# Crypto Cycle Models Also known as: pi-cycle top, stock-to-flow — discredited but famous. A Breadth, Sentiment & External Data concept (Crypto-native) in the LuxAlgo Library, with 3 indicator implementations. ## What are Crypto Cycle Models? Crypto cycle models are frameworks for estimating where Bitcoin, and by extension the wider crypto market, sits inside its multi-year boom and bust sequence, which has historically tracked the roughly four-year halving cadence. Instead of generating trade signals, they estimate phase: accumulation, markup, euphoria, decline, bottom. The genre includes the pi-cycle top (a cross of the 111-day simple moving average above two times the 350-day SMA that landed near several prior peaks), logarithmic regression and power-law growth curves fitted to all of price history, the famous and now widely criticized stock-to-flow scarcity model, and valuation bands such as MVRV from the [on-chain valuation suite](https://www.luxalgo.com/library/concept/on-chain-valuation-suite/). The genre grew up with Bitcoin itself. The four-year narrative took shape after the 2012 and 2016 halvings each preceded major bull markets; log-regression rainbow charts circulated from around 2014; the pseudonymous analyst PlanB published stock-to-flow in early 2019, and Philip Swift introduced the pi-cycle top the same year. Power-law treatments have been argued for by researchers such as the physicist Giovanni Santostasi. Each model's fame tracks a cycle it appeared to call, and each subsequent cycle has tested that fame. Every model in this family deserves statistical humility. Bitcoin has completed only a handful of full cycles, so any curve fitted to that history is trained on a tiny sample, and models are often refit as new data arrives, which flatters their track record. Structural change (spot ETFs, institutional flows, the shrinking supply impact of each halving) may bend or break past geometry. The honest use is context: a cycle model can say price is historically stretched or historically depressed relative to its own past, not that a top or bottom is due on a date. In practice cycle models are read alongside faster market data rather than instead of it. Derivatives conditions such as [open interest](https://www.luxalgo.com/library/concept/open-interest/), funding, and [implied volatility](https://www.luxalgo.com/library/concept/implied-volatility/) describe how positioning is being built; [exchange and stablecoin flows](https://www.luxalgo.com/library/concept/exchange-and-stablecoin-flows/) track whether coins are moving toward or away from sale; rotation gauges such as the ETH/BTC [ratio chart](https://www.luxalgo.com/library/concept/ratio-charts/) describe risk appetite within the asset class. The cycle model supplies the slow variable; these faster series fill in the rest. ## How to read a cycle model on a chart Cycle models live on long-horizon charts, and most reading errors come from viewing them at the wrong scale. 1. Use a weekly or monthly chart on a logarithmic scale with full price history; mainstream cycle models are defined in log space, and on a linear scale their bands become unreadable. 2. Overlay the model's bands or curves, whether a regression corridor, power-law support and resistance, or valuation bands, and locate current price within the band structure rather than against any single line. 3. Mark the halving dates and note time elapsed since the last one, since most models in the genre are anchored, explicitly or implicitly, to that cadence. 4. For the pi-cycle top specifically, plot the 111-day SMA against two times the 350-day SMA and watch for the upward cross, remembering it gave no signal at the November 2021 high. 5. Cross-check the implied phase against independent data before acting: stretched price with restrained on-chain and flow readings is a different situation from stretched everything. ## How traders use it - As a macro filter over faster tools: lean toward accumulation-side tactics when price sits in a model's lower bands and toward de-risking when it stretches far above the long-run curve, letting shorter-term structure handle the actual entries. - As a top-watch checklist rather than a sell trigger: extreme readings (a pi-cycle cross, price several bands above the regression corridor, stretched on-chain valuation) argue for tightening stops and scaling out, not for shorting on sight. - For dollar-cost-averaging plans: accumulation-band models give long-horizon buyers a framework for scaling purchase size with cycle depth instead of buying a fixed amount regardless of context. - As one voice in a cross-check, never alone: a cycle-model extreme gains weight when independent families, such as on-chain metrics like SOPR, funding conditions, and exchange flows, say the same thing. - For altcoin timing: Bitcoin's estimated