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On-chain analytics and market psychology

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On-chain analytics frameworks for Bitcoin, derived from the public ledger. The area divides into three metric clusters plus a synthesis layer. Valuation and cost-basis metrics (Realized price, MVRV ratio, NUPL, SOPR) address whether Bitcoin is over- or under-valued by anchoring price against on-chain cost basis. Holder behavior and cohort metrics (HODL waves, Long-term vs short-term holder behavior, Coin Days Destroyed, Whale behavior) read the age, size, and movement of the UTXO set. Flow and sentiment metrics (Exchange flows, Miner flows, Sentiment indicators) track custodial-on-chain movement and off-chain sentiment proxies. The synthesis layer (Psychological phases of the market cycle, Using on-chain data for macro positioning) integrates the metrics into a cycle framework and bridges to the longer-horizon macro models. The area operates at intra-cycle timescales (hours to weeks-to-months), filling the gap below Bitcoin and global liquidity's ~10-12 week lead-lag and The Power Law model's multi-year trajectory. Contemporary anchor voices are James Check (Checkonchain, ex-Glassnode) and Ryan - On-Chain Mind.

How to use this sub-MOC

The notes here are arranged in three ways simultaneously:

  1. By cluster — valuation / behavior / flow + sentiment, reflecting what question each metric answers
  2. By suggested reading order — Realized price first (definitional anchor), then derivative valuation metrics, then behavioral cohorts, then flow and sentiment, then synthesis
  3. By function — distinguishing single-metric notes (the eleven primary metric notes) from integrative notes (the two synthesis notes)

Each metric note is structured around the metric-note template variant of the standard primary-note template: Why this matters → what the metric measures → how it’s calculated → what it tells you → empirical track record → limitations → counter-arguments → standard tail. The Power Law model note exemplifies the parent template; the on-chain notes adapt it for empirical-measurement framing rather than mechanistic-modeling framing.

The intellectual structure

On-chain analytics rests on a single foundational fact: Bitcoin’s blockchain is transparent. Every transaction, every UTXO, every coin’s last-moved price is publicly observable. This transparency means analysts can observe what market participants are actually doing in a way no other monetary asset permits. The on-chain metric tradition is the systematic exploitation of this transparency.

The area operates on two analytical premises:

Premise 1 — Cost basis is observable. The price at which each Bitcoin was last moved is recorded on-chain. Aggregated, this gives a network-level cost basis (Realized price) that anchors valuation metrics. Where traditional asset analysis estimates cost basis from imperfect tax filings, Bitcoin makes it directly visible.

Premise 2 — Holder behavior leaves signatures. Movement timing, holding duration, transaction size, exchange custody, miner spending patterns — each leaves observable traces. Aggregated across millions of UTXOs, these signatures form cohort behaviors that mark cycle phases.

The metrics in this area are operationalizations of these two premises. Cluster 1 metrics use cost basis to answer valuation questions; Cluster 2 metrics use behavioral signatures to answer “what are holders doing?”; Cluster 3 metrics extend the framework to cross-boundary flows and off-chain sentiment proxies. The synthesis notes integrate the metric layer with the cycle-phase framework Check has developed and with the longer-horizon macro frameworks (Bitcoin and global liquidity, Bitcoin and the ISM PMI cycle) the price-models area provides.

This area is empirical-behavioral in voice, distinct from the theoretical-argumentative voice of Economics and Culture-philosophy, the trajectory-modeling voice of the price-models area, and the operational voice of Self-custody. Each note presents a metric, what it measures, what it does and does not signal, empirical track record across cycles, and the steelmanned limitations and counter-arguments.

Valuation and cost-basis metrics

These metrics anchor current price against on-chain cost basis. They answer the question: “is Bitcoin expensive or cheap relative to where holders bought it?”

  • Realized price — total realized cap ÷ circulating supply. The aggregate on-chain cost basis; the definitional anchor for the cluster. Historically acts as a structural support level during deep corrections.
  • MVRV ratio — market cap ÷ realized cap (equivalently, price ÷ realized price). The flagship cycle-positioning indicator. High MVRV marks overheating; low MVRV marks capitulation. The MVRV Z-score normalizes across cycles.
  • NUPL — Net Unrealized Profit/Loss: (market cap − realized cap) ÷ market cap. A close mathematical cousin of MVRV expressed as a fraction. Calibrated against the psychological-phase framework (denial → hope → optimism → belief → euphoria → greed; and downside mirrors).
  • SOPR — Spent Output Profit Ratio: realized price of spent outputs ÷ acquired price. The realized-side analog to MVRV’s unrealized framing. SOPR > 1 means coins are being spent at profit; SOPR < 1 means at loss. The aSOPR (adjusted, excluding intraday churn) and LTH/STH-SOPR cohort variants are the most operationally useful refinements.

