Our Process
Protocol Wealth Asset Framework
Built on the Entropic Macro Framework (EMF) methodology
A four-step systematic process for allocating across traditional and digital assets. Every component draws from established academic and practitioner research. We built the integration layer — the system that combines these inputs, operates them across asset classes, and adjusts allocation based on what the signals say.
Assess the Environment
Regime Classification
Monitor real-time signals to classify the current market regime. Growth, Transition, Hard Asset, or Deflation — the environment determines the playbook.
Score the Asset
8-Check Quality + 7-Layer Durability
Every asset passes through 8 binary quality checks and gets classified into 1 of 7 durability layers. No human judgment — just computation.
Size the Position
Confidence Tiers + Layer Weights
Score determines confidence tier. Tier plus current regime determines layer weight. The intersection sizes the position — higher confidence in favorable regimes gets more capital.
Review and Adjust
Feedback Loop
Document failures. Correct signal drift. Update layer mappings. The framework evolves through transparent iteration, not black-box tuning.
Everything we do answers three questions
The four steps above implement a simple logic: understand the environment, evaluate the asset, and determine if it passes our quality bar.
What kind of market are we in?
Regime Classification
We monitor a set of signals — gold versus equities, real interest rates, dollar strength, volatility, credit conditions, bond markets, energy prices — to classify the current environment. Is it rewarding growth? Punishing risk? Favoring hard assets? Contracting? The classification drives how we allocate. This isn't prediction; it's measurement.
Where this comes from
- Hamilton regime-switching model (1989)
- Dalio economic machine
- Bridgewater All Weather
Will this asset last?
Durability Classification
Not every asset decays at the same rate. A nuclear plant's competitive advantage lasts 40-60 years. A trending software company can be disrupted in 18 months. We classify assets by how long their advantage is likely to persist — from the most durable (physical infrastructure, energy production) to the most fragile (early-stage innovation, speculative growth). In any environment, we want more of the portfolio in assets that compound over time.
Where this comes from
- Engineering depreciation models
- Buffett moat framework
- Mauboussin competitive advantage period (CAP)
- OSI model + Vaclav Smil energy systems
Does this specific asset pass our quality checks?
8-Check Quality Scoring
Before anything enters the portfolio, it has to pass a set of measurable tests drawn from published academic research. Is it generating real cash? Is its financial health improving or deteriorating? Is its trend likely to continue? Does the current environment favor it? Is it vulnerable to AI disruption? These aren't opinions — they're computations we run systematically.
Where this comes from
- Piotroski F-Score (2000)
- Greenblatt / quality-investing tradition
- Hurst exponent (1951) / Mandelbrot
- Carlota Perez technology cycles (2002)
- Christensen disruption theory
Assess the Environment
The framework classifies the current environment using 6 real-time signals. Here's what they're telling us now.
The 8 Quality Checks
Each check is binary — pass or fail. No human judgment required. The final score is simply how many checks pass (0-8).
The 7-Layer Durability Stack
Assets are classified by how long their competitive advantage persists. Physical infrastructure lasts 40-60 years; software moats erode in 3-5.
How Allocations Shift by Regime
When the environment changes, layer weights adjust. Hard Asset regimes favor infrastructure (L1-L2); Deflation maximizes hedges (L7 at 55%).
Size the Position
The quality score maps to a confidence tier. The tier determines whether and how much capital the asset receives. Layer weights (from Step 2b) then scale the allocation based on the current regime.
High Confidence
6-8 / 8 checks pass
Full position size. The asset demonstrates strong financial health, persistent trends, and favorable regime alignment. Eligible for core allocation.
Moderate Confidence
4-5 / 8 checks pass
Reduced position size. The asset has merit but carries identifiable weaknesses. Watchlist candidate — monitor for improvement or deterioration.
Low Confidence
3 / 8 checks pass
Minimal or no allocation. The asset fails more checks than it passes. May be held in small size for tactical reasons, but not a core holding.
Below Threshold
0-2 / 8 checks pass
No allocation. The asset does not meet minimum quality standards. Automatic exclusion from the portfolio regardless of narrative or momentum.
How it works together: An asset scoring 7/8 (High Confidence) classified as L2 (Energy/Mining) in a Hard Asset regime gets near-maximum allocation. The same asset in a Deflation regime gets less, because L2 weight drops. The score stays the same — the environment changed.
Review and Adjust
A systematic process improves by documenting failures and correcting them. The framework is not static — it evolves through transparent iteration.
DXY Data Source Drift
Our dollar strength signal drifted 4.5 points from the authoritative FRED index. Cross-validation detected the divergence, and we corrected the signal hierarchy — resulting in a regime reclassification from Growth 65% to Transition 72%.
Layer Misclassification
ETFs were incorrectly classified as Layer 5 (Interface) when they belonged to other durability layers. We implemented explicit sector-to-layer mapping and added an UNCLASSIFIED flag for unknown assets requiring manual review.
Scoring Consolidation
Multiple scoring definitions coexisted with slightly different thresholds for the same quality checks. We consolidated into a single canonical definition with one set of thresholds and full source attribution.
Every correction is documented publicly. The process is the product — if a signal drifts, a layer is wrong, or a check threshold needs adjustment, we fix it and explain why. No black-box tuning.
