# Deep-Reinforcement-Learning-in-Stock-Trading

**Repository Path**: zero2hero/Deep-Reinforcement-Learning-in-Stock-Trading

## Basic Information

- **Project Name**: Deep-Reinforcement-Learning-in-Stock-Trading
- **Description**: Using deep actor-critic model to learn best strategies in pair trading 
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-08
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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