The Model
Phase Model classifies market structure into regimes using a composite structural framework.
Structural Components
Macro
Broader market context derived from global money supply, interest rates, and cross-asset risk conditions. Frames the structural backdrop for regime classification across all covered assets.
Global M2 · BTC/M2 ratio · Risk correlation
Momentum
Multi-timeframe directional bias from normalized trend strength and return persistence. Focuses on structure, not short-term noise.
Multi-timeframe trend · Return persistence · Price structure
Liquidity
Measures whether capital conditions are supportive or restrictive at the asset level. Captures the liquidity side of the regime.
Capital flow direction · Asset-level liquidity conditions
Volatility
Tracks the volatility regime — whether structural price swings are expanding or compressing. Present across all asset types; captures risk appetite shifts through realized volatility and its historical percentile.
Realized volatility 30d · Volatility percentile 365d
Flow & Network
Tracks derivatives positioning, institutional capital flows, and on-chain network participation. Applies exclusively to crypto assets (BTC, ETH, SOL) — this is where each asset's structural profile diverges most.
Derivatives exposure · Funding rates · ETF flows · Exchange balances · On-chain participation
Regime Classification
Each structural component is normalized and aggregated into a composite regime score. The model evaluates structural alignment.
- expansion
- transition
- contraction
Confidence Score
The confidence score (0–100) measures internal signal agreement across all structural components — not return probability. High confidence means the components tell a consistent structural story.
Below 40 — signals diverge. Regime is detected but components disagree. Treat the output as directional, not definitive.
40 to 70 — partial alignment. Most components agree; one or two carry opposing signals.
Above 70 — strong structural coherence. All major components point to the same regime.
Supported Assets
Phase Model applies the same structural framework across all covered assets, with signals adapted to the specific characteristics of each market.
Crypto
BTC analysis incorporates on-chain signals unique to Bitcoin: hashrate trend, halving cycle position, miner behavior, and network participation.
ETH analysis captures DeFi ecosystem flow, staking dynamics, and smart contract activity — structural signals with no direct Bitcoin equivalent.
SOL analysis captures validator ecosystem dynamics, high-throughput network activity, and DeFi/NFT participation flow — signals specific to Solana's speed-first architecture.
Tradfi
S&P 500 analysis measures broad US equity structural conditions: earnings cycle, sector rotation, institutional risk appetite, and market breadth.
Gold analysis focuses on safe-haven demand, real interest rate sensitivity, inflation expectations, and central bank reserve allocation flows.
Silver analysis incorporates both industrial demand dynamics and safe-haven properties, including gold/silver ratio positioning and commodity cycle context.
Oil analysis tracks the energy market cycle, geopolitical risk premium, supply/demand balance, and oil's role as an inflation transmission signal.
NYSE composite analysis measures broad US equity market participation, breadth, and structural risk-on/risk-off conditions across the full exchange.
Shanghai Stock Exchange analysis captures the China equity cycle, domestic policy sensitivity, and emerging market risk appetite driven by global macro dynamics.
STOXX analysis covers European equity structural conditions, ECB monetary policy sensitivity, and euro area economic momentum.
B3 analysis captures the Brazilian equity cycle, commodity-driven economic dynamics, and emerging market risk appetite including currency and macro sensitivity.
Data & Methodology
Phase Model ingests data from on-chain providers, derivatives market feeds, traditional market data aggregators, and global macro sources. No single data point drives an output — every signal passes through normalization before contributing to a pillar.
Data Sources
On-chain data — block production, miner behavior, network participation, and protocol-level activity.
Derivatives market data — open interest, funding rates, and options market structure.
Global macro aggregators — money supply, interest rate environment, and cross-asset risk conditions.
Traditional market feeds — equity breadth, commodity prices, and volatility indices.
Computation Process
Each signal is normalized using a bounded transformation (Z-score + Tanh), removing magnitude differences between asset classes and mapping values to a consistent directional scale. Normalized signals are grouped by structural pillar. Pillars are aggregated into a composite regime score — Macro and Momentum carry weights that shift conditionally based on the macro risk and liquidity environment; all other pillars carry fixed weights.
The regime classification and confidence score are computed once per day in a single pipeline run: data is fetched, metrics computed, and the structural snapshot generated sequentially. There is no intraday update — the structural output is intentionally stable between daily runs to prevent noise from triggering false regime shifts.
Update Logic
Phase Model updates when structural thresholds are crossed, not on every price fluctuation.
This reduces noise and avoids regime oscillation caused by short-term volatility.
Structural orientation precedes tactical execution.