Source: arXiv:1803.02974 (Zhao et al., 2018), abstract. The abstract gives a construction rule: build a portfolio that mean-reverts strongly, keeps enough spread variance to trade, and stays inside a leverage limit. It does not give a trading rule, the exact mean-reversion measure, or the constraint values. Those are filled in below and have to be checked against the full paper. None of the paper's performance claims have been verified.
The basket is 3 assets linked by economics: GDX.USEQ (gold miners, the primary instrument), GLD.USEQ and SLV.USEQ. They trade daily on the commission-free USEQ venue and have about 19-20 years of history.
Weights: every 63 sessions, use the trailing 504 daily log closes to estimate the lag-0 covariance S0 and the lag-1 autocovariance S1. Take the vector with the lowest Box-Tiao predictability, which is a 3x3 generalised eigenproblem. Scale it so the absolute weights sum to 1 (the leverage budget) and make the GDX weight positive. If the spread's daily standard deviation is below 0.6% of gross notional (the variance floor), use the next eigenvector. If none passes, stay flat. Weights stay fixed between re-estimations. The full SCA solve from the paper is a separate pre-registered variant, not part of this claim.
Trading: take the spread s = w' log P and its z-score over 60 sessions. Enter at |z| >= 2 and exit at |z| <= 0.25, after 30 sessions, at |z| >= 4, or on a re-estimation date. The strategy is flat by default, about 65-75% of sessions. Gross notional is 100% of equity. Only the 3x3 eigen solve on re-estimation bars is heavy; per-bar work is O(1).
Fee arithmetic: expected capture is about 0.8-1.2% of gross notional per trade. Costs are about 0.10-0.15% for three legs at roughly 0.03-0.05% each, so capture is more than 5x cost. Expected rate is about 7-10 trades a year, or about 140-190 over the history.