https://github.com/iusztinpaul/portfolio-management
Financial portfolio management using Deep Reinforcement Learning.
https://github.com/iusztinpaul/portfolio-management
deep-learning finance machine-learning portfolio-management pytorch reinforcement-learning
Last synced: 6 months ago
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Financial portfolio management using Deep Reinforcement Learning.
- Host: GitHub
- URL: https://github.com/iusztinpaul/portfolio-management
- Owner: iusztinpaul
- License: mit
- Created: 2021-07-21T15:53:52.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2021-07-21T18:11:46.000Z (about 4 years ago)
- Last Synced: 2025-03-26T06:11:19.097Z (7 months ago)
- Topics: deep-learning, finance, machine-learning, portfolio-management, pytorch, reinforcement-learning
- Language: Python
- Homepage:
- Size: 256 KB
- Stars: 7
- Watchers: 4
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Portfolio Management
This is an implementation of the portfolio management solution described in the following paper:
[A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://arxiv.org/abs/1706.10059).
The purpose of this project was educational & to make the code more flexible than the original implementation for further usages in different applications.
## Requirements
* Code tested with `Python 3.7.9`, `pytorch 1.8.0`, `cuda 11.1` & `cudnn 8.0` on `Windows 10`
* Requirements are installed from the `Pipfile.lock`
( all commands are run at the `Pipfile` folder level).
To install your dependencies run:
```shell
pipenv sync
```
To activate the environment:
```shell
pipenv shell
```
Finally install `pytorch` ( you should be in the pipenv environment while doing this step, `pipenv` does not support `--find links / -f`):
```shell
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
```
## Train
```shell
python train.py
--config_file config/crypto.yaml
--storage_path
/path/to/data/logging
```
## Test
```shell
python backtest.py
--config_file config\crypto.yaml
--storage_path
/path/to/data/logging
```
## Concrete Backtest
* These are the results after running the following command:
```shell
python backtest.py
--config_file
config\crypto_test_2017.yaml
--storage_path
\path\to\aiie_crypto_storage
```
#### Prices
#### Relative results
* After a six month period of trading the agent made a +40% profit. But we can see that the portfolio value
was not stable at all. Therefore, there is place for a lot of improvement.