https://github.com/charlesyuan02/reinforcement-learning-stock-trader

A reinforcement learning stock trader for the S&P 500.
https://github.com/charlesyuan02/reinforcement-learning-stock-trader

deep-reinforcement-learning openai-gym reinforcement-learning stock-trading yahoo-finance

Last synced: 5 days ago
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A reinforcement learning stock trader for the S&P 500.

• Host: GitHub
• URL: https://github.com/charlesyuan02/reinforcement-learning-stock-trader
• Owner: CharlesYuan02
• License: mit
• Created: 2023-03-09T18:09:48.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-04-16T01:29:38.000Z (over 1 year ago)
• Last Synced: 2024-10-10T19:10:07.818Z (26 days ago)
• Topics: deep-reinforcement-learning, openai-gym, reinforcement-learning, stock-trading, yahoo-finance
• Language: Python
• Homepage:
• Size: 529 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# reinforcement-learning-stock-trader



## Prerequisites

All code was written in Python 3.10. Please see requirements.txt for dependencies.

```

gym==0.26.2

gym-anytrading==1.3.2

pandas-datareader==0.10.0

scipy==1.10.1

stable_baselines3==1.8.0

tensorflow>=2.11.0

tqdm==4.65.0

yfinance==0.2.12

```

## Description of Files

### data_extractor.py

This file contains all the code used to extract the list of stocks on the S&P 500 and their respective financial data. It also contains a dataloader function for loading the correct data in the training scripts. See doc strings for details.

### single_feature_model.py and multi_feature_model.py

This is where the deep Q-learning models are defined, along with the trade and batch_train methods used in the training scripts. The single feature model only takes in the closing price as a feature, while the multi-feature model takes in as many features as is defined in the training script. Modifications were made accordingly to the multi-feature model methods.

### train_single_feature.py and train_multi_feature.py

This is where the training for the model actually happens. Note that for the multi-feature model's training, we defined the function to allow for multiple stocks to be used, as a further improvement on the single-feature training.

## Description of Files - Post-Interim Report

### custom_environment.py and custom_environment_multistock.py

Implementations of training environments with continuous action spaces. Both follow the format that OpenAI Gym uses, and are compatible with the off-the-shelf models A2C and PPO obtained from stable baselines. 

### train_single_stock.py and train_multi_stock.py

The training and evaluation code for A2C and PPO, leveraging our custom environments. One is used for training on one stock at at time, while the other is used for a portfolio of multiple stocks. 

## License

This project is licensed under the MIT License - see the LICENSE file for details.