Guidelines for building a realistic algorithmic trading market simulator for backtesting while incorporating market impact: agent-based strategies in neural network format, ecosystem dynamics and detection

Journal article

In this paper, a shorter and more publication focused version of our recent article “A Bottom-Up Approach to the financial Markets” (Mahdavi-Damghani, & Roberts, S. 2019.) is presented. More specifically we propose a new approach to studying the financial markets using the Bottom-Up approach instead of the traditional Top-Down. We achieve this shift in perspective, by re-introducing the High Frequency Trading Ecosystem (HFTE) model Mahdavi-Damghani, B. 2017. More specifically we specify an approach in which agents in Neural Network format designed to address the complexity demands of most common financial strategies interact through an Order-Book. We introduce in that context concepts such as the Path of Interaction in order to study our Ecosystem of strategies through time. We show how a Particle Filter methodology can then be used in order to track the market ecosystem through time. Finally, we take this opportunity to explore how to build a realistic market simulator which objective would be to test real market impact without incurring any research costs.

Authors: Mahdavi-Damghani, B; Roberts, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: SAGE Publications
• Journal: Algorithmic Finance
• Volume: 10
• Issue: 3-4
• Pages: 92-114
• Publication date: 2025-03-03
• Acceptance date: 2023-08-01
• DOI: 10.3233/af-220356
• EISSN: 2157-6203
• ISSN: 2158-5571
• Language: English
• Keywords: particle filter; high frequency financial funnel (HFFF); systemic risk; multi-target tracking (MTT); machine learning; high frequency trading ecosystem (HFTE); high frequency trading; Markov chain Monte Carlo (MCMC); stability of financial systems; predator prey models; game theory; sequential Monte Carlo; electronic trading; data analysis and patterns in data; generative adversarial networks (GANs)