ml-strategy

To generate trading signals using machine learning, you train scikit-learn models on historical OHLCV data to predict future returns and output actionable buy or sell decisions.

Yes, you can use scikit-learn to predict future stock returns by performing feature engineering on OHLCV data to extract useful factors for training predictive models.

Yes, you need pandas and numpy to run predictive analytics for finance, as they handle the essential data processing and numerical operations required for model training.

You extract features from OHLCV data for model training by transforming raw open, high, low, close, and volume fields into structured factors that machine learning models can interpret.

Limitations of using sklearn models for forecasting stock price movements include their reliance on historical data patterns and the need for additional data processing tools to handle complex market volatility.