Research · Network Economics
Bitcoin Power Law Monitor
Price against a power law in time since the genesis block, fitted live in your browser, with the exponent's sensitivity, its decomposition into adoption and Metcalfe scaling, out-of-sample validation, and the conditions that would falsify it.
Current readings
Computed from the daily public snapshot for 14 October 2026, published 2026-10-15 11:22 UTC. The charts and tables below are drawn in the browser from the same file; these figures are the ones they show.
| Model-implied level | $140,207 | log10 P = a + β log10(days since genesis), from day 560 |
|---|---|---|
| Deviation from model | -0.227 dex | 26th percentile |
| Exponent | 5.600 | R² 0.96 |
| Residual dispersion | 0.301 dex | one standard deviation |
Where price sits relative to the model
Log deviation of price from the fitted model level, in dex (log10 units), placed on the fit window's own residual distribution. Cuts are the 10th, 30th, 70th and 90th percentiles.
Price and power-law model level
Deviation from model level
Fit
- Specificationlog10 P = a + β·log10 t
- OriginJan 3, 2009
- Fit window2010-08-18 to 2026-10-14 (5,902 days)
- Exponent β5.600
- Intercept a-16.201
- R²0.960
- Residual σ0.301 dex, a factor of 2.00
- Residual skewness0.81
- Residual excess kurtosis0.36
- Largest positive deviation+1.22 dex on Jun 11, 2011
- Largest negative deviation-0.74 dex on Oct 7, 2010
- Days above trend44% of window
Forward returns by starting deviation
| At the start decades from trend | 90d | 180d | 365d | Days |
|---|---|---|---|---|
| below −0.30 | +61% | +100% | +756% | 851 |
| −0.30 to −0.15 | +14% | +60% | +146% | 1,467 |
| −0.15 to 0 | +24% | +48% | +146% | 987 |
| 0 to +0.15 | -7% | -4% | +17% | 993 |
| +0.15 to +0.30 | +1% | -20% | +14% | 586 |
| above +0.30 | -22% | -25% | -51% | 1,018 |
Descriptive, overlapping windows, in-sample. Not a forecast. With the fit from 2010 the deepest rows lean on 2010 to 2012, when price multiplied many times a year; a +756% median says more about that era than about today.
Exponent sensitivity
| Origin | Fit from | β | R² | Spread σ, decades of price | Model-implied level |
|---|---|---|---|---|---|
| Genesis | day 560 | 5.60 | 0.960 | 0.301 | $140,207 |
| Genesis | 2011 | 5.48 | 0.954 | 0.294 | $131,679 |
| Genesis | 2012 | 5.56 | 0.944 | 0.281 | $136,787 |
| Genesis | 2014 | 5.33 | 0.905 | 0.264 | $123,533 |
| Whitepaper | day 560 | 5.77 | 0.959 | 0.304 | $145,673 |
| Whitepaper | 2011 | 5.63 | 0.954 | 0.294 | $135,868 |
| Whitepaper | 2012 | 5.69 | 0.943 | 0.282 | $139,404 |
| Whitepaper | 2014 | 5.43 | 0.905 | 0.264 | $124,670 |
| First price | day 560 | 4.38 | 0.945 | 0.275 | $109,556 |
| First price | 2011 | 3.90 | 0.940 | 0.337 | $82,505 |
| First price | 2012 | 4.36 | 0.946 | 0.275 | $108,648 |
| First price | 2014 | 4.45 | 0.901 | 0.269 | $111,808 |
Out-of-sample validation
| Refit at | Days in fit | β | Error next year RMSE, decades of price | Bias decades of price | Inside ±1σ | Inside ±2σ | Model-implied level today implied then |
|---|---|---|---|---|---|---|---|
| 1 Jan 2013 | 867 | 5.33 | 0.529 (122 pts) | +0.413 | 52% | 86% | $64,215 |
| 1 Jan 2014 | 1,232 | 6.44 | 0.343 (79 pts) | +0.232 | 76% | 100% | $579,876 |
| 1 Jan 2015 | 1,597 | 6.79 | 0.667 (154 pts) | -0.659 | 1% | 78% | $1,139,530 |
| 1 Jan 2016 | 1,962 | 5.93 | 0.414 (95 pts) | -0.412 | 25% | 100% | $238,389 |
| 1 Jan 2017 | 2,328 | 5.51 | 0.319 (74 pts) | +0.140 | 81% | 96% | $114,401 |
| 1 Jan 2018 | 2,693 | 5.64 | 0.319 (73 pts) | +0.244 | 72% | 100% | $141,597 |
| 1 Jan 2019 | 3,058 | 5.80 | 0.166 (38 pts) | -0.103 | 100% | 100% | $184,759 |
| 1 Jan 2020 | 3,423 | 5.74 | 0.154 (36 pts) | -0.119 | 99% | 100% | $168,250 |
| 1 Jan 2021 | 3,789 | 5.68 | 0.363 (84 pts) | +0.351 | 49% | 100% | $153,435 |
