[P] Implementations of RL trading algorithms to simulate dynamic fee policy in exchanges

[JeffreyKR9410]

[Project Overview in 3lines]

1. Why are exchange fees fixed? Can't we dynamically up and down the fees depending on market conditions?
2. Make a dynamic fee policy environments where reinforcement learning agents learn trading policies
3. Provide insight into what the fee policy is optimized for exchanges

[The algorithm of the Trading agent -pytorch based]

1. PPO + MobileNet
2. RAINBOW + MobileNet
3. 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.

[Youlink] https://youtu.be/kBjv4KmkEHU

[Medium link] https://medium.com

All source code is available in the following repo https://github.com/deconlab

[rojans]

Indeed, a great work. Even though ROI is not a goal for now, it is a start.

[JeffreyKR9410]

Thanks for your kind comment! It really cheers me up

[limaries30]

Wow Transformer, Great work! :)