The four notes form a connected set. Realized price is the foundation; MVRV and NUPL are unrealized-side valuation metrics built on it; SOPR is the realized-side analog. Reading the cluster gives a coherent picture of where holders’ aggregate cost basis sits relative to current price and how they are behaving with respect to that cost basis.

Holder behavior and cohort metrics

These metrics read the age, size, and movement of the UTXO set to characterize what cohorts of holders are doing.

  • HODL waves — supply-by-age distribution; the iconic stacked-area chart showing the fraction of supply that has been held for each age band. Reveals accumulation and distribution phases by cohort age.
  • Long-term vs short-term holder behavior — the 155-day cohort framework Check has been most influential in developing. LTH supply growth marks accumulation; LTH distribution marks late-cycle.
  • Coin Days Destroyed — supply-weighted velocity: the sum of coin-days reset by transactions. Old-coin movement registers as large CDD spikes; particularly informative when long-dormant supply suddenly moves.
  • Whale behavior — large-holder positioning; entity-size cohort dynamics; institutional vs retail behavioral signatures.

The four notes operate at different aggregation levels: HODL waves at the supply-by-age distribution level; LTH/STH at the binary-cohort threshold level; CDD at the velocity-weighted level; Whale behavior at the entity-size level. Together they characterize the behavioral state of the network at any given time.

Flow and sentiment metrics

These metrics extend the on-chain framework to flows across boundaries that matter (exchanges, mining-entity wallets) and to off-chain sentiment proxies.

  • Exchange flows — net Bitcoin moving onto vs off of exchange-custody wallets. Net inflow typically signals distribution intent; net outflow typically signals accumulation. Custodial-ETF flows complicate the picture from 2024 onward.
  • Miner flows — miner-wallet outflows; mining-entity behavior; the spent-by-miner cohort. Miner capitulation has been a recurring late-bear signal historically.
  • Sentiment indicators — off-chain sentiment proxies (Fear & Greed Index, funding rates, futures basis, social-sentiment scores). Strictly not on-chain but conventionally bundled with on-chain analysis as the behavioral-context layer.

The cluster is the most heterogeneous of the three. Exchange and miner flows are on-chain in the strict sense; sentiment indicators are off-chain proxies. They are grouped because they all answer “what does the broader market context look like?” rather than “what is the network state?”

Synthesis notes

These notes integrate the metric layer into operational frameworks.

Analytical voices anchoring this area

The area has two primary contemporary anchors plus several adjacent voices cited from this section.

Primary contemporary anchors

  • James Check — Australian on-chain analyst; ex-Glassnode lead of Week On-Chain; now operates Checkonchain. The most influential systematic on-chain framework developer in contemporary Bitcoin analytics. His MVRV, SOPR, realized-price, cohort, and psychological-phase frameworks underlie nearly every primary note in this section.
  • Ryan - On-Chain Mind — accessible video-format on-chain analyst; runs the On-Chain Mind YouTube channel, Substack, and onchainmind.io platform. Complementary to Check: custom indicators, visual-first presentation, growing-influence positioning.

Additional home-area voice

  • Dylan LeClair — home-area on-chain analyst working the macro-financial / on-chain-metric intersection (rose through Bitcoin Magazine; now senior advisor at corporate-treasury holder Metaplanet). A cycle-positioning and macro-context voice rather than a systematic-framework originator — narrower in framework contribution than Check and Ryan, but an on-chain-home analyst, not a merely-adjacent citation.

Adjacent voices cited from this area

  • Giovanni Santostasi — Power Law modeler; cited where on-chain cycle positioning interacts with the longer-horizon trajectory framework.
  • Stephen Perrenod — Power Law co-developer; cited for the log-periodic cycle structure on-chain metrics inform.
  • sminston_with — macro-correlation operationalizer; cited for the bridge from on-chain to global liquidity / ISM PMI.
  • Lyn Alden — macro-empirical thinker; cited for the fiscal-dominance context that interacts with on-chain extremes.
  • Plan B — engaged critically; the on-chain analytical tradition has generally moved on from S2F-as-price-model in favor of the cost-basis-and-cohort framework codified here.

Key connections to other areas

On-chain analytics sits at the intersection of several other areas. The connections are dense.

To Long-term price models and cycles

To Economics and monetary theory

To Investing and markets

  • Portfolio approaches to Bitcoin (home: investing) — practical allocation. On-chain metrics inform cycle-aware partial-profit-taking and entry-timing within a long-horizon allocation framework.
  • DCA, lump-sum, and cycle-aware allocation decisions all interact with on-chain extreme readings.

