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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Using deep actor-critic model to learn best strategies in pair trading
| Date | Stars |
|---|---|
| 2026-07-31 | 323 |
| 2026-08-02 | 323 |
| 2026-08-03 | 323 |
| 2026-08-06 | 323 |
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# Deep-Reinforcement-Learning-in-Stock-Trading Using deep actor-critic model to learn best strategies in pair trading ## Abstract Partially observed Markov decision process problem of pairs trading is a challenging aspect in algorithmic trading. In this work, we tackle this by utilizing a deep reinforcement learning algorithm called advantage actor-critic by extending the policy network with a critic network, to incorporate both the stochastic policy gradient and value gradient. We have also used recurrent neural network coupled with long-short term memory to preserve information from time series data of stock market. A memory buffer for experience replay and a target network are also employed to reduce the variance from noisy and correlated environment. Our results demonstrate a success on learning a well-performing lucrative model by directly taking data from public available sources and present possibilities for extensions to other time-sensitive applications ## Usage customize the stock pair/period to simulate in runner.py run "python RLMDP/runner.py" ## Credit to Yichen Shen Yiding Zhao ## Based on the previous work by Su Hang Zhaoming Wu Sam Norris
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matched fp:c06ecf18242e73c9, name:reinforcement learning, desc:actor-critic