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

[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

submitted by /u/JeffreyKR9410
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.