Personae - RL & SL Methods and Envs For Trading

Personae is a repo of implements and enviorment of Deep Reinforcement Learning & Supervised Learning.
This repo tries to implement new methods and papers in different ways (TensorFlow or PyTorch) and test them in Financial Market (Stock Market).

Contents

• Deep Deterministic Policy Gradient (DDPG)
  Implement of DDPG with TensorFlow.

  arXiv:1509.02971: Continuous control with deep reinforcement learning

• Policy Gradient
  Implement of Policy Gradient with TensorFlow.

  NIPS. Vol. 99. 1999: Policy gradient methods for reinforcement learning with function approximation

• DA-RNN (DualAttnRNN)
  Implement of arXiv:1704.02971, DA-RNN with TensorFlow.

  arXiv:1704.02971: A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

Experiments

• Deep Deterministic Policy Gradient (DDPG)
• Policy Gradient (PG)
  Train a Agent to trade in stock market, using stocks data set from 2008-01-01 to 2018-01-01 where 70% are training data, 30% are testing data.

Total Profits and Baseline Profits. (Test Set)

• DA-RNN (DualAttnRNN)
  Train a Predictor to predict stock prices, using stocks data set from 2008-01-01 to 2018-01-01, where 70% are training data, 30% are testing data.

Prices Prediction Experiments on 4 Bank Stocks. (Test Set)

Requirements

Before you start testing, following requirements are needed.

• Python3.5
• TensorFlow-1.4
• PyTorch
• Numpy
• Pandas
• sklearn
• mongoengine
• tushare
• matplotlib
• CUDA (option)
• Docker (option)

It's best that if you are a Docker user, so that you can use run the whole project without installing those dependencies.

And you can also use Ansible to run CUDA-Playbook and Docker-Playbook to install CUDA and Nvidia-Docker if you want to run tests in a Docker Container.

How to Use

If you use Docker

About base image

My image for this repo is ceruleanwang/haru, and haru is inherited from ceruleanwang/quant.
The image ceruleanwang/quant is inherited from nvidia/cuda:8.0-cudnn6-runtime.
So please make sure your CUDA version and cuDNN version are correct.

Instructions

First you should make sure you have stocks data in your mongodb. If you don't have, you can use a spider writen in this repo to crawl stocks data by following code:

docker run -t -v local_project_dir:docker_project_dir --network=your_network ceruleanwang/haru spider/finance.py

But remember to set stock codes that you want to crawl, the default are:

codes = ["600036", "601328", "601998", "601398"]

And they can be modified in:

• Default Args Parser

You can also use a mongo container (option) by following code:

docker run -p 27017:27017 -v /data/db:/data/db -d --network=your_network mongo

Then you can just run a model by:

docker run -t -v local_project_dir:docker_project_dir --network=yuor_network ceruleanwang/haru algorithm/RL or SL/algorithm_name.py

If you use Conda

You can create an env yourself, and install Python3.5 and all dependencies required, then just run algorithm in your way.

TODO

• More Implementations of Papers.
• More High-Frequency Stocks Data.