CRYPTO / MARKET STRUCTURE
Investigating liquidity, capital flows, and market structure through on-chain data.
01 / CENTRAL RESEARCH QUESTION
“How can on-chain holder behavior, exchange capital flows, macro liquidity, and narrative rotation be synthesized into an explainable market-regime classification?”
02 — 03 / CONTEXT & APPROACH
02 / RESEARCH CONTEXT
Digital asset markets are notoriously reflexive, yet public blockchains expose granular structural data—such as realized profit and loss, cohort holding duration, and exchange net flows—that rarely exist in traditional asset classes.
Taken individually, single on-chain indicators frequently fail when market structure shifts. This project explores how combining on-chain behavior with macro liquidity and capital flow layers can classify the prevailing market regime more transparently.
03 / ANALYTICAL APPROACH
Instead of treating on-chain metrics as standalone price predictors, the pipeline organizes signals into four distinct layers: Macro Liquidity, Capital Flow, On-Chain Holder Behavior, and Narrative Strength.
Each indicator casts an explicit, auditable directional vote. The system aggregates these votes into a seven-state regime ontology while reporting source coverage and confidence.
04 / METHODOLOGY & STRUCTURAL LAYERS
Layer 01 · Macro Liquidity Context
OBSERVED SIGNALS & INPUTS
Rates trajectory, dollar liquidity proxies, cross-asset risk appetite
ANALYTICAL ROLE
Contextualizes whether crypto market activity is supported by broader macro tailwinds or fighting liquidity headwinds.
Layer 02 · Capital Flow & Exchange Pressure
OBSERVED SIGNALS & INPUTS
Exchange net flows, liquidity concentration, stablecoin and wrapper flow proxies
ANALYTICAL ROLE
Tracks immediate supply-demand imbalances on spot and derivative venues.
Layer 03 · On-Chain Cohort Behavior
OBSERVED SIGNALS & INPUTS
SOPR (7D MA), RHODL Ratio, Long-Term vs Short-Term Holder (LTH/STH) balance, Puell Multiple
ANALYTICAL ROLE
Identifies whether long-term holders are accumulating, distributing into strength, or realizing losses.
Layer 04 · Narrative & Network Activity
OBSERVED SIGNALS & INPUTS
ETH gas regime, relative asset rotation, narrative dispersion
ANALYTICAL ROLE
Measures whether risk appetite is broadening across the ecosystem or compressing into Bitcoin.
05 / SYSTEM ARCHITECTURE
A Python and Dune SQL analytical pipeline that queries blockchain and market series, evaluates transparent rule logic across four composite layers, and renders an HTML regime report with probability distributions.
DATA INPUTS
EXECUTION SEQUENCE
06 / SYSTEM OUTPUT STRUCTURE
Rather than presenting fabricated historical backtests, the specimen below documents the actual diagnostic structure generated by the system.
07 / LIMITATIONS & EPISTEMIC BOUNDARIES
Every independent research tool operates under data constraints and simplifying assumptions. Documenting those boundaries explicitly is part of the work.
Live execution of full on-chain layers depends on active Dune API credentials and query execution quotas; some inputs fall back to proxy series when offline.
Regime probability distributions are heuristic rule mappings designed for structural clarity, not statistically calibrated probability forecasts.
Rapid off-chain leverage flushes in perpetual futures markets can move prices faster than daily on-chain settlement data reflects.
08 / RELATED RESEARCH & CONTINUATION