| 1 Jan 2022 | 4,154 | 5.84 | 0.263 (61 pts) | -0.164 | 73% | 100% | $193,717 |
| 1 Jan 2023 | 4,519 | 5.77 | 0.287 (66 pts) | -0.282 | 76% | 100% | $175,880 |
| 1 Jan 2024 | 4,884 | 5.67 | 0.077 (18 pts) | -0.037 | 100% | 100% | $152,691 |
| 1 Jan 2025 | 5,250 | 5.65 | 0.058 (13 pts) | +0.012 | 100% | 100% | $150,257 |
| 1 Jan 2026 | 5,615 | 5.66 | 0.268 (62 pts) | -0.260 | 72% | 100% | $150,930 |
Pooled next-year error: 0.343 dex (79 log points) over all refits; 0.213 dex (49 points) over the last five. Pooled coverage: 70% of forward days inside ±1σ (a calibrated model gives about 68%), 97% inside ±2σ (about 95%). Exponent across refits 5.33 to 6.79; model-implied level today implied by each refit $64,215 to $1,139,530.
What published research says
| Source | Published finding | This page, live | Status |
|---|---|---|---|
| Santostasi and Perrenod (2026), Nonlinear Science | β = 5.69 ± 0.05 from day 560, genesis origin, to Feb 2026 | β = 5.60 | consistent, difference explained by sample end and feed start |
| Santostasi and Perrenod (2026) | R² = 0.961, residual σ = 0.302 dex | R² = 0.960, σ = 0.301 dex | reproduced |
| Santostasi and Perrenod (2026) | β = β_A × β_M = 3.05 × 1.84 = 5.60 with non-zero-balance addresses | β_A 3.00 × β_M 1.83 = 5.50 with the same series | composition reproduced within 5% |
| Journal of Business Economics and Finance (2025) | R² = 0.9589, Jul 2010 to Jul 2025 | R² = 0.960 | consistent |
| Journal of Business Economics and Finance (2025) | Long-horizon mean reversion: lowest deviation deciles earn the highest annual returns | 365-day median return, decile 1 +1092% vs decile 10 -56% | reproduced |
| Journal of Business Economics and Finance (2025) | Short-horizon momentum: most overvalued decile has the highest weekly risk-adjusted return | 7-day median return, decile 10 -1.6% vs decile 1 +2.1% | not reproduced on medians (their result is a Sharpe ratio, not a median) |
| Wheatley, Sornette et al. (2019), Royal Society Open Science | Generalised Metcalfe exponent 1.69 on active users; LPPLS bubble diagnostics | β_M 1.83 on address stock; 2.0 to 2.1 on the companion monitor | exponent depends on the network-size proxy; 1.35 to 2.1 across proxies |
| Perrenod (2026), five new tests | DOLS price–address exponent 1.92; cointegration at weekly and monthly frequency | not computed here; companion monitor fixes 2 and validates out of sample | consistent with n² within stated intervals |
| Glassnode power law, as reported Dec 2025 and Feb 2026 | Trend about $118k on 6 Dec 2025 and $122k on 1 Feb 2026 | trend as computed at the time: $113,736 and $120,316 | within 10% |
| Peterson (2018), Alternative Investment Analyst Review | Metcalfe n² on wallet counts with Gompertz supply | companion monitor | see Metcalfe Value Monitor |
| Alabi (2017), Electronic Commerce Research and Applications | Metcalfe's law fits Bitcoin, Ethereum and Dash user growth | companion monitor | see Metcalfe Value Monitor |
| arXiv 2605.21316 (2026), "Weak Structure, Strong Forecasts" | Scale-invariance tests are passed by stacked sigmoids that later saturate; forecasts remain useful | falsifiability monitor above | acknowledged; the breakdown conditions are watched live |
| Santostasi (2014, 2018 posts); Burger (2019 corridor) | Origin of the price–time power law and of the historical model range visualisation | the historical model range bands on the main chart | not peer reviewed; attribution |
| PlanB stock-to-flow (2019) | Scarcity-in-time model; implied about $1.3M by 2025 | not a power law in time; invalid since 2021 (Glassnode) | contrast only |
"Power law" also names a different regularity in this literature: heavy, power-law tails in the distribution of Bitcoin returns (Begušić et al., 2018). That concerns the size of daily moves, not the level of price in time, and is not tested here.
