deep-RL-trading
Unverified ML strategy on Indices by golsun. BotFinder score 18 out of 100.
playing idealized trading games with deep reinforcement learning
Source: github
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deep-RL-trading
Playing trading games with deep reinforcement learning This repo is the code for this paper. Deep reinforcement learing is used to find optimal strategies in these two scenarios: Momentum trading: capture the underlying dynamics Arbitrage trading: utilize the hidden relation among the inputs Several neural networks are compared: Recurrent Neural Networks (GRU/LSTM) Convolutional Neural Network (CNN) Multi-Layer Perception (MLP) Dependencies You can get all dependencies via the Anaconda environment file, env.yml: conda env create -f env.yml Play with it Just call the main function python main.py You can play with model parameters (specified in main.py), if you get good results or any trouble, please contact me at gxiang1228@gmail.com
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