

Walk-forward validation prevents look-ahead bias by training machine learning models with expanding or sliding windows, ensuring each prediction uses only historical OHLCV data. This approach maps model probabilities to continuous trading signals in [-1.0, 1.0] for future N-day return direction.

You can engineer robust factors from OHLCV data including momentum, volatility, RSI, moving-average ratios, volume ratios, Bollinger position, and intraday ratios. These features undergo sanitization and zero-division guards to ensure clean inputs for scikit-learn models generating trading signals.

Yes, you can generate per-symbol long and short confidence signals for multiple symbols simultaneously. The workflow applies walk-forward training and forward-shifted return labels to your OHLCV DataFrames, producing continuous outputs clipped to [-1.0, 1.0] for each symbol.

Yes, you need pandas and numpy along with scikit-learn to build walk-forward machine learning models on OHLCV data. These dependencies handle data manipulation, numerical operations, and model training required for sanitized feature engineering and return prediction.

Model probabilities are mapped to continuous trading signals constrained within the range of [-1.0, 1.0]. This signal generation contract includes NaN-safe fallbacks to ensure clean outputs suitable for predicting future N-day return direction in financial backtests.

Limitations of using scikit-learn for walk-forward return prediction include the necessity for strict OHLCV validation and sanitized factor construction to avoid look-ahead bias. Rolling retraining is computationally intensive, and continuous outputs require clipping to manage extreme predictions.