Reinforcement-Learning-for-Market-Making
Unverified ML strategy on Multi by KodAgge. BotFinder score 18 out of 100.
Using tabular and deep reinforcement learning methods to infer optimal market making strategies
Source: github
BotFinder analysis pending.
Reinforcement-Learning-for-Market-Making
Reinforcement Learning for Market Making This is the GitHub repository for our MSc thesis project Reinforcement Learning for Market Making in financial mathematics at KTH Royal Institute of Technology. The thesis was written during the spring of 2022 in collaboration with Skandinaviska Enskilda Banken (SEB) and can be found here. In this project, we have used tabular and deep reinforcement learning methods in order to find optimal market making strategies. Continue reading for more! What is Reinforcement Learning? For anyone not familiar with reinforcement learning (RL), it's a concept that stems from the idea of how humans and animals learn: by interacting in an environment and learning from experience. RL has lately gotten a lot of attention, and one of the most famous examples is DeepMind's AlphaGo: AlphaGo was the first computer program to beat a world champion in the board game Go. If you haven't seen the documentary about AlphaGo, you should check it out. It's available on Youtube here. Other cool stuff that has been done is teaching a computer program to play Atari arcade game
⚠ No verified equity curve — no track-record source connected.
Drawdown profile
Data unavailable — contact the owner.
Verification ledger
How the score has moved
Recalculated at each data collection. Transparency means showing the bad weeks too.
No score history is stored yet — only the current score is shown.
Reviews & comments
No reviews collected from the source yet.
⚠ No live verification account connected — ask for proof before buying.
Alerts on changes: coming soon
Prop-firm compatibility not provided.
Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)