To Self-custody and sovereignty

  • The shift from exchange-custodied to self-custodied Bitcoin is one of the structural signals exchange-flow analysis captures. Self-custody adoption is an on-chain-visible cohort phenomenon.
  • ETF and institutional-custody growth from 2024 onward complicates the historic exchange-flow signal — a known limitation of the framework.

To Mining

  • The halving - Mechanism is the supply-schedule event miner-flow analysis interacts with; miner capitulation cycles are partially halving-driven.
  • Hashrate dynamics relate to the broader network-state context on-chain metrics sit within.

What this area doesn’t cover

To set expectations for what isn’t here:

  • Long-term price prediction. Multi-year trajectory modeling is the price-models area’s domain. On-chain metrics inform cycle positioning, not where Bitcoin is going on a 10-year horizon.
  • Pure technical analysis. Chart-pattern frameworks (Elliott Wave, head-and-shoulders, Wyckoff) are not treated as serious analytical frameworks in this section. The on-chain area focuses on empirical-behavioral metrics grounded in observable blockchain data.
  • Altcoin on-chain analysis. Altcoin on-chain metrics, BTC-dominance signals as trading inputs, and comparative on-chain analysis across cryptocurrencies are out of scope.
  • Specific data-provider methodology. Glassnode, Coin Metrics, and Checkonchain each have specific definitional and methodological choices that affect metric values at the margin. The notes here present the conceptual frameworks; specific data-provider implementations are referenced but not exhaustively documented.
  • Trading-frequency signals. Hourly and daily timeframes are out of scope. The shortest timescale this area engages is multi-day to weekly, with the most informative signals operating at multi-week to multi-month timescales.
  • Theoretical economic framework. The on-chain area is empirical-behavioral; theoretical foundations live in Economics. On-chain metrics are tools, not theory.

The boundary with the price-models area is the most porous; cycle-positioning work straddles both.

Open questions in this area

  • How does the ETF and custodial-institutional shift affect on-chain metrics from 2024 onward? Exchange flows, cohort definitions, and HODL-wave dynamics all shift when a large fraction of Bitcoin sits in custodial-ETF wallets. The frameworks need adaptation for the post-ETF regime.
  • Are cohort thresholds (155 days for LTH/STH) still calibrated correctly? The threshold emerged from earlier cycle dynamics; later cycles may warrant recalibration.
  • What is the appropriate epistemic weight to give on-chain extremes vs macro signals when they diverge? When MVRV says cheap and global liquidity says tight, which signal dominates? The synthesis note is the place to engage this; the answer is not obviously settled.
  • How do on-chain metrics evolve as Bitcoin’s monetization matures? The cycle-phase framework rests on patterns from the 2013, 2017, and 2021 cycles. If Diminishing returns thesis holds, future cycles will produce attenuated metric extremes, and the calibration of “overheating” and “capitulation” thresholds may shift.
  • Can on-chain metrics produce reliable cycle-top signals? Historical track record is mixed. Some cycle tops have been called well; others have produced premature signals. The honest answer is that on-chain metrics provide probability-weighted information, not certainty.
  • What are the structural limits of on-chain analysis? Custodial-exchange and ETF holdings obscure individual-holder behavior. Self-custody growth changes what’s visible. The framework’s reach is bounded by what the blockchain actually shows.
  • How should the framework engage post-quantum and protocol-change scenarios? A long-horizon question, but one that would invalidate the historical-data calibration if it occurs.

Canonical sources across the area

Primary contemporary sources

  • Checkonchain platform (checkonchain.com) — James Check’s independent on-chain platform; the primary contemporary reference for systematic on-chain frameworks. See James Check.
  • Glassnode Week On-Chain archive — the historical newsletter Check led; foundational source for the 2020-2023 development of contemporary on-chain frameworks.
  • On-Chain Mind YouTube, Substack, and platform (onchainmind.io) — Ryan’s accessible video-format on-chain analysis; complementary contemporary source. See Ryan - On-Chain Mind.

Data providers

  • Glassnode — the most-cited Bitcoin on-chain data platform; comprehensive metric infrastructure
  • Coin Metrics — research-quality on-chain data; rigorous methodological documentation
  • Checkonchain — Glassnode-derived data presented through Check’s analytical framework

Adjacent academic and research literature

  • Various BitMEX Research pieces on on-chain dynamics
  • Various Coin Metrics State of the Network reports
  • Academic blockchain-analysis literature (more limited than the practitioner literature)

Adjacent canonical sources from other areas

  • Broken Money (Lyn Alden) — macro framework intersecting on-chain cycle positioning. See Broken Money - Lyn Alden.
  • The Bullish Case for Bitcoin (Vijay Boyapati) — monetization-phase framework on-chain cohort dynamics empirically operationalize.

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