Guidelines for Building a Realistic Algorithmic Trading Market Simulator for Backtesting while Incorporating Market Impact
19 Pages Posted: 5 Aug 2019 Last revised: 9 Mar 2020
Date Written: July 30, 2019
In this paper, a shorter and more publication focused version of our recent article “A Bottom-Up Approach to the financial Markets” 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. 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.
Keywords: Generative Adversarial Networks (GANs), High Frequency Trading Ecosystem (HFTE), High Frequency Financial Funnel (HFFF), Multi-Target Tracking (MTT), Stability of Financial Systems, Markov Chain Monte Carlo (MCMC), Data Analysis and Patterns in Data, Elec- tronic Trading, Systemic Risk, HFT
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