Dip-buy exposure (retired)
The rule that turned the previous residual into a position multiplier — retired on 2026-09-07 after it was shown to subtract value at every scale.
Why it matters
It's documented here precisely because it was retired: it's the project's best example of a rule that passed its original test with a spectacular p-value and was wrong anyway. The number wasn't false — across 57 quarters the overlay beat buy-and-hold on quarterly Sharpe 64.9% of the time, at p=1.5e-06 — but Sharpe can't be averaged across windows and read as a property of the whole period: whole-period Sharpe was 0.721 against 0.840 for doing nothing. It won many quarters by a little and lost the high-variance ones. The metric is still computed because its history feeds charts and models, but it must not be read as a recommended position size.
Build it yourself
Drag the residual z and see what exposure the rule asked for — and, next to it, what the following 90 days historically returned on average in that zone.
The zone figures are measured, not illustrative: they come from re-evaluating the rule over the full 14 years of history (analysis/scripts/phase8_overlay_leverage_2026-09-07.py in the project's repo).
Illustrative example numbers for practice — not real data.
How it's calculated, step by step
- It starts from the power-law residual z (the previous indicator).
- If price is at or above trend (z ≥ 0), exposure is 1.0× — it never goes below. The rule was asymmetric by design.
- If price is below (z < 0), exposure = 1 − 0.6 × z ÷ 2, clipped to [1.0×, 1.8×]: the further below trend, the more leverage.
- That multiplier would scale the position. That's where it breaks: at maximum exposure (≥1.75×, 104 days in all of history) BTC is on average 48.7% below its high and the next 90 days return −1.3%; at neutral exposure, the next 90 days return +48.7%.
Chart
This is real Bitcoin data, computed from the blockchain — not the illustrative example above.
Data last updated on 2026-09-09.