AITrading

Reinforcement Learning for trading cryptocurrencies, stocks and forex

Conclusion:

• Works well in patterned data

• Works all right in less volatile data

• Doesn work in volatile data

Python Virtual Environment

Using Conda

https://carpentries-incubator.github.io/introduction-to-conda-for-data-scientists/02-working-with-environments/index.html

Install virtual environment in current project folder

• conda create --prefix ./env python=3.8
• conda activate ./env
• conda remove --prefix /path/to/conda-env/ --all

Install virutal environmenbt in default home folder

• conda create --name env_tensortrade python=3.8
• conda remove --name my-first-conda-env --all
• conda list --name basic-scipy-env

Using Python venv

• python3 -m venv <name_of_virtualenv>
• virtualenv venv
• source ./venv/bin/activate

Documentation

This directory contains the sources (.md and .rst files) for the documentation. The main index page is defined in source/index.rst. The Sphinx options and plugins are found in the source/conf.py file. The documentation is generated in full by calling make html which also automatically generates the Python API documentation from docstrings.

Sphinx and ReadtheDocs

• Sphinx

• Read the Docs theme

• Sphinx Markdown support

• Sample Doc

pip install sphinx

pip install sphinx_rtd_theme

pip install --upgrade myst-parser

Create folder for documentation

mkdir docs

cd docs

sphinx-quickstart

make html

Update conf.py

Building documentation locally

Dependencies must be installed using make sync from the project root. Run make docs-build from project root, or make html from the docs/ subfolder (this one).

Note this can take some time as some of the notebooks may be executed during the build process. The resulting documentation is located in the build directory with build/html/index.html marking the homepage.

Sphinx extensions and plugins

We use various Sphinx extensions and plugins to build the documentation:

• recommonmark - to handle both .rst and .md
• sphinx.ext.napoleon - support extracting Numpy style doctrings for API doc generation
• sphinx_autodoc_typehints - support parsing of typehints for API doc generation
• sphinxcontrib.apidoc - automatic running of sphinx-apidoc during the build to document API
• nbsphinx - parsing Jupyter notebooks to generate static documentation
• nbsphinx_link - support linking to notebooks outside of Sphinx source directory via .nblink files

The full list of plugins and their options can be found in source/conf.py.

Reference

TensorTrade Source Code https://github.com/tensortrade-org/tensortrade

TensorTrade Doc - HTML https://www.tensortrade.org/en/latest/index.html

TensorTrade Doc - PDF https://readthedocs.org/projects/tensortrade/downloads/pdf/latest/

(Guide - English) Trade and Invest Smarter — The Reinforcement Learning Way https://towardsdatascience.com/trade-smarter-w-reinforcement-learning-a5e91163f315

Using TensorTrade for Making a Simple Trading Algorithm https://levelup.gitconnected.com/using-tensortrade-for-making-a-simple-trading-algorithm-6fad4d9bc79c

(Guide - CN Translated) TensorTrade：基于深度强化学习的Python交易框架 https://cloud.tencent.com/developer/article/1525771?tt_from=copy_link&utm_source=copy_link&utm_medium=toutiao_ios&utm_campaign=client_share

(Guide - RL) Reinforcement Q-Learning from Scratch in Python with OpenAI Gym https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/

(Guide - PPO) PPO Hyperparameters and Ranges https://medium.com/aureliantactics/ppo-hyperparameters-and-ranges-6fc2d29bccbe

Train a Deep Q Network with TF-Agents https://www.tensorflow.org/agents/tutorials/1_dqn_tutorial

Deep Reinforcement Learning for Automated Stock Trading https://towardsdatascience.com/deep-reinforcement-learning-for-automated-stock-trading-f1dad0126a02

【莫烦Python】强化学习 Reinforcement Learning

https://www.bilibili.com/video/BV13W411Y75P?from=search&seid=13844167983297755236

【李宏毅】2020 最新课程 (完整版) 强化学习

https://www.bilibili.com/video/BV1UE411G78S?from=search&seid=13844167983297755236