trxw / RL-Trading-Agent

Direct Reinforcement Learning Agent for Portfolio Management Purposes

Reinforcement Learning Trading Agent

The repository contains implementation of an attempt to train RL agent to trade in the financial market using a Deep Direct Reinforcement Learning model through Technical Analysis methodon the data to provide more robust framework on top of the data using minute-based HKEX market data. Refer to the UROP_1100_Report.pdf for detailed explanation.

The RL agent and its environment is defined in the ./model/ directory, the data is in ./data/ directory, and scripts for pre-processing the data is present in the ./data_wrangling/ directory.

Detailed documentation is provided on README.md in each folder and its corresponding files.

Usage

To use and train the model, refer to the 3 notebooks where the model is trained on 3 different dataset. Refer to the DRL_BOC+CNOOC.ipynb for the most updated and refactored code with its documentation.

To use the your own data, replace and edit the data in the ./data/ directory.

Dependencies

Using conda package manager, build the environment with environment.yml:

$ conda env create -f environment.yml

Or without conda:

• Python 3.6
• TA-Lib >= 0.4.17. On some machines, underlying TA-Lib library should be installed in advance before installing using pip or other package manager. Refer here for the predecessor library.
• Pytorch >= 0.4.1
• tqdm >= 4.28.1
• pyfolio >= 0.9.0
• seaborn
• numpy
• pandas
• jupyter notebook
• and other basic packages.

Installation

Clone the repo, and build virtual environment using conda for easier installation through the using the step above.

Important note: this project utilises TA-Lib library for python (Technical Analysis) as a part of the knowledge discovery on the data. It requires the predecessor TA-Lib library to be installed on your machine before the TA-Lib library for python can be installed properly (i.e. using pip install talib or conda).

To install predecessor TA-Lib, refer here.

Contributor

• Nathaniel Wihardjo
• Hanif Dean
• Kenneth Lee

Reference

• (https://github.com/yuriak/RLQuant/)
• Recurrent Reinforcement Learning: A Hybrid Approach
• Deep Direct Reinforcement Learning for Financial Signal Representation and Trading
• Using a Financial Training Criterion Rather than a Prediction Criterion
• A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem