BSc IT Project Guide: Reinforcement Learning for Trading
1. Project Title
Reinforcement Learning for Trading: Simulating Stock Trading Bots
2. Introduction
This project involves building a stock trading simulation using reinforcement learning (RL) techniques. The RL model learns optimal trading strategies by interacting with a simulated stock market environment and maximizing returns. It aims to model how an intelligent trading agent can make buy/sell/hold decisions over time.
3. Objectives
- Implement a reinforcement learning algorithm (e.g., Deep Q-Learning, PPO)
- Simulate stock trading environment with historical data
- Train an agent to learn profitable trading strategies
- Visualize agent performance against standard benchmarks (e.g., buy-and-hold)
4. System Requirements
- Python 3.x
- TensorFlow or PyTorch
- OpenAI Gym
- Matplotlib, Pandas, NumPy
- Jupyter Notebook or IDE (e.g., VSCode)
5. Modules to be Implemented
- Data Preprocessing Module
- Stock Market Environment Simulation
- Reinforcement Learning Agent
- Training and Evaluation Module
- Visualization and Analytics Dashboard
6. Methodology
The project begins by collecting and preprocessing historical stock data. An environment that mimics stock market behavior is built, including transaction costs and portfolio tracking. A reinforcement learning agent is then trained in this environment to make trading decisions based on state observations. The agent is evaluated against benchmark strategies using metrics like cumulative returns and Sharpe ratio.
7. Expected Output
- Trained trading agent model
- Plots showing trading decisions and portfolio growth
- Performance metrics vs. baseline strategies
- A detailed report documenting methodology and findings
8. Future Scope
- Expand to multi-asset trading
- Integrate with live stock feeds for real-time trading simulations
- Use ensemble of RL agents for diversified strategies
- Explore hybrid models with technical indicators and NLP-based signals
9. References
- Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction
- OpenAI Gym Documentation
- TensorFlow & PyTorch RL tutorials
- Quantitative Finance forums and Kaggle notebooks