# deep-RL-trading

**Repository Path**: triobox/deep-RL-trading

## Basic Information

- **Project Name**: deep-RL-trading
- **Description**: playing idealized trading games with deep reinforcement learning
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-06-05
- **Last Updated**: 2021-06-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# **Playing trading games with deep reinforcement learning**

This repo is the code for this [paper](https://arxiv.org/abs/1803.03916). Deep reinforcement learing is used to find optimal strategies in these two scenarios:
* Momentum trading: capture the underlying dynamics
* Arbitrage trading: utilize the hidden relation among the inputs

Several neural networks are compared: 
* Recurrent Neural Networks (GRU/LSTM)
* Convolutional Neural Network (CNN)
* Multi-Layer Perception (MLP)

### Dependencies

You can get all dependencies via the [Anaconda](https://conda.io/docs/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file) environment file, [env.yml](https://github.com/golsun/deep-RL-time-series/blob/master/env.yml):

    conda env create -f env.yml

### Play with it
Just call the main function

    python main.py

You can play with model parameters (specified in main.py), if you get good results or any trouble, please contact me at gxiang1228@gmail.com