[P] Implementations of RL trading algorithms to simulate dynamic fee policy in exchanges
[Project Overview in 3lines]
- Why are exchange fees fixed? Can’t we dynamically up and down the fees depending on market conditions?
- Make a dynamic fee policy environments where reinforcement learning agents learn trading policies
- Provide insight into what the fee policy is optimized for exchanges
[The algorithm of the Trading agent -pytorch based]
- PPO + MobileNet
- RAINBOW + MobileNet
- RAINBOW + Transformer(MultiheadAttention)
[Your Utility] If you want to use reinforcement learning in stock investment, you can use this source and it will be a baseline! (However, in this simulation, ROI was not the goal. So the performance is not guaranteed. 🙁 )
Below is a 2:30 second video link and a medium article link describing the project.
[Medium link] https://medium.com
All source code is available in the following repo https://github.com/deconlab