5 papers
FlowLOB: Efficient and Controllable Limit Order Book Generation with Flow Matching
Zhuohan Wang, Andreea Bacalum, Ollie Olby +2
Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation…
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…
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…
To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions
Dimitrios Emmanoulopoulos, Ollie Olby, Justin Lyon +1
Large language models (LLMs) are increasingly deployed in agentic frameworks, in which prompts trigger complex tool-based analysis in pursuit of a goal. While these frameworks have…