Bitcoin buy timing analysis / a different approach than Fear & Greed

Everyone checks Fear & Greed daily,

but I've been testing a different approach that combines three fundamental factors into a single 0-100 score, and wanted to share both the methodology and — just as importantly — where it's statistically weak.

bitcoin market temperature

**Methodology**

Three components, combined into a Base Regime and then adjusted by a policy multiplier:

1. Macro score (V3): 63-business-day change in the yield curve (10Y-2Y, 35% weight), real rates (10Y TIPS, 20%), and DXY (45%), each smoothed over 5 days, then converted to a rolling Z-score (504-day window, min 63 days, capped at ±2.5).

2. Halving factor: linear ramp from 50→100 over the 180 days before each halving, then linear decay 100→50 over the following 540 days. Neutral (50) outside that window. Base Regime = 0.85 × Macro Score + 0.15 × Halving Factor

3. Policy score: an event ledger

if read as independent observations. Since I'm computing forward returns from every single day, adjacent days share almost the entire 90-day return window — this is a classic overlapping-window autocorrelation problem.

If I instead count only non-overlapping 90-day windows per bucket, the effective independent sample size drops to roughly: Excellent: ~10 independent windows Good: ~7 Neutral: ~14 Caution: ~5 Poor: ~3 That's nowhere near enough to compute a meaningful p-value or claim statistical significance, and I'm not going to pretend otherwise by reporting a fake confidence interval.

What I can say is the *direction* is consistent (higher score → better subsequent returns) across the full history, but with N in the single-to-low-double digits per bucket, this should be read as a descriptive backtest, not a validated predictive model. 2016-2026 was also a net bull market for BTC overall, so there's inherent regime/survivorship bias in any long-only backtest over this window.

Also worth noting: this is built for multi-month cycle positioning, not short-term timing — the macro Z-score in particular is a rate-of-change measure, so it can go flat or even reverse mid-trend once a move decelerates, even if the underlying level is still favorable.

Free to check: https://macrodashboard.net/btc-regime

Genuinely interested in pushback on the methodology, especially the overlapping-window issue — if anyone has a cleaner way to test this that doesn't just throw away 90% of the data by going non-overlapping, I'd like to hear it.