3 papers
q-fin.TR2024
Consistent time travel for realistic interactions with historical data: reinforcement learning for market making
Vincent Ragel, Damien Challet
Reinforcement learning works best when the impact of the agent's actions on its environment can be perfectly simulated or fully appraised from available data. Some systems are howe…
q-fin.ST2023
Recurrent Neural Networks with more flexible memory: better predictions than rough volatility
Damien Challet, Vincent Ragel
We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes…
q-fin.TR2023
Interpretable ML for High-Frequency Execution
Timothée Fabre, Vincent Ragel
Order placement tactics play a crucial role in high-frequency trading algorithms and their design is based on understanding the dynamics of the order book. Using high quality high-…