no code implementations • 9 Jun 2023 • David Byrd
In this article, I consider a series of experiments in which an intelligent stock trading agent maximizes profit but may also inadvertently learn to spoof the market in which it participates.
no code implementations • 20 Feb 2022 • David Byrd, Vaikkunth Mugunthan, Antigoni Polychroniadou, Tucker Hybinette Balch
Privacy-preserving federated learning enables a population of distributed clients to jointly learn a shared model while keeping client training data private, even from an untrusted server.
no code implementations • 12 Oct 2020 • David Byrd, Antigoni Polychroniadou
This reduces the risk of exposing sensitive data, but it is still possible to reverse engineer information about a client's private data set from communicated model parameters.
no code implementations • 15 Jun 2020 • David Byrd, Sruthi Palaparthi, Maria Hybinette, Tucker Hybinette Balch
There is a pervasive assumption that low latency access to an exchange is a key factor in the profitability of many high-frequency trading strategies.
no code implementations • 10 Dec 2019 • Svitlana Vyetrenko, David Byrd, Nick Petosa, Mahmoud Mahfouz, Danial Dervovic, Manuela Veloso, Tucker Hybinette Balch
Machine learning (especially reinforcement learning) methods for trading are increasingly reliant on simulation for agent training and testing.
no code implementations • 22 Aug 2019 • David Byrd, Tucker Hybinette Balch
In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices.
3 code implementations • 26 Apr 2019 • David Byrd, Maria Hybinette, Tucker Hybinette Balch
ABIDES is designed from the ground up to support AI agent research in market applications.
Multiagent Systems