Future Work

Future Work

This page outlines potential improvements and future directions for this project.

1. Model Improvements

• Implement Double DQN to reduce overestimation bias in Q-values.
• Try Dueling DQN architecture to better separate value and advantage functions.
• Explore other architectures, such as LSTM-based or Transformer-based models, to handle temporal dynamics more effectively.

2. Feature Engineering

• Incorporate additional technical indicators (e.g., MACD, Bollinger Bands).
• Experiment with volume-based features.
• Use volatility-adjusted features to enhance robustness during unstable market conditions.

3. Data Expansion

• Extend the historical data range further back if possible.
• Apply the method to different assets (e.g., other ETFs, stocks, forex).

4. Risk Management

• Introduce stop-loss and take-profit mechanisms.
• Explore reward shaping techniques to encourage risk-aware behavior.

5. Optimization and Fine-tuning

• Perform a more systematic hyperparameter optimization (e.g., using Optuna or Ray Tune).
• Implement automated retraining with updated market data.

6. Deployment

• Develop a simulation framework for forward-testing (out-of-sample validation).
• Build an API or lightweight app for strategy visualization and backtesting.

7. Documentation and Accessibility

• Expand the Wiki with more detailed tutorials and examples.
• Create educational materials for newcomers to reinforcement learning in finance.

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Notes

These directions aim to make the model more robust, generalizable, and practically useful in real-world trading environments.