Exponent stability
Decomposition
- Adoption seriesaddresses with a non-zero balance (Coin Metrics), as published
- Adoption exponent β_A (address stock ∝ t^β_A)3.00 (R² 0.974)
- Metcalfe exponent β_M (price ∝ N^β_M)1.83 (R² 0.950)
- Product β_A × β_M5.50
- Direct exponent β5.60
- Gap-1.8%
- Published (non-zero-balance addresses)3.05 × 1.84 = 5.60 vs 5.69
With the daily snapshot the adoption series is the count of addresses with a non-zero balance (Coin Metrics), the same series the paper uses; without it the page falls back to cumulative addresses used and the test is indicative only. The adoption series in use is stated in the first row.
Falsifiability monitor
FALSIFICATION STATUS: ACTIVE: all stated conditions hold. This reports only whether the model’s own published breakdown conditions have been breached; it is not a statement that the model is predictively valid. The conditions are those published with the model, not thresholds invented to produce a badge.
- Current deviation within ±2σyes (-0.23 dex vs ±0.60)
- Consecutive days beyond ±2σ now0 (window share beyond ±2σ: 4.8%)
- Expanding-window β stable over the last five yearsyes (now 5.60; five-year range 5.60 to 5.85, tolerance 0.30)
- Residual drift, last four years+0.021 dex per year (R² 0.03)
- Breakdown condition, as stated in the papersustained residual beyond −2σ, or a secular drift in residuals, or the exponent leaving its historical range
What the fit does and does not establish
The sample is four cycles, whatever the daily count. The fit above uses several thousand daily closes. A trend model’s effective sample is not that number; it is the number of independent regimes the trend has survived, because price today is almost entirely price yesterday. That is four or five, which is the same limit that leaves all but two of the thirty-six rules on the scorecard unjudgeable. “Held for thirteen years” and “five for five” are the same argument in different clothes, and the Pi Cycle page declines the second.
Calibration, read honestly. Pooled across every annual refit, 70% of forward days fell inside that year’s own ±1σ band and 97% inside ±2σ, against the 68% and 95% a calibrated model should give. That looks like vindication and is not quite. The pooled figure averages years in which the bands were far too wide with years in which the model was badly wrong: the worst refit (2015) captured 1% of the following year inside ±1σ with a bias of −0.66 dex, while four refits captured 99–100%. A model that is right on average by being too generous in calm years and badly wrong in transitions is calibrated in the aggregate and unreliable in any given year. The exponent across refits ran from 5.33 to 6.79, and the level those refits implied for today ran from about $64,215 to about $1,139,530.
The band is a factor of four at every horizon. Residual dispersion is constant in log space, so the ±1σ band is always about 4× wide and ±2σ about 16×, in 2027 exactly as in 2040. The model does not lose precision with distance; it never had more than a factor-of-four band to lose. Any single far-future number quoted from this fit — and several circulate — carries that width with it whether or not the person quoting it says so. The extended table below shows the band as a multiple for that reason.
