How can the Power Law channel itself become a discipline?
The Power Law page makes a structural argument: bitcoin's price oscillates inside a bounded channel, with a conservative floor at 0.42× trend, a central trend, and an upper boundary at 3× trend that historical excursions briefly touch. Across fifteen years and nine orders of magnitude, that channel has held with R² above 0.95. Most readers encounter this as a model to believe or reject.
There is another reading. The channel can be treated not as a forecast but as a protocol. If price oscillates between bounds, then bounded triggers — sell when the historical distribution says you're in a high-percentile zone, rebuy when it says you're in a low-percentile zone — transform the model from a thing-you-believe-in into a thing-you-act-on. The channel becomes a discipline.
This isn't active trading. It isn't market timing. It isn't speculation about where price is going. It is a disciplined response to where price is, relative to a structural channel. The triggers are observable, the actions are bounded, and the framework is articulable in two sentences. Inside a retirement-account wrapper where buys and sells generate no tax events, the only cost of being wrong about a sell-and-rebuy bet is the bitcoin you fail to capture if the rebuy doesn't materialize.
The structural argument
The framework rests on three observations.
First, the channel's asymmetry is real. The floor at 0.42× trend has been a structural backstop — bitcoin has never sustained a daily close below it across multiple regulatory shocks, exchange collapses, and macro crises. The upper band at 3× trend is different in character: a brief-spike zone visited only at cycle euphoria peaks. The two bounds are not symmetric; they are not analogous; and the discipline that lives between them must respect that asymmetry.
Second, the historical distribution of price relative to trend is well-defined. Bitcoin has spent roughly half its history at or below trend, half above it. The 80th percentile of historical position corresponds to a specific multiple of trend; the 20th percentile to another. These are facts about the data, not predictions about the future. The Calculator tab's percentile sliders surface exactly these historical-frequency anchors so the user can see, in real time, how often a given trigger condition has actually existed.
Third, inside a retirement-account wrapper, the rebalancing protocol generates no tax events. Sells trigger no capital gains; rebuys reset no holding-period clocks; the only friction is the bitcoin you fail to capture if a sell-and-rebuy bet doesn't complete. Outside that wrapper, every speculative sale is a taxable event, and the framework collapses into something far less defensible — the Calculator's account-type toggle makes that cost explicit.
These three observations combine into the discipline: Sell a defined fraction of the stack when price reaches a high-percentile zone. Re-buy when it returns to a lower-percentile zone. Accept that some cycles won't trigger; accept that the discipline only works inside a wrapper that absorbs the friction.
Audience and entry point
The framework explicitly assumes one structural prerequisite: bitcoin held inside a retirement-account wrapper, or funds that could be moved into one. Self-directed IRAs, Roth conversions, solo-401(k) plans — the mechanism varies; the wrapper is what matters. The Calculator's account-type toggle exists to make this concrete: selecting Regular surfaces the tax drag that turns the discipline from defensible into questionable.
The audience explicitly includes users who don't yet hold bitcoin in a retirement account but could move funds into one. The framework itself is part of the argument for why that move might be considered. The page is not gated; anyone can read it. The retirement-account context is surfaced as a structural prerequisite, not as a credentialing check.
What ‘rebalancing’ means here
The conventional finance framing of rebalancing — periodically restoring a target allocation across uncorrelated risk factors — doesn’t describe what most Bitcoiners thinking about this strategy are actually considering. The relevant question isn’t how to rotate between bitcoin and other assets. It’s whether a once-in-a-decade peak is the moment to fund a long-deferred lifestyle purchase — a house, a vehicle, a sabbatical, kids’ education — that years of accumulation finally make possible.
With that framing, selling at the most extreme peaks isn’t aggressive. It’s the moment years of accumulation were leading toward. The ‘rebuy’ is then just deploying whatever savings remain, gradually returned to bitcoin over the following years. The discipline’s default state at any reasonable sell percentile is HODLing — sell triggers sit far above current price most of the time, and the strategy simply tells you to keep holding until they don’t.
This approach uses the Power Law to define its sell and rebuy triggers. If the Power Law isn't a model you find useful for bitcoin's price trajectory, this framework probably isn't either. The Power Law page lays out the model and its evidence — including the out-of-sample validation and the channel asymmetry — for readers who want to assess it directly before using it.
What this is not
- Not active trading. The triggers fire on percentile crossings of a defined channel, not chart patterns or sentiment shifts. Cycles complete on the order of years, not weeks.
- Not market timing speculation. The framework makes no claim about when the next trigger will fire — only about how to act if and when one does. The Calculator tab grounds the framework in fifteen years of actual price data; it does not project forward.
