deep-RL-time-series.zip_强化学习_强化学习 预测_时间序列算法_深度强化学习_预测

# **Playing trading games with deep reinforcement learning** This repo is the code for this [paper](https://arxiv.org/abs/1803.03916). 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](https://conda.io/docs/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file) environment file, [env.yml](https://github.com/golsun/deep-RL-time-series/blob/master/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 [email protected]