Originally published at SatoshiMacro. This is a syndicated re-post of editorial research from satoshimacro.com; the original is the authoritative version.

Giovanni Santostasi and Harold Christopher Burger built something useful in 2018. They took Bitcoin's price history, plotted it against time on a log-log axis, fit a linear regression, and got a slope of approximately 5.7. They called the result the Bitcoin Power Law.

Most of the people now selling courses around this framework do not get the nomenclature right. "Power law" in physics means something specific: a relationship governed by a universal constant tied to a physical mechanism. Newton's law of gravitation. Coulomb's law. The Stefan-Boltzmann law. Each one carries the weight of an empirical regularity that does not depend on the adoption of a technology by humans.

Bitcoin Power Law is not that. It is a statistical fit. It works because Bitcoin's adoption curve has had a roughly linear log-log relationship to time over fifteen years. If that adoption curve bends, and history says adoption curves of every previous technology bend somewhere, the fit drifts. The slope of 5.7 is descriptive, not predictive.

I have run institutional factor models long enough from a Sydney desk to know that "law" is the wrong word for any regression with fewer than three orders of magnitude of underlying physical justification. But I also have to admit something more important: the framework has caught every documented Bitcoin cycle extreme since 2013. That record is not a coincidence. This piece is about what the indicator actually does, where it sits inside the SatoshiMacro Model confluence framework, and where it stops being load-bearing.

What the Bitcoin Power Law actually measures

The model takes the natural log of price, plots it against the natural log of days-since-genesis-block, and fits a linear regression. The slope of that regression is the "power" in the name. For Bitcoin's full history through May 2026, the slope sits at approximately 5.7.

Around the regression line, the model defines bands at standard-deviation thresholds. Plus one standard deviation above the mean marks cycle-top territory. Minus 0.5 standard deviations below marks accumulation territory. Those thresholds were not derived from theory. They were anchored empirically against the 2013 and 2017 cycle inflections, then tested forward against 2021 and 2025.

The math is straightforward. The interpretation is where things get interesting.

What the +1 standard-deviation band has caught

The full record across every confirmed Bitcoin cycle top since 2013:

• December 2013: price hit approximately USD 1,100. The +1 band sat near USD 1,000 at that date. The model flagged cycle-top territory through most of November and December.
• December 2017: USD 19,500 at the peak. The +1 band held around USD 18,800 across the December window.
• April 2021: USD 64,800. The +1 band sat near USD 62,000.
• October 2025: Bitcoin printed all-time-high at USD 126,198 on 2025-10-06. The +1 band held near USD 118,000 across September and October.

That is every documented Bitcoin cycle top, caught by one regression model with one threshold. The November 2021 echo top is the documented near-miss. The metric was already inside the +1 band from April through November, so it did not produce a fresh signal for the second top. A trader using only Power Law would have held through the entire summer drawdown and the round-trip back up to USD 69,000.

What the -0.5 standard-deviation band has caught

• January 2015: USD 178. Model in accumulation zone from late 2014 through early 2015.
• December 2018: USD 3,200. Accumulation from October 2018 through February 2019.
• November 2022: USD 15,700. Accumulation from the FTX-collapse window through January 2023.

Three of three confirmed cycle bottoms caught inside the accumulation band.

Where Power Law sits inside the SatoshiMacro Model

The Power Law deviation feeds Tier 1 Valuation at 25 per cent of total model weight. Inside Tier 1 it sits alongside:

• MVRV Z-Score realised-cap normalised deviation
• Mayer Multiple 200-day moving average valuation ratio
• Pi Cycle Top and Pi Cycle Bottom momentum-divergence signals
• Bitcoin Risk Metric 0-to-1 multi-factor composite

The Tier 1 Valuation cluster is the cycle-extreme detector. Power Law contributes the log-trend-distance signal. Mayer Multiple contributes the 200-day stretch signal. MVRV contributes the on-chain valuation signal. Pi Cycle contributes the momentum-divergence signal. Risk Metric contributes the multi-factor composite. They overlap somewhat but each measures a slightly different dimension of cycle position. When three or more fire simultaneously above the cycle-top threshold, the SatoshiMacro Model gauge surfaces a confluence warning.

Confluence is the actual edge. Every documented Bitcoin cycle top since 2013 was caught only when 3 to 5 indicators across multiple tiers fired simultaneously inside a 4 to 8 week window. Power Law on its own carries one signal. The full model needs 48 of them.

