DQN-Trading This is a framework based on deep reinforcement learning for stock market trading. This project is the implementation code for the two papers: - Learning financial asset-specific trading rules via deep reinforcement learning - A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules The deep reinforcement learning algorithm used here is Deep Q-Learning. Acknowledgement - Deep Q-Learning tutorial in pytorch Requirements Install pytorch using the following commands. This is for CUDA 11.1 and python 3.8: - python = 3.8 - pandas = 1.3.2 - numpy = 1.21.2 - matplotlib = 3.4.3 - cython = 0.29.24 - scikit-learn = 0.24.2 TODO List - [X] Right now this project does not have a code for getting user hyper-parameters from terminal and running the code. We preferred writing a jupyter notebook (Main.ipynb) in which you can set the input data, the model, along with setting the hyper-parameters. - [X] The project also does not have a code to do Hyper-parameter search (its easy to implement). - [X] You can also set the seed for running the experiments in th

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