Economic-Trend Sleeve, macro-fundamental trend (campaign): a killed candidate

Verdict: KILLED Stage: deployed gauntlet
Identity: econtrend

Trend on first-release macro vintages (payrolls, CPI, IP, credit spreads, yields, dollar) driving the 17-ETF basket by a pre-committed economic sign matrix. It has a REAL crisis-alpha personality — +60.7% through the 2008 GFC (SPY -46%), +17.5% in the 2022 bear (SPY -18%), essentially uncorrelated with the live AlphaTrend book (+0.009) and mildly SPY-negative (-0.20). But it fails the adopt bar 2 of 3: net Sharpe 0.211 (bar 0.40), DSR 0.00 at N=103, and it gets caught in fast gap-down crashes (COVID -25.9%, breaching the -22.5% floor). Genuine decorrelation, not enough edge. A hostile 3-auditor leakage panel cleared the vintage plumbing before the one-shot ran. KILLED at the gauntlet. Also tested as a combined-book DIVERSIFIER (not just standalone), since near-zero correlation can lift a book even below the solo bar: it fails there too. At equal total vol its naive Sharpe ticks up ~0.02-0.04 but within noise, it DEEPENS the GFC and 2022 drawdowns, and decisively — strip its DSR-0.00 mean and the optimal weight goes to exactly 0.00 (the whole 'benefit' was return-stacking a mean statistically indistinguishable from zero, not real diversification). Not added, in any construction. Reproduce: scripts/probe_econtrend_book.py.

Why it was worth testing

This one reached deployment before it was killed, which makes it the most expensive kind of kill and the most important to publish in full. A candidate that passed on the evidence available at the time and failed on better evidence later is not a process failure to be hidden; refusing to withdraw it would be.

The result

Measure	Value
Screen net Sharpe	0.2110

What this does and does not say

It says this configuration, on this data, net of the costs we charge, did not clear the bar it pre-registered. It does not say the underlying economic effect does not exist, that no implementation of it works, or that someone with different data or different execution would reach the same conclusion. A null is evidence about a test, not a proof about a market.

It also does not say the trial was free. Every hypothesis tested raises the deflated-Sharpe hurdle for every sleeve already in the book, including the ones that survived. That is why the kill count is published beside the survivor count rather than behind it.