Trading with recurrent actor-critic reinforcement learning
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A3C trading
Note: Sorry for misleading naming - please use A3Ctrading.py for training and testtrading.py for testing.
Trading with recurrent actor-critic reinforcement learning - check paper and more detailed old report
Configuration: config.py
This file contains all the pathes and gloabal variables to be set up
Dataset: download from GDrive
After setting
config.pyplease 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,
Workerand
Test_Workerclasses
Training: A3C_training.py
Run this file, preferrable in
tmux. During training it will create files in
tensorboard_dirand in
model_dir
Testing: A3C_testing.ipynb
Jupyter notebookcontains 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} }