How can ML be used in momentum trading

Machine learning (ML) can be used in momentum trading to analyse historical market data and identify patterns or trends that may not be apparent through traditional methods. ML algorithms can predict future price movements, generate trading signals, and optimise trading strategies based on the identified patterns.

ML approaches offer several advantages in momentum trading, including the ability to analyse large datasets quickly, identify complex patterns, and adapt to changing market conditions.

The ML approach for momentum trading utilises various types of data inputs, including historical price data, trading volumes, technical indicators (e.g., moving averages, RSI), etc. These inputs help ML algorithms in identifying patterns and making predictions.

Key Points:

XGBoost model to predict momentum and create a time-series momentum strategy.

Improve the accuracy model using cross-validation and hyperparameter tuning.

By using the prediction from multiple ML models such as Logistic Regression, ADA Boost, ExtraTrees, along with XGBoost models and aggregating them with the help of Voting Classifier. 

Libraries like Pandas and NumPy for data manipulation. You will also use a few machine learning libraries such as Sklearn and XGBoost. For plotting and visualisation tasks you will be using the Matplotlib library.