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q-fin.CP2026
LOB-ID: Evaluating Synthetic Market Data by Inception Distances
Andreea Bacalum, Zhuohan Wang, Ollie Olby +2
Generative models of limit orderbook (LOB) data have advanced rapidly, but their evaluation often focuses on stylised facts and selected market statistics. These measures provide u…
q-fin.CP2025
TABL-ABM: A Hybrid Framework for Synthetic LOB Generation
Ollie Olby, Rory Baggott, Namid Stillman
The recent application of deep learning models to financial trading has heightened the need for high fidelity financial time series data. This synthetic data can be used to supplem…
q-fin.CP2025
Right Place, Right Time: Market Simulation-based RL for Execution Optimisation
Ollie Olby, Andreea Bacalum, Rory Baggott +1
Execution algorithms are vital to modern trading, they enable market participants to execute large orders while minimising market impact and transaction costs. As these algorithms…