ml-strategy

You can generate trading signals from OHLCV data using machine learning by applying walk-forward training and feature engineering to forecast future returns and map model probabilities to long, neutral, or short decisions.

Walk-forward training in ML forecasting is a validation method that trains models sequentially on historical data to prevent data leakage during model evaluation, ensuring predictions remain robust and reliable.

To engineer features from OHLCV data for predictive modeling, you construct technical indicators like momentum, volatility, RSI, moving average ratios, and volume ratios to power the machine learning algorithms.

Yes, you can use machine learning forecasting models for cryptocurrency data, as the pipeline supports OHLCV data across equities, futures, and cryptocurrencies provided there is ample history to train and validate models.

Supported models for walk-forward ML forecasting include RandomForest, GradientBoosting, and Ridge, which adapt to different data regimes and varying prediction horizons.

To map model probabilities to continuous trading signals, the pipeline converts predicted outcomes into continuous signals or discrete classes, directly outputting safe long, neutral, or short trading decisions.