5 citations · 5 across the 5 of their papers we have counts for
6 papers
Deep Learning of Robust Market Making under Regime-Switching Order Flow
Felipe Moret, Fabrizio Lillo
Classical market-making strategies based on stochastic control, such as the Avellaneda-Stoikov and the Guéant-Lehalle-Fernandez-Tapia (GLFT) extension, provide closed-form quoting…
Can Reinforcement Learning Efficiently Discover Price Manipulation?
Ioanna-Yvonni Tsaknaki, Andrea Macrì, Fabrizio Lillo
In this paper, we investigate whether a model-free RL agent can identify and exploit price manipulation opportunities more effectively than a traditional model-based approach that…
Deep reinforcement learning for optimal trading with partial information
Andrea Macrì, Sebastian Jaimungal, Fabrizio Lillo
Reinforcement Learning (RL) has attracted increasing interest in financial applications, including optimal trading and execution. However, the use of RL for optimal trading strateg…
Deviations from Tradition: Stylized Facts in the Era of DeFi
Daniele Maria Di Nosse, Federico Gatta, Fabrizio Lillo +1
Decentralized Exchanges (DEXs) are now a significant component of the financial world where billions of dollars are traded daily. Differently from traditional markets, which are ty…
Deviations from the Nash equilibrium in a two-player optimal execution game with reinforcement learning
Fabrizio Lillo, Andrea Macrì
The use of reinforcement learning algorithms in financial trading is becoming increasingly prevalent. However, the autonomous nature of these algorithms can lead to unexpected outc…
Reinforcement Learning for Optimal Execution when Liquidity is Time-Varying
Andrea Macrì, Fabrizio Lillo
Optimal execution is an important problem faced by any trader. Most solutions are based on the assumption of constant market impact, while liquidity is known to be dynamic. Moreove…