Selim Amrouni

J.P.Morgan · AI Reseach

We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market, for which the typical example is foreign exchange. We show how a suitable design of parameterized families of reward functions coupled with associated shared policy learning constitutes an efficient solution to this problem. Precisely, w...

Market regimes is a popular topic in quantitative finance even though there is little consensus on the details of how they should be defined. They arise as a feature both in financial market prediction problems and financial market task performing problems. In this work we use discrete event time multi-agent market simulation to freely experiment i...

Model-free Reinforcement Learning (RL) requires the ability to sample trajectories by taking actions in the original problem environment or a simulated version of it. Breakthroughs in the field of RL have been largely facilitated by the development of dedicated open source simulators with easy to use frameworks such as OpenAI Gym and its Atari envi...

Business analysts create billions of slide decks, reports and documents annually. Most of these documents have well-defined structure comprising of similar content generated from data. We present 'AI pptX', a novel AI framework for creating and modifying documents as well as extract insights in the form of natural language sentences from data. AI p...