One Framework, Every Account
The same four-step process applies universally. The inputs change (asset types, time horizons, constraints), but the framework logic is identical.
Treasury Management
Corporate treasuries deploying idle capital. The framework scores yield instruments through the same 8 checks, emphasizing L1-L2 durability and low-decay assets. Regime classification ensures treasury allocations adapt to macro conditions.
Individual Portfolios
Personal investment accounts with varying risk tolerance. Confidence tiers map directly to position sizing — High Confidence for core holdings, Moderate for satellite. The framework enforces discipline regardless of market narrative.
Business Accounts
Operating companies managing reserves. The same regime classification that guides individual portfolios determines whether business reserves favor growth assets or capital preservation — systematically, not by committee.
DeFi Positions
On-chain liquidity provision and protocol exposure. Digital assets run through identical quality checks. Layer classification captures protocol durability (L1 base layers vs. L6 application protocols). The framework is asset-class agnostic.
Intellectual Lineage
We built the integration layer, not the underlying ideas. Every component of PWAF has an academic or practitioner source that we credit.
| Component | Source | What PW Does With It |
|---|---|---|
| Financial Health | Piotroski F-Score (Journal of Accounting Research, 2000) | Run it programmatically as a survival filter |
| Cash Generation | Greenblatt / quality-investing tradition | Apply as a binary gate for capital allocation |
| Trend Persistence | H.E. Hurst (1951); Mandelbrot applied to markets (1960s-70s) | Compute it as a momentum confirmation signal |
| Durability / Decay | Engineering depreciation + Buffett moat framework + Mauboussin CAP | Formalize decay rates into a scoring input |
| Regime Detection | Hamilton regime-switching (1989); Dalio economic machine; Bridgewater All Weather | Specify which signals map to which regimes; maintain systematic classification |
| Technology Cycle | Carlota Perez, Technological Revolutions and Financial Capital (2002) | Classify where assets sit in the long-term technology adoption cycle |
| AI Disruption Screen | Christensen disruption theory applied to enterprise software | Screen SaaS for vulnerability: low connectivity + seat-based pricing + discretionary spend |
| Layer Classification | OSI model (networking); Vaclav Smil (energy systems) | Apply infrastructure stacking to investment classification |
Technology Adoption Through-Line
The durable investment isn't the innovation; it's the infrastructure layer underneath it. This principle has held across three technology cycles.
BBS SysOp
Built and operated a bulletin board system (Epic Illusions, Renegade software). First experience running infrastructure that other people depend on.
Ethereum node operator
Ran a full ETH node during Ethereum's early years. Observed that the protocol layer matters more than the application layer.
Protocol Wealth technology platform
Designed and operates Protocol Wealth's analytical infrastructure: 200+ systematic tools across 6 integrated systems, applying the same principle at every layer — invest in the infrastructure, not just the innovation on top of it.
What We're NOT
Not stock pickers
We classify environments and score durability. Individual names enter the portfolio because they pass systematic checks, not because someone likes the story.
Not market timers
We read conditions, not predict events. The regime classification tells us what kind of market we're in; it doesn't forecast what happens next.
Not claiming invention
Every component of the framework has a published academic or practitioner source. We built the integration layer — the system that combines these inputs and operates them across traditional and digital assets.
FAQ
Is PWAF a predictive model?
No. The framework classifies the current market environment based on measurable signals. It does not predict future market movements. The classification drives allocation decisions, but the system is descriptive, not forecasting.
How often does the regime change?
Regime transitions happen when enough signals cross their thresholds simultaneously. Historically this occurs several times per year. The system includes hysteresis to prevent whipsawing on noisy signal boundaries.
What happens when the system is wrong?
We document it and correct the process. Recent examples include the DXY data source drift and the ETF layer misclassification. Transparency about failures is part of how the system improves.
What are the confidence tiers?
Assets scoring 6-8/8 are High Confidence (full allocation). 4-5/8 is Moderate Confidence (reduced size). 3/8 is Low Confidence (minimal). 0-2/8 is Below Threshold (excluded). These tiers are applied identically across all asset classes.
Can I access the scoring tools directly?
Yes. PW Nexus exposes over 200 analytical tools via the MCP protocol, accessible through Claude.ai. Public-tier tools are available without authentication.
How does PWAF differ from EMF?
PWAF is the client-facing framework name. EMF (Entropic Macro Framework) is the underlying methodology — the academic and practitioner research that powers the scoring, regime detection, and layer classification. Same engine, clearer label.
Ready to put the framework to work?
Protocol Wealth applies PWAF as a fiduciary advisor. Schedule a consultation to discuss how systematic investing can work for your portfolio.
Protocol Wealth, LLC is an SEC-registered investment adviser (CRD #335298). Registration does not imply a particular level of skill or training. All investments involve risk, including the potential loss of principal. Digital assets are highly speculative and volatile. Past performance does not guarantee future results. The Protocol Wealth Asset Framework (PWAF), built on the Entropic Macro Framework (EMF) methodology, including the 7-layer durability model, 8-check scoring system, and related analytical methodologies, are systematic frameworks built on established research — not predictive models and not investment advice. Framework scores, tiers, and classifications reflect historical and current quantitative metrics only; they do not constitute buy, sell, or hold recommendations for any specific security. Past performance is not indicative of future results.