- Not signal-driven decision making. The triggers are observable percentile positions in a structurally-defined channel, not interpretive judgments about cycle tops, sentiment regimes, or technical levels.
What gets settled — and what doesn't
The Calculator tab walks the discipline through fifteen years of actual bitcoin price history. The user sets sell and rebuy percentile thresholds; the page identifies every historical day the strategy would have triggered and tracks the running BTC stack across the cycles that resulted. The output is a record — "starting from one bitcoin in 2010, here is what the discipline at these settings would have done across the cycles bitcoin actually went through" — not a forecast.
What it does not settle: whether to follow the framework. Whether retirement-wrapped bitcoin makes sense for your situation. Whether your tax bracket warrants the slider you've chosen. Whether the next cycle will materialize, or in what shape. The calculator computes outcomes under your assumptions. The decision is yours.
Calculator
This calculator is a historical record, not a forecast. Set your sell and rebuy thresholds; the chart below shows where they sit against fifteen years of actual bitcoin price data, and the table beneath walks through every signal that would have fired, tracking a 1.00 BTC starting position across each cycle.
The strategy’s verdict is era-dependent, not good or bad full-stop. Across the full historical record, two of three default presets destroyed bitcoin holdings — the strategy underperformed pure HODL by huge margins. Restricted to cycles since 2015, every preset beat HODL. Whether the future resembles 2010–2014’s extreme volatility or the maturing market evident since 2015 is the reader’s judgment.
Figures throughout are in USD. Reading from outside the US? Read why →
a = 1.6×10−17, b = 5.77. See The Channel for the canonical view.
The discipline assumes the Power Law channel continues to bound bitcoin's price oscillations — that historical mean-reversion to the channel persists. That's an empirical observation, not a guarantee. Two specific failure modes are worth flagging concretely:
The Power Law has held with R² above 0.95 across fifteen years and nine orders of magnitude. Past adherence does not guarantee future adherence. If price departs the channel and stays out — sustained close below the floor in a structural drawdown, or sustained close above the upper band in a regime shift — the triggers stop being meaningful.
If bitcoin sustains below the floor permanently, sell triggers may never fire and you bear the full drawdown of HODL with no offsetting cash position. If bitcoin sustains above the upper band permanently, sell triggers fire but no rebuy ever materializes — you're left holding cash while bitcoin continues climbing past your sell price, indefinitely. There is no internal signal in the strategy that can tell you which regime you're in until well after the fact.
Both triggers can execute correctly — sell at the high percentile, rebuy at the low percentile — and you can still finish a cycle holding less bitcoin than if you'd just held through. This isn't a hypothetical. It has happened multiple times in bitcoin's actual history — the table above makes this visible whenever cumulative BTC falls below 1.00 across a sell→rebuy pair.
The most striking example: at Standard 80/50 settings, the strategy sold in March 2013 at $70 (1.78× trend). The next rebuy didn't fire until January 2015, by which time bitcoin was trading at $234 (0.73× trend). Both triggers fired correctly — high sell, low rebuy — but the rebuy executed at 3.3× the prior sell price. Selling the full position at $70 and rebuying it all back at $234 leaves you with 0.30 BTC vs. 1.0 BTC for HODL through that span — the discipline kept 30% of the stack while HODL kept 100%, even though both triggers fired exactly as designed.
The mechanism: triggers are defined relative to the trend, but rebalancing happens in absolute dollars. When the underlying trend grows fast between cycles — which it did during bitcoin's early years — the percentile-relative low can sit higher in absolute dollars than the percentile-relative high of the previous cycle. The strategy still ‘worked’ in trend-relative terms; it just underperformed HODL in absolute BTC count.
Recent cycles have been less punishing because trend growth has slowed (each successive doubling takes longer in calendar time, by construction of the Power Law) and because cycle amplitudes have compressed. But the failure mode is structural, not historical-only.
The Math
The Calculator tab walks the discipline through fifteen years of actual bitcoin price history. This page walks through how percentiles are defined, how the trigger state machine works, how cumulative BTC accumulates across cycles, how taxes drag on the result, and what we're not modeling. The goal is not to convince you the discipline is right for you — it's to make the calculator's reasoning auditable.
1. Percentile, defined
The single most consequential math decision on this page is what "80th percentile" means. We use historical-frequency percentile, matching the convention already in use on the Power Law page's status badge.
For each day in PL_DATA (genesis through present, ~5,500+ samples), we compute that day's price/trend ratio — the closing price divided by the Power Law trend value at that day. We sort all the resulting ratios. The Pth percentile is the ratio level such that historical ratios have been at or below that level P% of the time.
