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https://github.com/notadamking/RLTrader
A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym
https://github.com/notadamking/RLTrader
Last synced: about 1 month ago
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A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym
- Host: GitHub
- URL: https://github.com/notadamking/RLTrader
- Owner: notadamking
- License: gpl-3.0
- Created: 2019-04-27T18:35:15.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-01-22T05:01:41.000Z (almost 3 years ago)
- Last Synced: 2024-10-29T17:40:21.529Z (about 1 month ago)
- Language: Python
- Homepage: https://discord.gg/ZZ7BGWh
- Size: 16.9 MB
- Stars: 1,729
- Watchers: 133
- Forks: 535
- Open Issues: 42
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome-deep-rl - RLTrader - A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym. (Environments)
- awesome-google-colab - RLTrader - A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym (Applications)
README
# RLTrader — The Predecessor to [TensorTrade](https://github.com/notadamking/tensortrade)
[](https://travis-ci.org/notadamking/RLTrader)
[](http://makeapullrequest.com)
[](https://opensource.org/licenses/GPL-3.0/)
[](https://discord.gg/ZZ7BGWh)
[](https://www.python.org/downloads/release/python-360/)
[](https://github.com/notadamking/RLTrader)
Development on this library has slowed down, in favor of working on TensorTrade - a framework for trading with RL: https://github.com/notadamking/tensortrade
If you'd like to learn more about how we created this agent, check out the Medium article: https://towardsdatascience.com/creating-bitcoin-trading-bots-that-dont-lose-money-2e7165fb0b29
Later, we optimized this repo using feature engineering, statistical modeling, and Bayesian optimization, check it out:
https://towardsdatascience.com/using-reinforcement-learning-to-trade-bitcoin-for-massive-profit-b69d0e8f583b
Discord server: https://discord.gg/ZZ7BGWh
Data sets: https://www.cryptodatadownload.com/data/northamerican/
# Getting Started
### How to find out if you have nVIDIA GPU?
Linux:
```bash
sudo lspci | grep -i --color 'vga\|3d\|2d' | grep -i nvidia
```
If this returns anything, then you should have an nVIDIA card.
### Basic usage
The first thing you will need to do to get started is install the requirements. If your system has an nVIDIA GPU that you should start by using:
```bash
cd "path-of-your-cloned-rl-trader-dir"
pip install -r requirements.txt
```
More information regarding how you can take advantage of your GPU while using docker: https://github.com/NVIDIA/nvidia-docker
If you have another type of GPU or you simply want to use your CPU, use:
```bash
pip install -r requirements.no-gpu.txt
```
Update your current static files, that are used by default:
```bash
python ./cli.py update-static-data
```
Afterwards you can simply see the currently available options:
```bash
python ./cli.py --help
```
or simply run the project with default options:
```bash
python ./cli.py optimize
```
If you have a standard set of configs you want to run the trader against, you can specify a config file to load configuration from. Rename config/config.ini.dist to config/config.ini and run
```bash
python ./cli.py --from-config config/config.ini optimize
```
```bash
python ./cli.py optimize
```
### Testing with vagrant
Start the vagrant box using:
```bash
vagrant up
```
Code will be located at /vagrant. Play and/or test with whatever package you wish.
Note: With vagrant you cannot take full advantage of your GPU, so is mainly for testing purposes
### Testing with docker
If you want to run everything within a docker container, then just use:
```bash
./run-with-docker (cpu|gpu) (yes|no) optimize
```
- cpu - start the container using CPU requirements
- gpu - start the container using GPU requirements
- yes | no - start or not a local postgres container
Note: in case using yes as second argument, use
```bash
python ./ cli.py --params-db-path "postgres://rl_trader:rl_trader@localhost" optimize
```
The database and it's data are pesisted under `data/postgres` locally.
If you want to spin a docker test environment:
```bash
./run-with-docker (cpu|gpu) (yes|no)
```
If you want to run existing tests, then just use:
```bash
./run-tests-with-docker
```
# Fire up a local docker dev environment
```bash
./dev-with-docker
```
# Windows 10 installation, no CUDA installation needed
conda create --name rltrader python=3.6.8 pip git
conda activate rltrader
conda install tensorflow-gpu
git clone https://github.com/notadamking/RLTrader
pip install -r RLTrader/requirements.txt
# Optimizing, Training, and Testing
While you could just let the agent train and run with the default PPO2 hyper-parameters, your agent would likely not be very profitable. The `stable-baselines` library provides a great set of default parameters that work for most problem domains, but we need to better.
To do this, you will need to run `optimize.py`.
```bash
python ./optimize.py
```
This can take a while (hours to days depending on your hardware setup), but over time it will print to the console as trials are completed. Once a trial is completed, it will be stored in `./data/params.db`, an SQLite database, from which we can pull hyper-parameters to train our agent.
From there, agents will be trained using the best set of hyper-parameters, and later tested on completely new data to verify the generalization of the algorithm.
Feel free to ask any questions in the Discord!
# Google Colab
Enter and run the following snippet in the first cell to load RLTrader into a Google Colab environment. Don't forget to set hardware acceleration to GPU to speed up training!
```
!git init && git remote add origin https://github.com/notadamking/RLTrader.git && git pull origin master
!pip install -r requirements.txt
```
# Common troubleshooting
##### The specified module could not be found.
Normally this is caused by missing mpi module. You should install it according to your platorm.
- Windows: https://docs.microsoft.com/en-us/message-passing-interface/microsoft-mpi
- Linux/MacOS: https://www.mpich.org/downloads/
# Contributing
Contributions are encouraged and I will always do my best to get them implemented into the library ASAP. This project is meant to grow as the community around it grows. Let me know if there is anything that you would like to see in the future or if there is anything you feel is missing.
**Working on your first Pull Request?** You can learn how from this _free_ series [How to Contribute to an Open Source Project on GitHub](https://egghead.io/series/how-to-contribute-to-an-open-source-project-on-github)
# Support
Want to show your support for this project and help it grow?
Head over to the successor framework: https://github.com/notadamking/tensortrade
Supporters:
* Ap9944
* KILLth
* Nex