{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T00:35:59Z","timestamp":1792888559688,"version":"4.5.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":5,"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>We propose a distribution-free test of whether a systematic trading strategy generates a structural edge-one invariant across instruments, volatility regimes, and time-as opposed to regimecontingent returns, using only the trade-level R-multiple output and no access to the signal. Because a strategy with no edge can be distributionally invariant by construction, an invariance score alone is insufficient; we combine two orthogonal gates, one for the significance of the mean return and one for distributional invariance tested component by component against a Monte-Carlo null. We measure the edge on the raw directional return, which is free of any stop or target parameter, and show that judging it on a fixed reward-to-risk payoff instead would make the verdict depend on an arbitrary stop. The empirical application is built entirely on free, publicly available data: fourteen publicly specified trading strategies are reconstructed over 2008-2025, and none clears the edge gate. A proprietary FX strategy, regenerated on the same public price series and entering the test through its trade-level output alone, clears both gates (t = 3.64, N = 582). A placebo permutation, a graded sensitivity experiment and a Monte-Carlo power study calibrate the invariance gate and establish the sample size it requires. The framework is complementary to backtest-overfitting diagnostics and to factor attribution.<\/jats:p>","DOI":"10.2139\/ssrn.7525240","type":"posted-content","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T07:04:13Z","timestamp":1790665453000},"source":"Crossref","is-referenced-by-count":0,"title":["A Two-Gate Test for Structural Edge in Systematic Trading Strategies"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7185-7770","authenticated-orcid":true,"given":"Sofiane","family":"Boutgajouft","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"ref1","first-page":"1328","article-title":"Multivariate Goodness-of-Fit Tests Based on Wasserstein Distance","volume":"20","author":"D H Bailey","year":"2017","journal-title":"Journal of Computational Finance"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1093\/rfs\/hhv059","article-title":"and the Cross-Section of Expected Returns","volume":"29","author":"C R Harvey","year":"2016","journal-title":"Review of Financial Studies"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1111\/jofi.12365","article-title":"Does Academic Research Destroy Stock Return Predictability?","volume":"71","author":"R D Mclean","year":"2016","journal-title":"Journal of Finance"},{"key":"ref4","first-page":"355","article-title":"Computational Optimal Transport","volume":"104","author":"T J Moskowitz","year":"2012","journal-title":"Foundations and Trends in Machine Learning"},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-71050-9","volume":"338","author":"C Villani","year":"2009","journal-title":"Optimal Transport: Old and New. Grundlehren der mathematischen Wissenschaften"}],"container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T00:19:57Z","timestamp":1792887597000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7525240"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":5,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7525240","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}