Overview
This project aims to develop an automated stock trading system using deep reinforcement learning (DRL). By leveraging advanced machine learning techniques, the system will learn to make profitable trading decisions in the stock market. The project will involve downloading historical stock data, enhancing it with technical indicators, creating a simulated trading environment, and training multiple DRL agents including PPO, A2C, DDPG, SAC, TD3, and an ensemble agent. The performance of these agents will be evaluated and compared based on various financial metrics.
This project is based on the research paper: “Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy” (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690996).
Project’s resources can be found at:
- LinkedIn: https://www.linkedin.com/pulse/deep-reinforcement-learning-automated-stock-trading-pham-the-anh-xrqqc/?trackingId=tofWRky%2FTCKIwF783zP6fA%3D%3D
- Kaggle: https://www.kaggle.com/code/phamtheanh97/deep-reinforcement-learning-with-stock-trading
- GitHub: https://github.com/theanh97/Deep-Reinforcement-Learning-with-Stock-Trading
Guide Instruction
1. Install the Required Packages
Install necessary libraries.
2. Download the Dow Jones 30 Stocks Data
Use yfinance to download historical stock data for Dow Jones 30 companies.
3. Load the Data from the CSV Files
Load the stock data from CSV files.
4. Add Technical Indicators to the Data
Enhance the data with technical indicators (RSI, MACD, CCI, ADX, etc.).
5. Create Trading Environment
Set up the environment for simulating stock trading.
6. Training Model — Ensemble + PPO + A2C + DDPG + SAC + TD3
6.1 Define Agents Define PPO, A2C, DDPG, SAC, TD3, and an ensemble agent.
6.2 Function to Create Environment and Train Agents Create environment and train the six agents.
6.3 Function to Visualize Portfolio Changes Visualize portfolio changes and net worth.
6.4 Function to Visualize the Agent’s Performance Visualize individual agents’ performance.
6.5 Function to Test and Visualize the Ensemble Agent’s Performance Test and visualize the ensemble agent’s performance.
6.6 Function to Compare the Performance of the Agents Compare the agents based on returns, standard deviation, and Sharpe ratio.
6.7 Run Execute training and testing, visualize and compare results.
7. Testing Model
Test the models on new data to evaluate performance.
1. install the required packages
2. Download the Dow Jones 30 stocks data
3. Load the data from the CSV files
4. Add technical indicators to the data
5. Create Trading Environment
6. Training Model — Ensemble + PPO + A2C + DDPG + SAC + TD3
6.1 — Define Agents ( Ensemble + PPO + A2C + DDPG + SAC + TD3)
6.2 — Function to Create Environment + Train 6 Agents: PPO, A2C, DDPG, SAC, TD3 and Ensemble ( PPO + A2C + DDPG + SAC + TD3)
6.3 — Function to visualize Portfolio changes, Portfolio Net Worth and Multiple Portfolio Net Worth
6.4 — Function Visualize the Agent’s performance
6.5 — Function to Test and Visualize the Agent’s performance ( Ensemble Agent )
6.6 — Function to compare the performance of the 4 Agents : Returns, Standard Deviation, Sharpe Ratio
6.7 — Run
7. Testing Model
In Conclusion
This project demonstrates the potential of deep reinforcement learning to enhance automated stock trading strategies. By combining various DRL algorithms and incorporating realistic factors like transaction costs, we’ve shown promising results in terms of return and risk management.
While the DDPG agent emerged as a top performer, the ensemble and TD3 agents also offered compelling options for balancing risk and reward. The PPO agent, though requiring further refinement, highlights the importance of ongoing optimization in this dynamic field.
Future work will focus on exploring additional RL techniques, incorporating more sophisticated features, and extending the model to diverse market conditions.
I hope you found this project overview insightful. Feel free to connect, share your thoughts, and collaborate!