golsun / Deep Rl Trading
Licence: mit
playing idealized trading games with deep reinforcement learning
Stars: ✭ 228
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python
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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 [email protected]
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].