Honest limitations of the Power Law

Three things to know before you trust it:

1. The slope of 5.7 is descriptive, not predictive. If Bitcoin's adoption trajectory bends, which it might as spot ETF flows reshape demand or as institutional treasury allocation grows, the slope drifts. The model assumes the past 15-year regression line continues. That is an assumption, not a theorem.

2. Standard-deviation bands compress over time. A standard deviation in 2014 with five years of price history is wider than a standard deviation in 2026 with sixteen years. The model has been getting mechanically more precise, not because Bitcoin has become more predictable, but because the denominator keeps growing. This makes calibration drift a slow but real problem.

3. The November 2021 echo top is a documented near-miss. The +1 band was breached from April 2021 through November 2021. A trader relying only on Power Law would have held the entire summer drawdown and the round-trip back to the second top. The Mayer Multiple caught November 2021 because the 200-day moving average had reset by then. Confluence catches what Power Law alone misses.

Australian-resident framing: the CGT discount ladder

The +1 / -0.5 band structure maps cleanly onto a 12-month CGT discount eligibility ladder for an Australian resident:

• Above the +1 band: distribution-eligible. Tranche disposal across the window. Prioritise lots purchased outside the previous 12 months so the 50 per cent CGT discount applies on the gain.
• Between the mean and +1 band: late-cycle territory. No tactical action required. The model is correctly telling you the cycle is late but not at the peak.
• Between the mean and -0.5 band: mid-cycle drawdown territory. Watch for accumulation-band entry.
• Below the -0.5 band: accumulation. Start tranche purchases. Begin the 12-month CGT clock on new lots.

For Australian residents thinking about EOFY 2026 tactical positioning ahead of the 1 July 2027 CGT discount cut-over in the 2026 Federal Budget, the crypto tax-loss harvesting calculator on the site walks through the Part IVA framework, cost-base method choice, and carry-forward record-keeping.

Frequently asked questions

What is the Bitcoin Power Law?

The Bitcoin Power Law is a log-log regression of Bitcoin's price against time. The slope is approximately 5.7 for the full price history through 2026. Bands at +1 standard deviation mark cycle-top territory. Bands at -0.5 standard deviations mark cycle-bottom accumulation territory.

Who created the Bitcoin Power Law?

Giovanni Santostasi and Harold Christopher Burger popularised the formulation in published research from 2018 onward. The underlying log-log regression methodology had been used by various crypto researchers since the early Bitcoin years; their contribution was formalising the framework and naming it.

Is the Bitcoin Power Law actually a "law" in the scientific sense?

No, and the naming is misleading. A scientific law is a relationship governed by a universal constant tied to a physical mechanism. The Bitcoin Power Law is a statistical fit, a regression line through historical price data. It works because Bitcoin's adoption curve has had a roughly linear log-log relationship to time. If the adoption curve bends, the fit drifts. Useful framework, wrong nomenclature.

How accurate is the Bitcoin Power Law?

The +1 band caught every documented Bitcoin cycle top since 2013 (December 2013, December 2017, April 2021, October 2025). The -0.5 band caught every documented bottom (January 2015, December 2018, November 2022). The November 2021 echo top is a documented near-miss because the metric was already inside the +1 band from April and did not refire on the second top.

Where can I see the current Bitcoin Power Law reading?

The live current reading, full historical chart back to 2013, and the cycle-comparison view that overlays current position against 2012, 2016, and 2020 trajectories are all on satoshimacro.com/tools/crypto/cycle-indicators/bitcoin-power-law/. The model is free with no paywall.

Closing

The Bitcoin Power Law is misnamed but useful. The framework has caught every documented Bitcoin cycle extreme since 2013, which is more than most indicators can claim. The honest framing is that it is a statistical fit through a 15-year price-history dataset, not a physical law. Treat the slope of 5.7 as a description of past behaviour, not a prediction of future behaviour.

The most important lesson I took from ten years on the institutional side: trust the indicator that has caught the extremes, but never confuse "this regression has held for 15 years" with "this regression will hold for the next 15 years." Adoption curves bend. Calibrations drift. Confluence catches what single indicators miss.

Live SatoshiMacro Model with current Power Law reading and all 48 underlying signals on the home gauge.

Disclosure: I built and maintain SatoshiMacro. The model is free and ad-supported (broker affiliate links on the main site, not in this post). This post is editorial, not financial advice.