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Hypotheses

Cross-Asset 52-Week-High Anchoring, Long-Short ETF Basket (USEQ 1-DAY: long the 3 ETFs closest to their trailing high, short the 3 farthest, inverse-vol weighted, rebalance every N sessions)

A dollar-neutral, multi-instrument, cross-sectional long-short on 10 liquid cross-asset ETFs on USEQ daily bars, OHLCV only: SPY (primary), EFA, EEM, EWJ, EWZ (equity regions), TLT, IEF, LQD (rates/credit), GLD, SLV…

Hypothesis

A dollar-neutral, multi-instrument, cross-sectional long-short on 10 liquid cross-asset ETFs on USEQ daily bars, OHLCV only: SPY (primary), EFA, EEM, EWJ, EWZ (equity regions), TLT, IEF, LQD (rates/credit), GLD, SLV (metals). Every name has daily data in the catalog through 2026-10-01; the common overlap starts 2006-04-28 (SLV), giving about 20 years. The signal is George & Hwang (2004) 52-week-high anchoring, not trailing return: proximity_i = close_i / max(close_i over the last lookback_high sessions).……Show moreShow less

A dollar-neutral, multi-instrument, cross-sectional long-short on 10 liquid cross-asset ETFs on USEQ daily bars, OHLCV only: SPY (primary), EFA, EEM, EWJ, EWZ (equity regions), TLT, IEF, LQD (rates/credit), GLD, SLV (metals). Every name has daily data in the catalog through 2026-10-01; the common overlap starts 2006-04-28 (SLV), giving about 20 years. The signal is George & Hwang (2004) 52-week-high anchoring, not trailing return: proximity_i = close_i / max(close_i over the last lookback_high sessions). Investors anchor on the 52-week high, so they under-react to news that pushes an asset toward it and over-sell assets far below it. Proximity has been shown to subsume plain momentum and does not suffer the same momentum crashes. The cross-ASSET version applies because bond, metal and regional-equity ETFs carry the same retail anchoring as stocks. Every hold_bars sessions, counted by primary-bar index rather than a calendar date (lesson L179 is respected because the trigger is a ranking, not a date window), rank the 10 by proximity. Go long the top n_legs and short the bottom n_legs. Each leg is weighted by inverse 60-session realized volatility, normalized so that the long book = gross_exposure/2 of equity and the short book = gross_exposure/2. Only legs whose membership or weight changes by more than 25% relative are traded. This differs from the in-pipeline IndustryFactorShortLagMomentumSpyHedged, which ranks 9 equity sectors on trailing return versus SPY: here the universe crosses asset classes and the signal is distance-to-high, which is weakly correlated with return momentum across asset classes. Precompute: keep a rolling deque per instrument (max 252) and update on each bar, giving O(1) per bar and avoiding the sandbox timeouts seen on prior USEQ baskets. Rows are aligned only when all 10 ETFs have a bar with the same ts_init. Risk: book-level stop at 12% equity drawdown from the last rebalance peak, which flattens all legs until the next rebalance. Sizing uses get_account_equity() with mark-to-market legs. Fixed risk budget: gross_exposure 0.9 (0.45 long / 0.45 short; within Reg-T 2x and safe at engine leverage 1). Max single-leg notional is 25% of equity. The engine's configured leverage is left at its default and is NOT pinned in the optimization plan.

Backtest and paper results are hypothetical. Trading involves risk of loss.