It is one survivor from a pool nobody has counted. Stock-to-flow, rainbow bands, pure exponentials and logistic curves have all been fitted to this series. The power law is the 2013-era form that still describes it; stock-to-flow explained everything up to its 2019 publication and failed within two years. A fit that has held since 2013 is one draw from the models proposed in 2013, most of which are forgotten precisely because they failed. That pool has not been counted, so the base rate for “a curve that held this long” is unknown.
Why the fit starts at day 560. Day 560 from genesis is 17 July 2010, the first day with an exchange price. Before that there is no market and the series is either absent or a handful of over-the-counter trades; including them would fit the line to numbers that were not prices. The origin is genesis rather than that first-price day because the model’s claim is about age since the network began; the exponent-sensitivity panel above shows what each alternative origin does to the fit.
There is no mechanism. The Metcalfe page at least proposes why value should scale with users squared. Nothing on this page explains why the exponent is 5.6 rather than 4 or 8, and no published account does either. A fit with no mechanism has no particular reason to persist through a regime it has not seen. That is the strongest argument against the model, and it is why this page reports a falsification status rather than a forecast.
What it does establish. The model’s substantive claim is not a level but a deceleration: price scales with age to roughly the 5.6th power, so each doubling of bitcoin’s age yields about 5.6 doublings of price, and doublings of age take progressively longer. That predicts that later entrants get less. The portfolio page measured exactly that, independently and out of sample: a 20% bitcoin weight added 12.9 points of annual return to a portfolio started in 2015 and as little as 3.0 points to one started in 2022. It is not a clean confirmation: the uplift does not fall steadily with the entry year, and entries made after a cycle top did worse than those made a year later, so where in the cycle the money went in matters at least as much as when. A structural claim agreeing in direction with a separate measurement is worth reporting, as long as it is not mistaken for a test; two models fitted to the same price series agreeing with each other is not, and this site’s evidence-dependence panel says why.
Model-implied level, extended
| Date | Model-implied level | −1σ | +1σ | ±1σ as a multiple | ±2σ as a multiple | Range across last five refits |
|---|---|---|---|---|---|---|
| 2027-01 | $150,031 | $75,032 | $299,993 | 4.0× | 16.0× | $160,893 to $207,891 |
| 2028-01 | $203,060 | $101,553 | $406,029 | 4.0× | 16.0× | $218,413 to $285,037 |
| 2030-01 | $355,848 | $177,964 | $711,536 | 4.0× | 16.0× | $384,879 to $511,632 |
| 2032-01 | $592,124 | $296,129 | $1,183,981 | 4.0× | 16.0× | $643,659 to $870,086 |
| 2035-01 | $1,176,849 | $588,557 | $2,353,168 | 4.0× | 16.0× | $1,287,982 to $1,780,840 |
| 2040-01 | $3,151,465 | $1,576,087 | $6,301,510 | 4.0× | 16.0× | $3,482,775 to $4,974,023 |
External references
| Source | Value | As of |
|---|---|---|
| Santostasi and Perrenod (2026), exponent β | 5.69 ± 0.05 | Feb 2026 |
| Santostasi and Perrenod (2026), R² and residual σ | 0.961, 0.302 dex | Feb 2026 |
| Santostasi and Perrenod (2026), decomposition | β_A 3.05 × β_M 1.84 = 5.60 | Feb 2026 |
Data audit
| Series | First | Last | Days | Min | Max | Latest |
|---|---|---|---|---|---|---|
| Price, USD | 2010-08-18 | 2026-10-14 | 5,902 | $0.0600 | $124,777 | $83,094 |
| Cumulative addresses (fallback adoption proxy) | 2010-08-18 | 2026-10-14 | 5,902 | 106,402 | 2.40B | 2.40B |
| Addresses with a balance (Coin Metrics) | 2010-08-18 | 2026-10-14 | 5,902 | 54,907 | 57.2M | 57.2M |
- Daily calendar continuousyes
- All prices positiveyes
- Trend recomputed from a, β at last day$140,207 matches chart
- Mean residual in fit window-2.5e-14 dex (zero, as required)
- First day in feed vs published startday 592 vs day 560
- Data sourcedaily snapshot
- Live price vs last daily closeno live price
Method
The model is log10 P = a + β · log10 t, with t the number of days since the chosen origin, fitted by ordinary least squares on daily closes from the chosen start. The published specification (Santostasi and Perrenod, 2026) uses the genesis block as origin and begins at day 560; on their sample they report β = 5.69 ± 0.05, R² = 0.961 and a residual σ of 0.302 dex. The default here is the same specification on the live feed. The feed begins on day 592, so the first month of their sample is absent; that and the later end date account for a difference of a few hundredths in β, inside their stated uncertainty.