Computed against current PL_DATA, the canonical thresholds are:
| Percentile | Approx. ratio | Interpretation |
|---|---|---|
| 5th | 0.46× trend | Floor zone — rare deep drawdowns |
| 20th | 0.55× trend | Below-trend buying territory Aggressive rebuy |
| 40th | 0.72× trend | Below trend, common Conservative rebuy |
| 50th | 0.87× trend | Historical median Standard rebuy |
| 70th | 1.34× trend | Modestly above trend Conservative sell |
| 80th | 1.78× trend | Clearly above trend Standard sell |
| 90th | 2.83× trend | Upper-band approach Aggressive sell |
| 95th | 3.81× trend | Historical spike zone |
Note the asymmetry: the historical median (50th percentile) sits at 0.87× trend, not 1.0×. Bitcoin has spent slightly more than half its time at-or-below trend. This is consistent with the band-asymmetry posture of the channel itself — the upper region reflects historical spikes rather than a plateau anchor.
2. Trigger detection
The discipline has two states:
- Holding-stack (initial). Watching for the sell trigger.
- Holding-cash-and-stack (post-sell). Watching for the rebuy trigger.
A sell trigger fires when, in the holding-stack state, the price/trend ratio crosses upward through your sell-percentile threshold — the ratio at day D-1 was below the threshold, and at day D it is at or above. On firing, the calculator sells the entire BTC position at that day's price and transitions to holding-cash-and-stack.
A rebuy trigger fires symmetrically: the ratio crosses downward through your rebuy-percentile threshold while in holding-cash-and-stack. On firing, all cash is deployed at that day's price.
Three edge cases worth surfacing:
- State-locking. A sell trigger does not re-fire while the discipline is in holding-cash-and-stack, even if the ratio re-crosses upward. The user must complete a rebuy first. This prevents oscillation around either threshold from generating spurious trades.
- Record ends mid-cycle. If the historical record ends while the discipline is in holding-cash-and-stack (a sell fired but no rebuy ever did), the Today row in the table reflects that — cumulative position is the BTC held plus the cash held since the last sell. The "waiting to rebuy" state stays open until a rebuy trigger materializes.
- Gap-overs. If a single day's price move spans both thresholds (rare in
PL_DATAbut possible), both triggers fire on the same day — a one-day cycle. The convention is to honor both rather than skip either.
3. Cycle accumulation — worked example
Each completed sell→rebuy cycle changes your BTC count by exactly the ratio of the two prices. With the entire position sold at each trigger and rebought at the next, cumulative BTC compounds multiplicatively:
Per-cycle BTC multiplier:
new_stack = old_stack × (P_sell / P_rebuy)
where P_sell is the price at which sell fired and P_rebuy is the price at which the next rebuy fired.
The discipline grows BTC if and only if P_sell > P_rebuy. In ratio-relative terms, this is always true (sell ratio > rebuy ratio by construction). In absolute-dollar terms, it is only sometimes true — this is the structural failure mode documented in How this strategy can fail on the Calculator tab. When trend grows fast between cycles, a low-percentile rebuy can land above a high-percentile sell from the previous cycle.
For the default settings (Standard preset: 80th sell, 50th rebuy):
- Sell fires near 1.78× trend
- Rebuy fires near 0.87× trend
- Per-cycle multiplier (simplified, assuming trend doesn't change during the cycle):
1.78 / 0.87 = 2.05×
If a 1.0 BTC starting position runs through 4 complete cycles where the trend doesn't grow at all, BTC count compounds: 1.0 → 2.05 → 4.20 → 8.61 → 17.6. The actual historical record produces dramatically smaller multipliers — sometimes negative ones — for the reason described next.
In practice, the Power Law trend itself grows during the time between sell and rebuy (typically 3–4 years). That growth reduces the per-cycle BTC gain, because rebuying at the same percentile means rebuying at a higher absolute price than the sell-time trend would have implied. The day-by-day backtest handles this exactly — the table on the Calculator tab shows what happens at each historical trigger, including the cycles where the discipline ended with fewer BTC than HODL because the rebuy fired above the prior sell price (visible whenever cumulative BTC falls below 1.00). Realistic per-cycle multipliers in the historical record are typically 0.95–1.30×, depending on threshold settings and which cycle you look at.
4. Tax drag — Regular account mode
In Retirement mode, every transaction is zero-tax. There's nothing to compute.
In Regular mode, every sell event is a taxable event. The calculator applies your tax rate to the capital gain portion of each sell:
per_sell_tax = (P_sell − cost_basis) × shares_sold × tax_rate
Cash available to rebuy = (BTC_sold × P_sell) − per_sell_tax
Reduced cash buys fewer BTC at the rebuy, which compounds the tax drag across cycles. After each rebuy, the cost basis is recomputed as a weighted average of the held BTC's basis and the newly-bought BTC's basis.
