About us
The Thalesians are a group of dedicated professionals with an interest in Artificial Intelligence (AI) / Machine Learning (ML), quantitative finance, economics, mathematics, physics and computer science, not necessarily in that order. We currently run events London, New York, Budapest, Frankfurt and Prague!
Please also visit our main Thalesians web page here too to learn more about us!
The Thalesians are a member of Level39 - Europe's largest technology accelerator for finance, retail, cyber-security and future cities technology companies.
We consult, train, and write software. Our offering can be found on http://ai.thalesians.com/
Our GitHub page contains our open source Python financial analysis library PyThalesians.
If you are a full-time student, between jobs, or for any other reason would struggle with our Meetup dues, please let us know and we'll mark you as exempt from them!
Upcoming events
1
Hybrid Event: Maxim Bichuch - Optimal Growth Rate for Liquidity Providers
140 W 62nd St, New York, NY, USIAQF & Thalesians Seminar Series: Optimal Long-Term Growth Rate for Liquidity Providers in Automated Market Makers. A Seminar by Maxim Bichuch.
6:30 PM Seminar Begins
8:00 PM ReceptionFordham University
McNally Amphitheater
140 West 62nd Street
New York, NY 10023Hybrid Event
Free Registration!For Virtual Attendees: Please email [web@iaqf.org](mailto:web@iaqf.org) for the link.
Abstract:
We derive the optimal long-term growth rate for an agent investing in a market composed of a numéraire asset, a risky asset subject to transaction costs, and a liquidity pool within an Automated Market Maker (AMM). We first establish the necessary conditions to ensure a no-arbitrage environment within this market structure. Under these conditions, we determine the asymptotically optimal trading strategy for liquidity providers. Finally, we provide economic intuition for the strategy’s sensitivity to various market parameters, supported by numerical illustrations of our theoretical results.Bio:
Maxim Bichuch holds a M.S. from NYU and a Ph.D. from Carnegie Mellon University both in Financial Mathematics. He was a Postdoctoral Research Associate & Lecturer in the ORFE department in Princeton, and an Assistant Professor at Worcester Polytechnic Institute and Johns Hopkins University, before joining the department of Mathematics at The University at Buffalo. Prior to obtaining his Ph.D. He has also gained corporate experience working for Citigroup and Bear Stearns. His research interests include optimal investment, optimal control, stochastic volatility, credit, funding and counterparty risks, and most recently electricity markets, machine learning and AI, decentralized finance and fintech.13 attendees
Past events
455
