Optimal Stop-Loss and Take-Profit Parameterization for Autonomous Trading Agent Swarm
arXiv:2604.27150v1 Announce Type: new Abstract: Autonomous crypto trading systems often spend most of their design effort on finding entries, while exits are left to fixed rules that are rarely tested in a systematic way. This paper examines whether better stop-loss and take-profit settings can improve the performance of an autonomous trading agent swarm. Using more than 900 historical trades, we replay each trade under many alternative exit policies and compare results against the existing prod
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Computer Science > Artificial Intelligence arXiv:2604.27150 (cs) [Submitted on 29 Apr 2026] Title:Optimal Stop-Loss and Take-Profit Parameterization for Autonomous Trading Agent Swarm Authors:Nathan Li, Aikins Laryea, Yigit Ihlamur View a PDF of the paper titled Optimal Stop-Loss and Take-Profit Parameterization for Autonomous Trading Agent Swarm, by Nathan Li and 2 other authors View PDF HTML (experimental) Abstract:Autonomous crypto trading systems often spend most of their design effort on finding entries, while exits are left to fixed rules that are rarely tested in a systematic way. This paper examines whether better stop-loss and take-profit settings can improve the performance of an autonomous trading agent swarm.
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