5 papers
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…
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…
Agent-based Liquidity Risk Modelling for Financial Markets
Perukrishnen Vytelingum, Rory Baggott, Namid Stillman +4
In this paper, we describe a novel agent-based approach for modelling the transaction cost of buying or selling an asset in financial markets, e.g., to liquidate a large position a…
Neuro-Symbolic Traders: Assessing the Wisdom of AI Crowds in Markets
Namid R. Stillman, Rory Baggott
Deep generative models are becoming increasingly used as tools for financial analysis. However, it is unclear how these models will influence financial markets, especially when the…
Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks
Namid R. Stillman, Rory Baggott, Justin Lyon +4
The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenari…