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‎README.md

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‎README.rst

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================================
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|Crypto Trading Environment|
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================================
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.. |Crypto Trading Environment| raw:: html
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<h1 align='center'>
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<img src = 'https://github.com/ClementPerroud/Gym-Trading-Env/raw/main/docs/source/images/logo_light-bg.png' width='500'>
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</h1>
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.. raw:: html
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<section class="shields" align="center">
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<a href="https://www.python.org/">
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<img src="https://img.shields.io/badge/python-v3-brightgreen.svg"
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alt="python">
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</a>
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<a href="https://pypi.org/project/gym-trading-env/">
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<img src="https://img.shields.io/badge/pypi-v1.1.3-brightgreen.svg"
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alt="PyPI">
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</a>
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<a href="https://github.com/ClementPerroud/Gym-Trading-Env/blob/main/LICENSE.txt">
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<img src="https://img.shields.io/badge/license-MIT%202.0%20Clause-green"
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alt="Apache 2.0 with Commons Clause">
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</a>
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<a href='https://gym-trading-env.readthedocs.io/en/latest/?badge=latest'>
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<img src='https://readthedocs.org/projects/gym-trading-env/badge/?version=latest' alt='Documentation Status' />
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</a>
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<br>
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<a href="https://github.com/ClementPerroud/Gym-Trading-Env">
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<img src="https://img.shields.io/github/stars/ClementPerroud/gym-trading-env?style=social" alt="Github stars">
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</a>
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</section>
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Cryto Trading Env is an OpenAI Gym environment for simulating stocks and train Reinforcement Learning (RL) trading agents.
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It was designed to be fast and customizable for easy RL trading algorythms implementation.
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+---------------------------------------------------------------------------------+
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| `Documentation <https://gym-trading-env.readthedocs.io/en/latest/index.html>`_ |
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+---------------------------------------------------------------------------------+
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Key features
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---------------
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This package aims to greatly simplify the research phase by offering :
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* Easy and quick download technical data on several exchanges
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* A simple and fast environment for the user and the AI, but which allows complex operations (Short, Margin trading).
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* A high performance rendering (can display several hundred thousand candles simultaneously), customizable to visualize the actions of its agent and its results.
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* (Coming soon) An easy way to backtest any RL-Agents or any king
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.. image:: docs/source/images/render.gif
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Installation
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---------------
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Crypto Trading Env supports Python 3.9+ on Windows, Mac, and Linux. You can install it using pip:
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.. code-block:: console
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pip install gym-trading-env
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Or using git :
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.. code-block:: console
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git clone https://github.com/ClementPerroud/Gym-Trading-Env
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`Documentation available here <https://gym-trading-env.readthedocs.io/en/latest/index.html>`_
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‎docs/source/images/render.gif

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‎docs/source/index.rst

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.. title:: Crypto Trading Environment for Reinforcement Learning
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===================
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|Crypto Trading Environment|
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===================
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================================
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.. |Crypto Trading Environment| raw:: html
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.. |Crypto Trading Environment| image:: images/logo_light-bg.png
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:width: 500
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<h1 align='center'>
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<img src = 'https://github.com/ClementPerroud/Gym-Trading-Env/raw/main/docs/source/images/logo_light-bg.png' width='500'>
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</h1>
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.. raw:: html
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<section class="shields">
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<section class="shields" align="center">
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<a href="https://www.python.org/">
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<img src="https://img.shields.io/badge/python-v3-brightgreen.svg"
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alt="python">
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</a>
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<a href="https://pypi.org/project/lib-pybroker/">
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<a href="https://pypi.org/project/gym-trading-env/">
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<img src="https://img.shields.io/badge/pypi-v1.1.3-brightgreen.svg"
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alt="PyPI">
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</a>
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Cryto Trading Env is an OpenAI Gym environment for simulating stocks and train Reinforcement Learning (RL) trading agents.
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It was designed to be fast and customizable for easy RL trading algorythms implementation.
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+---------------------------------------------------------------------------------+
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| `Github <https://github.com/ClementPerroud/Gym-Trading-Env>`_ |
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+---------------------------------------------------------------------------------+
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Key features
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---------------
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* A high performance rendering (can display several hundred thousand candles simultaneously), customizable to visualize the actions of its agent and its results.
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* (Coming soon) An easy way to backtest any RL-Agents or any king
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.. image:: images/render.gif
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Installation
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---------------
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.. code-block:: console
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git clone https://github.com/ClementPerroud/Gym-Trading-Env
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Contents
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---------------
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.. toctree::
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Introduction <self>
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getting_started
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.. toctree::
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:caption: 🤖 Reinforcement Learning
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rl_tutorial
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customization
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advanced
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multi_datasets
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vectorize_env
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.. toctree::
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:caption: 🦾 Functionnalities

‎docs/source/advanced.rst ‎docs/source/multi_datasets.rst

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Advanced
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Multi datasets environment
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===========
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Now, you know how to create a environment for your RL agent with one dataset. But something seems weird ... One good dataset is about 100k data points long. But it sounds very little compared to the millions steps that RL-Agent needs to get good.
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How to use ?
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^^^^^^^^^^^^^
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You need to specify a `glob path<https://docs.python.org/3.6/library/glob.html>`_ that gather all of the datasets (in .pkl format).
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You need to specify a `glob path <https://docs.python.org/3.6/library/glob.html>`_ that gather all of the datasets (in .pkl format).
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Imagine you have several preprocessed dataset in a folder named ``preprocessed_data`` .
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.. code-block:: python

‎docs/source/rl_tutorial.rst

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* ``0.5`` : 50% in BTC & 50% in USD
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* Even : ``0.1`` : 10% in BTC & 90% in USD ....
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In fact, it is way simpler for a RL-agent to work with positions. This way, it can easily make complex operation with a simple action space.
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.. note::
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It is way simpler for a RL-agent to work with positions. This way, it can easily make complex operation with a simple action space.
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.. code-block::python
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