Saad Naja, Nasser Odtallah, Jake Van-Praag, Sarah May · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26324 · cited by 0 works (OpenAlex) · open-access PDF
Trading-Regime-Disziplin oft fehlschlägt. Unsere letzten Daten haben getestet, ob vier KI-Handelsagenten ihre Strategien und Risikobedingungen befolgten. Die meisten Positionen wurden gegen die Live-Studie-Preise mit einer Runde-Trip-Gebühr mit 0,09% simuliert; 18% beteiligten sich an echten Hyperliquid-Tool-Bestellungen. Jede Position erklärte eine Grenze für eine Strategie, Stop und Ziel, mit einer Grenze von fünf offenen Positionen pro Agent. Die Flotte beendete die Netto-Positive-Gebühren nach dem 27. August 2026. Die Einhaltung der Regeln war hoch über alle vier Agenten, einschließlich der unrentablen Studie. Die Profitabilität wurde oft verfolgt, wie Agenten in Bezug
Machine translation (Meta M2M100); the English original follows.
Trading discipline often fails people. Our study tested whether four AI trading agents followed their strategies and risk rules. Offering traders the evidence and an agent evaluation toolkit to separate luck from following the rules, before giving agents meaningful autonomy or capital. Our study analysed 2,627 closed positions from 12 February to 27 August 2026 across the same asset universe and execution conditions. Most positions were simulated against live prices with a 0.09% round-trip fee; 18% involved real Hyperliquid orders. Each position declared a strategy, stop and target, with a limit of five open positions per agent. The fleet finished net positive after fees. Rule adherence was high across all four agents, including the unprofitable one. Profitability tracked how often agents won relative to their break-even win rates. Institutional-style controls ran by default and contained losses through a 29% market drawdown and recovery, with documented exceptions. The discipline held for all four agents. Agents kept 97.5% of gross loss within declared stops, and typical losses closed at declared levels. Declared risk remained stable after losses and broadly stable across calm and volatile months. Twenty-three of 24 runs respected the position limit; logs identify the exception's size and duration. The same live data produced different results across assigned strategies. The trade-log tests can be rerun from the published trade snapshot; regime tests also require the published Bitcoin prices and regime labels. Section 3.5 specifies the stop-breach tolerance. These trade-level results are not account-level returns or proof of a lasting edge. Our study provides an evaluation toolkit to help traders distinguish rule-following from luck before committing capital. For anyone considering allocating capital to agents to trade, discipline should be the entry test, not the only test.
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
Topics: Position (finance), Luck, Drawdown (hydrology), Profitability index, Asset (computer security), Algorithmic trading
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.26324, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange