Deep Reinforcement Learning For Trading
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Notebooks and code for Alpha Architect post on reinforcement learning.

  • Tic-Tac-Toe.ipynb - Table-based reinforcement learning to play Tic-Tac-Toe, and analogous if pointless deep learning algo
  • Cart-Pole.ipynb - Building deep reinforcement learning algos from scratch with Keras for OpenAI environments like Cartpole and LunarLander.
  • Ray_tune.ipynb - Similar but with state of the art RL from UC Berkeley Ray project
  • Trading_with_RL.ipynb - Algos to trade fake market data, inspired by Gordon Ritter paper Machine Learning for Trading. This should run in Google Colab.

Typical installation procedure:

  • Install Anaconda python data science distribution

  • Make an environment like

    conda create --name tf tensorflow

    or if you have Nvidia GPU

    conda create --name tf_gpu tensorflow-gpu 

    This should install requirements like working Nvidia drivers

  • Upgrade TensorFlow to latest version with

    pip install --upgrade tensorflow
  • Install additional requirements as necessary - requirements.txt has python modules installed at time of testing.

    pip install -r requirements.txt
  • TensorFlow Docker install may also be a good way to start but has not been tested.

  • Run notebooks using

    jupyter notebook
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