Home

Awesome

...Minimizing the mean square error on future experience.  - Richard S. Sutton

<a name="title"></a>BTGym

Scalable event-driven RL-friendly backtesting library. Build on top of Backtrader with OpenAI Gym environment API.

Backtrader is open-source algorithmic trading library:
GitHub: http://github.com/mementum/backtrader
Documentation and community:
http://www.backtrader.com/

OpenAI Gym is..., well, everyone knows Gym:
GitHub: http://github.com/openai/gym
Documentation and community:
https://gym.openai.com/

<a name="outline"></a>Outline

General purpose of this project is to provide gym-integrated framework for running reinforcement learning experiments in [close to] real world algorithmic trading environments.

DISCLAIMER:
Code presented here is research/development grade.
Can be unstable, buggy, poor performing and is subject to change.

Note that this package is neither out-of-the-box-moneymaker, nor it provides ready-to-converge RL solutions.
Think of it as framework for setting experiments with complex non-stationary stochastic environments.

As a research project BTGym in its current stage can hardly deliver easy end-user experience in as sense that
setting meaninfull  experiments will require some practical programming experience as well as general knowledge
of reinforcement learning theory.

News and update notes

<a name="contents"></a>Contents

<a name="install"></a>Installation

It is highly recommended to run BTGym in designated virtual environment.

Clone or copy btgym repository to local disk, cd to it and run: pip install -e . to install package and all dependencies:

git clone https://github.com/Kismuz/btgym.git

cd btgym

pip install -e .

To update to latest version::

cd btgym

git pull

pip install --upgrade -e .
Notes:

  1. BTGym requres Matplotlib version 2.0.2, downgrade your installation if you have version 2.1:

    pip install matplotlib==2.0.2

  2. LSOF utility should be installed to your OS, which can not be the default case for some Linux distributives, see: https://en.wikipedia.org/wiki/Lsof

<a name="start"></a>Quickstart

Making gym environment with all parmeters set to defaults is as simple as:

from btgym import BTgymEnv

MyEnvironment = BTgymEnv(filename='../examples/data/DAT_ASCII_EURUSD_M1_2016.csv',)

Adding more controls may look like:

from gym import spaces
from btgym import BTgymEnv

MyEnvironment = BTgymEnv(filename='../examples/data/DAT_ASCII_EURUSD_M1_2016.csv',
                         episode_duration={'days': 2, 'hours': 23, 'minutes': 55},
                         drawdown_call=50,
                         state_shape=dict(raw=spaces.Box(low=0,high=1,shape=(30,4))),
                         port=5555,
                         verbose=1,
                         )
See more options at Documentation: Quickstart >>
and how-to's in Examples directory >>.

<a name="description"></a> General description

<a name="problem"></a> Problem setting

<a name="data"></a> Data selection options for backtest agent training:

Notice: data shaping approach is under development, expect some changes. [7.01.18]

<a name="reference"></a>Documentation and Community

<a name="issues"></a> Known bugs and limitations:

<a name="roadmap"></a> TODO's and Road Map:

<a name="news"></a>News and updates:

<a href="https://stackexchange.com/users/10204071/andrew-muzikin"><img src="https://stackexchange.com/users/flair/10204071.png" width="208" height="58" alt="profile for Andrew Muzikin on Stack Exchange, a network of free, community-driven Q&amp;A sites" title="profile for Andrew Muzikin on Stack Exchange, a network of free, community-driven Q&amp;A sites" /></a>