The exponent is not a constant to the second decimal. It depends on the origin, the start of the fit, the end of the fit and the price source for 2010. The sensitivity table shows the same fit under the other defensible choices; the origin matters most, and the law is stated relative to the genesis block for that reason.
The paper's contribution is mechanistic: the exponent decomposes as adoption growth N ∝ t^βA (about 3, an epidemic saturation wave on a scale-free network) composed with generalised Metcalfe scaling P ∝ N^βM (about 1.84), whose product reproduces the direct exponent. The decomposition panel repeats that test with the public address stock as the adoption proxy. The Metcalfe half of the identity is the subject of the companion Bitcoin Metcalfe Value Monitor.
Validation follows the same protocol as the Metcalfe monitor: fit through each 31 December, predict the following year, repeat, and report the error, the exponent each refit produced and the value each implied for today. Early refits are unstable and the page shows that rather than hiding it. The falsifiability monitor applies the paper's own breakdown conditions to the live data: the rolling exponent leaving its historical range, the residual staying beyond two standard deviations for an extended period, or a secular drift appearing in the residuals.
What this page tells you, and what it does not
Descriptive: price has followed a power-law relationship with time over the stated sample, with the fit and residual dispersion reported. The page also tests one-year-ahead performance out of sample and reports the resulting error. Not claimed: that time causes price, or that the model-implied level is a target. Time is a proxy for the adoption process the decomposition describes, and the extrapolation table is an extension of a fitted line under the assumption that the process continues, shown with its dispersion. A critique in the literature (arXiv 2605.21316, 2026) shows that scale-invariance tests of this kind can be passed by stacked sigmoids that eventually saturate; the falsifiability monitor exists because that objection is legitimate.
Sources
Daily price from Blockchain.com and addresses with a balance from Coin Metrics, read from the site’s daily public snapshot (btc-data), with the Blockchain.com Charts API as a fallback. The feed begins on 18 August 2010, day 592, so the fit starts there. Spot price from Coinbase, refreshed every minute while the page is open. Data is cached in this browser for six hours.
G. Santostasi and S. Perrenod, "A mechanistic derivation of the Bitcoin price power law: network adoption dynamics and generalised Metcalfe scaling," Nonlinear Science, 2026, ScienceDirect. The critique: "Bitcoin's Power Law: Weak Structure, Strong Forecasts," arXiv 2605.21316. Also: "Bitcoin valuation through power law analysis: evidence for long-term mean reversion and short-term momentum," Journal of Business Economics and Finance, 2025; Wheatley, Sornette, Huber, Reppen and Gantner, "Are Bitcoin bubbles predictable? Combining a generalized Metcalfe's law and the LPPLS model," Royal Society Open Science, 2019; Peterson, 2018, and Alabi, 2017, on Metcalfe scaling; Begušić, Kostanjčar, Stanley and Podobnik, "Scaling properties of extreme price fluctuations in Bitcoin markets," Physica A, 2018, on power-law return tails; the survey "Bitcoin price prediction: peer-reviewed evidence and social media discourse," arXiv 2606.00071, 2026. Origin of the idea: Santostasi's 2014 and 2018 posts; corridor visualisation: Burger, 2019.
Nothing here is investment advice. The model-implied level is an estimate with a stated dispersion of about 0.30 dex, meaning price has spent a third of its history more than a factor of two away from it.