At @buttonxyz, our core mission is to deeply understand the structure, behavior, and underlying mechanics of the crypto market.Crypto is not just a new asset class , it is a new market architecture, shaped by liquidity dynamics, microstructure forces, on-chain flows, funding conditions, and behavioral reflexivity that differ sharply from traditional finance.

This study on liquidity regimes  comparing BTC and SPY across macro liquidity conditions  is part of our broader effort to:

• Build data-driven tools for understanding market states
• Develop systematic frameworks for interpreting crypto price behavior
• Identify structural edges rooted in liquidity, execution, and flow dynamics
• Bridge macroeconomic liquidity research with digital asset microstructure

We believe that the next generation of crypto analytics will not come from superficial indicators, but from robust statistical methods, liquidity intelligence, and cross-asset regime modeling.

Across both crypto and equities, one invisible force consistently shapes returns, volatility, price impact, and overall market behavior: macro liquidity.

Using a PCA-based liquidity index derived entirely from non-market FRED data, we map daily liquidity conditions into four regimes : Crisis, Tight, Normal, and Abundant. When we overlay these regimes onto BTC and SPY price, return, volatility, and microstructure metrics, an unmistakable pattern emerges:

• BTC behaves like a high-beta liquidity amplifier.
• Most gains in both assets cluster in Abundant liquidity periods.
• Execution costs and volatility explode in Crisis liquidity regimes.

In short:

Liquidity is the real regime engine. Understanding it transforms how we interpret markets, risk, and opportunity.

Below is the full deep-dive.

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1. Why We Use PCA to Measure Liquidity

Liquidity is multidimensional shaped by money supply, interest rates, funding stress, credit conditions, policy spreads, reserves, and short-term volatility. These indicators interact, overlap, and often move together when liquidity tightens or loosens.

But tracking them individually creates noise.PCA solves this elegantly.

What PCA Does Intuitively

• Take 15+ correlated liquidity indicators
• Find the single strongest shared pattern
• Compress the system into one factor: PC1, the “common liquidity factor”

Instead of juggling dozens of signals, PCA gives us the underlying liquidity tide. This makes it ideal for regime analysis, macro modeling, and studying market sensitivity.

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Why PCA Makes Sense

• Reduces dimensionality
• Filters noise and measurement errors
• Captures systemic co-movement

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2. How the Liquidity Index Is Constructed

The index is built using only non-market macro data from the Federal Reserve Economic Data (FRED), ensuring that liquidity regimes are not polluted by asset price movements themselves.

Step-by-Step Method

1. Data Collection (15+ Indicators)

Examples include:

• Reserve balances
• Fed funds rate, Treasury rates
• SOFR–EFFR and EFFR–IORB spreads

These metrics capture both availability and cost of liquidity.

2. Standardization

All variables converted to z-scores so nothing dominates due to scale.

3. PCA Application

Extract PC1 → explains the majority of liquidity variation.

4. Direction Check

Ensure that high PC1 = more liquidity(low spreads, high reserves, low VIX)

5. Normalization

Convert PC1 → 0–100 index.

6. Regime Classification

Divide the full historical distribution into quartiles:

| Score  | Regime   | Meaning                       |
| ------ | -------- | ----------------------------- |
| 0–25   | Crisis   | Severe liquidity stress       |
| 25–50  | Tight    | Funding cost ↑, credit harder |
| 50–75  | Normal   | Stable liquidity              |
| 75–100 | Abundant | Easy liquidity, accommodative |

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This produces a stable, interpretable regime map used in the BTC & SPY analysis.

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3. Multi-Asset Liquidity Regime Behavior: What the Data Shows

A. Price Paths in Crisis Regime (Top Panel)

The far-right region is shaded dark red, confirming:

The current market environment is a Crisis liquidity regime.

BTC Behavior in Crisis

• Price stalls
• Trend becomes unstable
• Moves become choppier and less directional
• Macro sensitivity increases sharply

SPY Behavior in Crisis

• Slows down
• Pulls back modestly
• Does not show BTC-style chaos
• Drawdown is controlled, not explosive
• BTC is far more liquidity-levered.

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B. Returns Distribution by Regime

The combined density plot shows:

BTC

• Heavy left-tail risk in Crisis
• Highest volatility of all regimes
• Distribution widens significantly in liquidity stress

SPY

• Returns cluster tightly
• Even in Crisis, SPY avoids extreme tails
• Much more symmetric distribution

BTC = tail expansion

SPY = controlled distribution

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C. Volatility Comparison by Regime

The boxplots reflect a key insight:

BTC in Crisis

• Volatility spikes hard
• Comparable or higher than other regimes
• Remains structurally elevated even outside Crisis
• More reactive to macro liquidity shocks

SPY in Crisis

• Volatility increases modestly
• Still significantly lower than BTC
• Maintains consistency and compression

BTC = volatility explosion

SPY = volatility slope shift

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D. Price Impact (Return/Volume Ratio)

Return-per-volume ratio (price impact proxy):

• BTC shows 10–30× higher price impact than SPY
• Impact spikes even more in Crisis
• BTC’s depth evaporates faster under stress

BTC microstructure becomes fragile.

SPY microstructure becomes thicker and more stable relative to BTC.

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E. Cumulative Returns Comparison

This panel tells the complete macro story:

BTC‍

• Explodes upward during Abundant / Normal liquidity
• Completely stalls during Crisis
• High convexity to liquidity regime
• Drawdowns or flatlining dominate low-liquidity periods

SPY‍

• Slow and steady
• Crisis flattens the slope rather than reversing it
• Less regime-convex than BTC
• Behaves like a macro trend asset

BTC = liquidity convexity

SPY = liquidity linearity

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4. What the Current Crisis Regime Means Right Now

Given the data, we are in a Crisis liquidity regime today.

BTC Right Now (Crisis Regime)

• Volatility is elevated
• Price impact rises sharply
• Liquidity depth collapses faster
• Range-trading with violent wicks
• Directional trends weaken
• Macro news sensitivity is extreme

BTC becomes fragile and jumpy under liquidity stress.

SPY Right Now (Crisis Regime)

• Volatility rises modestly
• Price action flattens
• Execution remains predictable
• Drawdown mild relative to BTC
• Macro sensitivity increases but remains controlled

SPY becomes heavy, not chaotic.

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5. Key Interpretation

• BTC = high beta to global liquidity
• SPY = low beta but still liquidity-dependent
• BTC’s crisis behavior is nonlinear and abrupt
• SPY’s crisis behavior is linear and moderated
• Liquidity regimes explain more variance than price patterns alone