Using deep actor-critic model to learn best strategies in pair trading

Related tags

Deep Learning Deep-Reinforcement-Learning-in-Stock-Trading
Overview

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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Comments
  • Negative port value

    Negative port value

    Hello, first of all, thank you for your great contribution. After successfully run the code I found out by locking to csv output files that the final port value is negative. The results are not the same as the ones in the data_for_vis_final.csv. Am I missing something? Also I would like to ask you how is calculated the price of buying/selling - Long/Short action? How would you change the project to take into account only one equity.

    opened by joaosalvado10 1
Owner
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GitHub
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