Pairstrade fyp 2019
We tested 3 approaches for Pair Trading: distance, cointegration and reinforcement learning approach.
Final year project at HKUST. We tested 3 main approaches for performing Pairs Trading: - distance method - cointegration method (rolling OLS, Kalman Filter) - reinforcement learning agent (proposed) The project is written primarily in Python, distributed under the MIT License license, first published in 2018. Key topics include: machine-learning, pairs-trading, reinforcement-learning.
pairstrade-fyp-2019
Final year project at HKUST. We tested 3 main approaches for performing Pairs Trading:
- distance method
- cointegration method (rolling OLS, Kalman Filter)
- reinforcement learning agent (proposed)
FYP members: myself, Gordon, Brendan
How to get started?
- Run
./setup.shto install all dependencies
Note
- In our experiments, we used financial data taken from the Interactive Brokers platform, which is not free. Due to their regulations, we cannot release the financial data used in our experiments to the public. Feel free to use your own price data to perform experiments.
Disclaimer
- The strategies we implemented have not been proven to be profitable in a live trading account
- The reported returns are purely from backtesting procedures, and they may be susceptible to lookahead bias that we are not aware of
Updates
- We're no longer developing this, check out Yuri's findings regarding the RL agent
Contributors
Showing top 2 contributors by commit count.
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