{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T19:09:57Z","timestamp":1790708997542,"version":"4.1.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.uspto.gov\/ip-policy\/copyright-policy\/copyright-basics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Algorithmic trading systems increasingly rely on predictive models to identify short-term market opportunities, yet predictive accuracy alone is often insufficient to achieve profitable trading outcomes. The ability to execute trading decisions quickly and reliably is equally important in modern electronic markets, where latency and market microstructure significantly influence execution quality. This project investigates the architectural tradeoffs between predictive signal generation and low-latency execution through the development of an event-driven trading simulation framework. The system replays historical market data, processes events sequentially using a deterministic architecture, generates trading signals from technical indicators, and simulates order execution using a queue-based matching model with execution frictions. Latency is measured throughout the processing pipeline to evaluate the impact of architectural decisions on system performance. Experimental results using historical S&amp;amp;P 500 data demonstrate average event-processing latencies of 0.0102 milliseconds and a modest positive portfolio return of 1.25% from an initial $100,000 investment. While the strategy produced relatively few trades, the findings highlight the importance of execution modeling, deterministic event processing, and system design in translating predictive signals into realized trading outcomes. The project demonstrates that successful algorithmic trading depends not only on prediction quality but also on the interaction between prediction, latency, and execution constraints within the trading infrastructure<\/jats:p>","DOI":"10.2139\/ssrn.7537838","type":"posted-content","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T18:44:55Z","timestamp":1790707495000},"source":"Crossref","is-referenced-by-count":0,"title":["Architectural Tradeoffs in Low-Latency Algorithmic Trading with Predictive Signals"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0245-9830","authenticated-orcid":true,"given":"Abhinav","family":"Randive","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T18:44:55Z","timestamp":1790707495000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7537838"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7537838","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}