Portfolio Allocation

Using an ensemble of Deep Reinforcement Learning Algorithms to perform Portfolio Allocation
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Table of Contents

1. About The Project
   • Built With
2. Roadmap
3. Contributing
4. License
5. Contact
6. Acknowledgements

About The Project

1. Uses an ensemble of Deep Reinforcement Learning Algorithms over a single algorithm to account for the various drawbacks
2. Uses high quality implementations provided by Stable Baselines
3. Follows the Black coding style
4. Is as deterministic as possible to increase reproducability.
5. Uses technical indicators such as Moving average convergence divergence (MACD) , Relative strength index (RSI), Commodity Channel Index (CCI) , Average Directional Movement Index (ADX) beside average returns

Built With

• PyTorch
• OpenAI Gym
• stable-baselines-3

Roadmap

Phase 1: Getting it to work
Phase 2: Improve code quality and refactor code according to the best practices
Phase 3: Support for Live Trading , Fractional Shares See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgements

Black

Contact

Prasanna Devadiga - @https://twitter.com/Prasanna280 - prasanna2019@iiitkottayam.ac.in

Sung Jae Bae - Twitter LinkedIn - sbae703@gmail.com

Project Link: https://github.com/Prasanna28Devadiga/Portfolio-Allocation-DRL