February 22, 2020

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evgps/a3c_trading

evgps/a3c_trading

Trading with recurrent actor-critic reinforcement learning

repo name evgps/a3c_trading
repo link https://github.com/evgps/a3c_trading
homepage
language Jupyter Notebook
size (curr.) 7506 kB
stars (curr.) 43
created 2018-06-04
license

A3C trading

Trading with recurrent actor-critic reinforcement learning - check paper

Full_UML

Configuration: config.py

This file contains all the pathes and gloabal variables to be set up

Dataset: download from GDrive

After setting config.py please run this file to download and preprocess the data need for training and evaluation

Environment: trader_gym.py

OpenAI.gym-like environment class

Model: A3C_class.py

This file is containing AC_network, Worker and Test_Worker classes

Training: A3C_training.py

Run this file, preferrable in tmux. During training it will create files in tensorboard_dir and in model_dir

Testing: A3C_testing.ipynb

Jupyter notebook contains all for picturing

Cite as:

@article{ponomarev2019using, title={Using Reinforcement Learning in the Algorithmic Trading Problem}, author={Ponomarev, ES and Oseledets, IV and Cichocki, AS}, journal={Journal of Communications Technology and Electronics}, volume={64}, number={12}, pages={1450–1457}, year={2019}, publisher={Springer} }

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