{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T02:10:03Z","timestamp":1749780603357,"version":"3.41.0"},"reference-count":57,"publisher":"SAGE Publications","issue":"3-4","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Algorithmic Finance"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:p> In this paper, a shorter and more publication focused version of our recent article \u201cA Bottom-Up Approach to the financial Markets\u201d ( Mahdavi-Damghani, &amp; 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. <\/jats:p>","DOI":"10.3233\/af-220356","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T14:34:55Z","timestamp":1696602895000},"page":"92-114","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Guidelines for building a realistic algorithmic trading market simulator for backtesting while incorporating market impact: Agent-Based Strategies in Neural Network Format, Ecosystem Dynamics &amp; Detection"],"prefix":"10.1177","volume":"10","author":[{"given":"Babak","family":"Mahdavi-Damghani","sequence":"first","affiliation":[{"name":"Oxford-Man Institute of Quantitative Finance, Oxford, UK"}]},{"given":"Stephen","family":"Roberts","sequence":"additional","affiliation":[{"name":"Oxford-Man Institute of Quantitative Finance, Oxford, UK"}]}],"member":"179","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"bibr1-AF-220356","first-page":"679","volume":"114","author":"Arnold A","year":"1957","journal-title":"Proceedings of the USSR Academy of Sciences"},{"key":"bibr2-AF-220356","unstructured":"Axelrod R. 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