FinRL is an open source framework to help practitioners pipeline the development of trading strategies. In deep reinforcement learning (DRL), an agent learns by continuously interacting with an environment, in a trial-and-error manner, making sequential decisions under uncertainty and achieving a balance between exploration and exploitation. The open source community AI4Finance (to efficiently automate trading) provides resources about deep reinforcement learning (DRL) in quantitative finance, and aim to accelerate the paradigm shift from conventional machine learning approach to RLOps in finance.
To contribute? Please check the end of this page.
Feel free to report bugs via Github issues, join the mailing list: AI4Finance, and discuss FinRL in slack channel:
Roadmaps of FinRL:
FinRL 1.0: entry-level toturials for beginners, with a demonstrative and educational purpose.
FinRL 2.0: intermediate-level framework for full-stack developers and professionals. Check out ElegantRL
FinRL provides a unified machine learning framework for various markets, SOTA DRL algorithms, benchmark finance tasks (portfolio allocation, cryptocurrency trading, high-frequency trading), live trading, etc.
We published papers in FinTech and now arrive at this project:
A YouTube video about FinRL library. [YouTube] AI4Finance Channel for quant finance.
We implemented Deep Q Learning (DQN), Double DQN, DDPG, A2C, SAC, PPO, TD3, GAE, MADDPG, MuZero, etc. using PyTorch and OpenAI Gym.
# grant access to execute scripting (read it, it's harmless)
$ sudo chmod -R 777 docker/bin
# build the container!
$ ./docker/bin/build_container.sh
# start notebook on port 8887!
$ ./docker/bin/start_notebook.sh
# proceed to party!
Build the container:
$ docker build -f docker/Dockerfile -t finrl docker/
Start the container:
$ docker run -it --rm -v ${PWD}:/home -p 8888:8888 finrl
Note: The default container run starts jupyter lab in the root directory, allowing you to run scripts, notebooks, etc.
Clone this repository:
git clone https://github.com/AI4Finance-LLC/FinRL-Library.git
Install the unstable development version of FinRL:
pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git
For OpenAI Baselines, you'll need system packages CMake, OpenMPI and zlib. Those can be installed as follows:
sudo apt-get update && sudo apt-get install cmake libopenmpi-dev python3-dev zlib1g-dev libgl1-mesa-glx
Installation of system packages on Mac requires Homebrew. With Homebrew installed, run the following:
brew install cmake openmpi
To install stable-baselines on Windows, please look at the documentation.
cd into this repository:
cd FinRL-Library
Under folder /FinRL-Library, create a Python virtual-environment:
pip install virtualenv
Virtualenvs are essentially folders that have copies of python executable and all python packages.
Virtualenvs can also avoid packages conflicts.
Create a virtualenv venv under folder /FinRL-Library
virtualenv -p python3 venv
To activate a virtualenv:
source venv/bin/activate
To activate a virtualenv on windows:
venv\Scripts\activate
The script has been tested running under Python >= 3.6.0, with the following packages installed:
pip install -r requirements.txt
Stable-Baselines3 is a set of improved implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines. If you have questions regarding Stable-baselines package, please refer to Stable-baselines3 installation guide. Install the Stable Baselines package using pip:
pip install stable-baselines3[extra]
A migration guide from SB2 to SB3 can be found in the documentation.
Still Under Development
python main.py --mode=train
Use Quantopian's pyfolio package to do the backtesting.
The stock data we use is pulled from Yahoo Finance API.
(The following time line is used in the paper; users can update to new time windows.)
@article{finrl2020,
author = {Liu, Xiao-Yang and Yang, Hongyang and Chen, Qian and Zhang, Runjia and Yang, Liuqing and Xiao, Bowen and Wang, Christina Dan},
journal = {Deep RL Workshop, NeurIPS 2020},
title = {FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance},
url = {https://arxiv.org/pdf/2011.09607.pdf},
year = {2020}
}
Will maintain FinRL with the "AI4Finance" community and welcome your contributions!
Please check the contributing guidances.
Thanks to all the people who contribute.
Support more markets, so that the users can test their stategies.
Maintain a pool of DRL algorithms that can be treated as SOTA implementations.
To help quants have better evaluations, will maintain benchmarks for many trading tasks, upon which you can improve for your own tasks.
Supporting live trading can close the simulation-reality gap, it will enable quants to switch to the real market when they are confident with their strategies.
MIT
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