Across the historical record at the default 15% tax rate and Standard preset, cumulative tax drag is visible directly in the Calculator tab's table — comparing the cumulative BTC column under Retirement vs. Regular at the same percentile settings shows the drag as a divergence in the running stack. At higher rates (30–40% — some federal+state combinations or international jurisdictions), the drag exceeds the discipline's edge over HODL in many configurations, which is precisely the structural argument for executing the discipline inside a tax-deferred wrapper.
5. The historical record is not a forecast
The Calculator tab shows what the discipline would have done across actual bitcoin price history at the user's chosen settings. That is a fact about the past. It is not a prediction about the future. Three concrete ways the future will differ from the historical record:
- Volatility compression. Bitcoin’s cycle amplitudes have decreased over time. Cycle 1 peaked near 8× trend in 2011; recent cycles peak closer to 2× trend. The triggers that fired in 2011 or 2017 against extreme price excursions will likely fire less aggressively in future cycles, even at the same percentile thresholds. The Power Law Theory tab documents this compression in detail.
- Regime change is unmodeled. If the Power Law channel’s shape shifts — if the slope changes, if mean reversion fails, if bitcoin breaks free of the channel and stays out — the percentile thresholds become unreliable. The historical record does not exhibit this; future records might.
- The strategy can fail without doing anything wrong. The historical record at certain settings shows cycles where both triggers fired correctly (high sell, low rebuy) and yet ended with cumulative BTC below 1.00 because the rebuy executed above the prior sell price. Detailed in the How this strategy can fail section on the Calculator tab.
When the user’s settings produce zero triggers across the historical record, the Calculator surfaces this with an empty-state note rather than a number. The discipline reduces to HODL across that span. The fact that the Standard preset (80/50) captured multiple cycles historically, while Aggressive (90/20) captured fewer or sometimes zero, is information about the framework — not a bug.
6. Historical channel positions
The chart below shows bitcoin's price/trend ratio across all of PL_DATA. Horizontal reference lines mark the canonical percentile thresholds. You can see where each excursion sat, how long it lasted, and how often each percentile boundary was crossed.
Above-80th excursions occurred on roughly 20% of historical days, with average excursion duration around 88 days. Above-90th excursions occurred on roughly 10% of days, with shorter average duration. These are facts about the historical record, not predictions about the future shape of cycles.
7. What we're not modeling
The calculator focuses tightly on the cycle protocol. The following are deliberately out of scope:
- Real-return-on-cash. Cash held between cycles earns 0% real return (i.e., tracks inflation in nominal terms). A money-market or treasury proxy would shift outcomes modestly.
- Tax-loss harvesting. In Regular mode, capital losses on un-cycled positions could offset cycle gains. The current backtest doesn't model this; gains are taxed in isolation.
- State-level tax variations. The tax-rate slider is a single percentage. California's 13.3% state cap-gains rate stacks on federal; a state-aware field is a natural extension we haven't built yet.
- Retirement-account contribution and withdrawal rules. Annual contribution limits, RMDs, Roth conversion mechanics, Backdoor / Mega-Backdoor pathways — none of this is modeled. The page is a cycle protocol calculator, not a retirement-account tax-strategy advisor.
- Multi-account splits. A user with bitcoin partly in a retirement account and partly in a regular account would benefit from running the discipline only on the retirement portion. This is a future page, not a feature of this one.
- Future-cycle uncertainty. The historical record is one realization. A Monte Carlo treatment with stochastic volatility would surface a distribution of possible outcomes rather than a single line through the past — but it would also hide the fact that the past is the actual evidence. We treat the historical record as the primary thing worth showing, and the volatility-compression note above as the honest acknowledgment that future cycles will look different.
- Behavioral cost. The discipline requires action at exactly the moments most users find hardest — selling into euphoria, rebuying into fear. The calculator assumes perfect execution of the protocol.
Methodology disclosure
- Power Law coefficients used:
a = 1.6 × 10−¹⁷,b = 5.77(canonical Porkopolis values — see The Channel for attribution) PL_DATAwindow: genesis (Jan 3, 2009) through present- Percentile distribution computed from all daily samples in
PL_DATA - Historical backtest: day-by-day pass through
PL_DATA, tracking BTC + cash + cost basis from a 1.0 BTC starting position - Cash returns: 0% real (inflation-tracking nominal)
- Tax computation: gain × rate at each Regular-mode sell event, weighted-average cost basis maintained