Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects

Belcak, Peter; Calliess, Jan-Peter; Zohren, Stefan

doi:10.1007/978-3-030-94548-0_4

Cited by 4 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Agent-based modelling has, in recent years, allowed for the design of highfidelity simulated markets (Belcak et al, 2020;Byrd et al, 2020). These artificial markets can run millions of in-silico trials to test counterfactual theories, research emergent phenomena, and train and test algorithms.…”

Section: Market Simulator Enginementioning

confidence: 99%

Black-Box Model Risk in Finance

Cohen¹,

Snow²,

Szpruch³

2023

Machine Learning and Data Sciences for Financial Markets

View full text Add to dashboard Cite

Machine learning models are increasingly used in a wide variety of financial settings. The difficulty of understanding the inner workings of these systems, combined with their wide applicability, has the potential to lead to significant new risks for users; these risks need to be understood and quantified. In this chapter, we will focus on a well studied application of machine learning techniques, to pricing and hedging of financial options. Our aim will be to highlight the various sources of risk that the introduction of machine learning emphasises or de-emphasises, and the possible risk mitigation and management strategies that are available.

show abstract

Section: Market Simulator Enginementioning

confidence: 99%

Black-Box Model Risk in Finance

Cohen¹,

Snow²,

Szpruch³

2023

Machine Learning and Data Sciences for Financial Markets

View full text Add to dashboard Cite

show abstract

“…By configuring agents that trade using common strategies, such as market makers, momentum traders, and mean reversion traders, the synthetic LOB can closely approximate the stylised facts of a real LOB [17]. Stock market simulators have a long history, from the Santa Fe artificial stock exchange [3] to recent multi-agent exchange environments [4]. Generative models attempt to learn regularities embedded in market event streams or the LOB directly.…”

Section: Generating Synthetic Lob Datamentioning

confidence: 99%

The Limit Order Book Recreation Model (LOBRM): An Extended Analysis

Shi¹,

Cartlidge²

2021

Preprint

View full text Add to dashboard Cite

The limit order book (LOB) depicts the fine-grained demand and supply relationship for financial assets and is widely used in market microstructure studies. Nevertheless, the availability and high cost of LOB data restrict its wider application. The LOB recreation model (LOBRM) was recently proposed to bridge this gap by synthesizing the LOB from trades and quotes (TAQ) data. However, in the original LO-BRM study, there were two limitations: (1) experiments were conducted on a relatively small dataset containing only one day of LOB data; and(2) the training and testing were performed in a non-chronological fashion, which essentially re-frames the task as interpolation and potentially introduces lookahead bias. In this study, we extend the research on LO-BRM and further validate its use in real-world application scenarios. We first advance the workflow of LOBRM by (1) adding a time-weighted z-score standardization for the LOB and (2) substituting the ordinary differential equation kernel with an exponential decay kernel to lower computation complexity. Experiments are conducted on the extended LOBSTER dataset in a chronological fashion, as it would be used in a real-world application. We find that (1) LOBRM with decay kernel is superior to traditional non-linear models, and module ensembling is effective; (2) prediction accuracy is negatively related to the volatility of order volumes resting in the LOB; (3) the proposed sparse encoding method for TAQ exhibits good generalization ability and can facilitate manifold tasks; and (4) the influence of stochastic drift on prediction accuracy can be alleviated by increasing historical samples.

show abstract