06. Practical Application of Logistic Regression

Preparing Data for Logistic Regression

Logistic regression is a powerful tool used to predict categories based on historical data. Understanding how to properly prepare this data is crucial for accurate model predictions.

Key Steps:

• Historical Data Collection: Utilize existing methods to gather historical data through an API.
• Feature Creation: Develop historical returns from this data, which will be used as features in the model.
• Dataset Splitting: Create separate training and test datasets to evaluate model performance.

Important Considerations:

• Categorical Target Variable: Convert the numerical target variable into categories. For example, designate positive returns as '1' and negative returns as '0'.
• Custom Categories: Define categories that align with specific investment goals, like factoring in trading costs.
• Multi-Class Returns: Introduce multiple return categories to manage investment decisions better based on predicted outcomes.

Application:

• Training and Prediction: Train the logistic regression model and make predictions.
• Stock Selection: Use predictions to select stocks and simulate market performance comparisons.

This approach allows tailoring a trading model to meet specific investment objectives and strategies.