phase is widely used as the risk backdrop for the rest of the asset class, with [relative strength](https://www.luxalgo.com/library/concept/relative-strength-comparative/) and dominance ratios deciding which assets, if any, deserve the exposure the phase allows. ## Crypto Cycle Models vs related concepts - **On-chain Valuation Suite** (https://www.luxalgo.com/library/concept/on-chain-valuation-suite/): On-chain models value the network from blockchain data (cost basis, realized value, holder behavior), while cycle models in the narrow sense work from price and time alone. Many dashboards blend both. - **Exchange & Stablecoin Flows** (https://www.luxalgo.com/library/concept/exchange-and-stablecoin-flows/): Flow metrics watch coins and stablecoin dry powder moving between wallets and venues in near real time; cycle models operate on years. Flows can say distribution is happening now; a cycle model can only say the phase where distribution would be unsurprising. - **Open Interest** (https://www.luxalgo.com/library/concept/open-interest/): Open interest describes leverage and participation in derivatives at the current moment, resetting with every washout. Cycle models ignore positioning entirely and work from price and time. The two fail differently: crowded leverage can end a rally mid-cycle, and a cycle extreme can arrive with unremarkable positioning. ## FAQ ### Do crypto cycle models still work? Unknown, and unknowable in advance. They are fitted to very few completed cycles, and each cycle has broken some earlier assumption: the 2021 double top and the 2024 pre-halving all-time high both defied patterns older models relied on. Use them as context for how stretched price is, and require independent confirmation before acting. ### What happened to the stock-to-flow model? Stock-to-flow priced Bitcoin from scarcity alone, dividing existing supply by annual new issuance. It became famous for its aggressive 2020-2021 projections, then price spent 2022 trading far below the model's trajectory. Many analysts now consider it discredited, and it survives mainly as a case study in curve fitting on a small sample. ### What is the pi-cycle top? A timing study that fires when the 111-day moving average crosses above two times the 350-day moving average, named because 350 divided by 111 approximates pi. Crosses landed within days of the 2013, 2017, and April 2021 peaks, but the November 2021 all-time high came with no signal. A close historical fit on a few events is suggestive, not a reliable rule. ### What is the Bitcoin power law model? A fit of log price against log time since Bitcoin's earliest traded prices, producing a straight-line corridor that price has so far oscillated around. Proponents argue the fit's longevity implies an underlying growth law; critics reply that a handful of oscillations around any fitted curve is weak evidence and that parameters shift as data is added. Treat it as one more definition of stretched and depressed. ### Is the four-year Bitcoin cycle still intact? Contested. The halving cadence still exists mechanically, but each halving's new supply is a shrinking fraction of coins outstanding, ETF-era flows introduced a demand driver with no cycle logic, and 2024's pre-halving all-time high broke prior sequencing. Some analysts expect dampening cycles, others outright decoupling. The defensible position is to treat four-year seasonality as a hypothesis to weigh, not a timetable. ## Implementations in the Library - Bitcoin Power Law (LuxAlgo): https://www.luxalgo.com/library/indicator/bitcoin-power-law/ - Bitcoin Expectile Model (LuxAlgo): https://www.luxalgo.com/library/indicator/bitcoin-expectile-model/ - Bitcoin Power Law Clock (LuxAlgo): https://www.luxalgo.com/library/indicator/bitcoin-power-law-clock/ ## Related concepts - Liquidation Clusters: https://www.luxalgo.com/library/concept/liquidation-clusters/ - Open Interest: https://www.luxalgo.com/library/concept/open-interest/ - On-chain Valuation Suite: https://www.luxalgo.com/library/concept/on-chain-valuation-suite/ - Exchange & Stablecoin Flows: https://www.luxalgo.com/library/concept/exchange-and-stablecoin-flows/ - Power-law Growth Curves: https://www.luxalgo.com/library/concept/power-law-growth-curves/ - Whale-wallet Tracking: https://www.luxalgo.com/library/concept/whale-wallet-tracking/ - Funding Rate: https://www.luxalgo.com/library/concept/funding-rate/ - Futures Basis: https://www.luxalgo.com/library/concept/futures-basis/ - Long/short Account Ratio: https://www.luxalgo.com/library/concept/long-short-account-ratio/ - NVT Ratio: https://www.luxalgo.com/library/concept/nvt-ratio/ --- Source: https://www.luxalgo.com/library/concept/crypto-cycle-models/ (LuxAlgo Library, the encyclopedia of trading & technical analysis). Free to use with attribution: https://www.luxalgo.com/library/license/