The Everything Code: Understanding the Macro-Crypto Connection

"The Everything Code" is a macro framework popularized by Raoul Pal (founder of Real Vision and Global Macro Investor) that argues all risk assets—stocks, crypto, commodities, real estate—are ultimately driven by the same force: global liquidity.

This article explores the core ideas, the evidence, and the implications for portfolio construction.

For a deeper dive into Raoul Pal's specific framework and methodology, see Raoul Pal's Liquidity Framework.

Core Thesis

The Everything Code framework rests on three pillars:

1. The Debt Refinancing Cycle

Post-2008, central banks engineered what some call a "perpetual debt jubilee":

• Governments issue debt — Running deficits to fund spending
• Central banks buy debt — Through QE, keeping rates artificially low
• Debt refinances on 3-5 year cycles — Creating predictable liquidity pulses

This 4-year cycle creates a rhythm in markets:

2. Liquidity as Master Variable

The framework argues that liquidity is a dominant driver of asset price levels (not a precise short-horizon return-forecasting signal).

In VantMacro’s own empirical tests, simple regressions on log price levels show substantial explanatory power:

• US Net Liquidity (level) vs NASDAQ (level): R² ≈ 0.684 (2003–2025)
• Global liquidity composite (level) vs NASDAQ (level): R² ≈ 0.655 (1971–2025)

However, when you move to changes (e.g., YoY), explanatory power drops materially (e.g., R² ≈ 0.157 for a YoY variant). This is a key reason we treat liquidity as context rather than a mechanical timing tool.

Key insight: Prices rise not primarily because of improving fundamentals, but because there's more money chasing the same assets.

3. Fiat Debasement

The system structurally debases fiat currency:

Implication: Any investment returning less than 11% makes you poorer in real terms. This 11% becomes the "new risk-free hurdle rate."

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The Everything Connection

The framework explains why different asset classes move together:

Correlation During Liquidity Events

During major liquidity shocks, cross‑asset correlations often rise: many risk assets move together. High‑beta assets (tech, crypto) tend to swing more than broad equities, while defensive assets can behave differently depending on the shock (growth vs inflation vs policy).

Beta to Liquidity

Different assets have different sensitivities to liquidity:

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Evidence For the Framework

M2 and Asset Prices

VantMacro’s data supports a nuanced version of the liquidity thesis:

• Liquidity measures can explain a large share of price-level variance in-sample (high R² in log-level regressions).
• The same relationship is much weaker in growth-rate / change specifications (and does not automatically translate into tradable predictability).

The 4-Year Cycle

Proponents often argue that liquidity cycles (and refinancing cycles) create a rhythm in risk assets. This is plausible as a narrative, but the strength of any “cycle” depends heavily on:

• the liquidity definition (M2 vs balance sheets vs net-liquidity adjustments),
• the lag choice,
• and the sample window.

Treat “cycle” claims as hypotheses to test, not as universal laws.

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Criticisms and Nuances

1. Correlation ≠ Causation

Both liquidity and asset prices respond to economic conditions. The correlation could be spurious—both caused by a third factor.

2. The Debasement Rate Is Approximate

11% is a estimate, not a constant. It varies by:

• Time period measured
• Currency (USD, EUR, emerging markets)
• Definition of inflation

Some argue 5-7% is more accurate historically.

3. Timing Remains Uncertain

Knowing liquidity is expanding doesn't tell you exactly when to buy or how much assets will rally. The framework provides direction, not timing.

4. Structural Change Risk

If central banks genuinely normalize policy (possible, though not currently happening), the framework may weaken.

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Practical Application

Asset Allocation by Liquidity Regime

What to Track

1. M2 YoY — The broadest money supply measure
2. Fed Balance Sheet YoY — The Fed-specific liquidity
3. Global Central Bank Liquidity — Fed + ECB + BoJ + PBoC combined
4. Dollar (DXY) — Inverse proxy for global liquidity conditions

Signals to Watch For

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The Everything Code and Bitcoin

Bitcoin occupies a special place in this framework:

Why Extreme Beta?

1. No cash flows to discount — Pure monetary asset
2. Fixed supply — No inflation of supply to dilute holders
3. Retail + institutional — Sensitive to both M2 and Fed actions
4. 24/7 liquidity — Responds immediately to macro events

The "Debasement Hedge" Thesis

Framework proponents argue Bitcoin is the optimal asset for:

• Compounding above the 11% debasement hurdle
• Benefiting from ongoing monetary expansion
• Providing asymmetric upside during liquidity expansions

Caveats

• Bitcoin has experienced 80%+ drawdowns multiple times
• Regulatory risk remains material
• The thesis is unproven over full economic cycles

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How VantMacro Uses This

VantMacro incorporates the framework while maintaining empirical rigor:

What We Track

1. Money and policy liquidity separately — M2, central bank balance sheets, and net-liquidity drains
2. Global central bank aggregate — GDP-weighted composite
3. YoY changes — Direction matters more than absolute levels

What We Add

1. Regime classification — Combining liquidity with growth and risk
2. Empirical validation — 427 regime changes backtested
3. Educational context — Explaining the "why" alongside the data

What We Don't Claim

1. 11% is the magic number — We present it as an estimate, not gospel
2. Bitcoin will outperform — We show the framework; you make decisions
3. Timing is possible — We provide context, not trading signals

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Summary

The Everything Code framework argues:

1. All risk assets float on liquidity — M2, Fed balance sheet, global central banks
2. Debt cycles create predictable pulses — ~4-year refinancing rhythm
3. Fiat is debased at ~11%/year — The hurdle rate for real wealth creation
4. High-beta assets (crypto, tech) benefit most — When liquidity expands
5. Correlations break in crises — Until central banks respond

Use it as a lens, not gospel. Combine with other frameworks and your own judgment.

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Data Sources

• Primary framework sources: public “Everything Code” commentary (notably Raoul Pal and related macro strategists) describing liquidity-driven market dynamics.
• Empirical checks referenced use public macro/liquidity series (money supply, central bank balance sheets, liquidity drains) and public asset price history.

Methodology

• Treats liquidity as a slow-moving state variable and validates claims using long samples and multiple transformations (levels vs YoY/changes).
• Uses out-of-sample sanity checks where possible and cross-references liquidity signals with growth and risk dimensions (regime framing).

Limitations

• Liquidity definitions vary widely; different composites, lags, and sample windows can flip conclusions.
• Levels-based correlations can be dominated by shared trends; high R² is not proof of causality or a trading edge.
• Framework alignment is probabilistic; it provides context, not precise return forecasts or timing signals.

Further Reading

• Raoul Pal's Liquidity Framework — The originator's detailed framework
• The Complete Guide to Global Liquidity — How to track liquidity
• Market Regimes Explained — Full regime classification

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See the Framework on VantMacro

• Real-time M2 and Fed balance sheet tracking
• Global liquidity composite
• Regime classification with liquidity dimension

Explore Liquidity Dashboard →