09. Decision Trees Demo

Exploring Decision Trees for Stock Market Analysis

This demo covers using decision trees to predict stock market returns by turning predictions into a classification problem. Follow these steps:

1. Import Libraries: Load necessary libraries such as Pandas, Matplotlib, NumPy, and StandardScaler from sklearn. Import decision tree classifier for modeling.

2. Data Preparation: Work with pre-loaded data. Convert returns into a binary indicator variable (1 if returns > 0, otherwise 0).

3. Data Split: Divide data into 70% training and 30% testing sets. Retain the returns in the test dataset for evaluation.

4. Feature Scaling: Use StandardScaler on the training data to normalize, then apply it to test data to avoid look-ahead bias.

5. Model Training: Train the decision tree model using the scaled training dataset.

6. Prediction and Evaluation: Predict on test data. Use predictions to develop a trading strategy, compare performance to the S&P 500 benchmark.

7. Analyze Results: Evaluate model performance, noting an 8% return with a 0.8 Sharpe ratio, acknowledging underperformance relative to market.

Learn about refining strategies in forthcoming discussions.