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Trading with recurrent actor-critic reinforcement learning
| Date | Stars |
|---|---|
| 2026-07-24 | 437 |
| 2026-07-25 | 437 |
| 2026-07-28 | 437 |
| 2026-07-30 | 437 |
| 2026-07-31 | 437 |
| 2026-08-06 | 437 |
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# A3C trading
*Note: Sorry for misleading naming - please use A3C_trading.py for training and test_trading.py for testing.*
Trading with recurrent actor-critic reinforcement learning - check [paper](https://arxiv.org/abs/2002.11523) and more detailed old [report](https://drive.google.com/file/d/1PK7a8pfZGrQs6WfVO-kNdgNOfvkdi5CW/view?usp=sharing)
## Configuration: `config.py`
This file contains all the pathes and gloabal variables to be set up
## Dataset: download from [GDrive](https://drive.google.com/drive/folders/1T2Xj2N0UrZ2ZtHAI8eRAFdz23hGRm5I3?usp=sharing)
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}
}
Excerpt of 1,459 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b6deb09c47f29fea, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning
matched fp:b6deb09c47f29fea, topic:tensorflow
matched fp:b6deb09c47f29fea, topic:trading