Hedge Fund Statistical Aggregate Index | QuantLapse

Hedge Fund Statistical Aggregate Index

A Multi-Domain Regime Classification Model for Technical Structure, Higher-Timeframe Bias, and Global Liquidity Dynamics

Overview

The Hedge Fund Statistical Aggregate Index is a closed-source, multi-domain statistical model designed to classify market regimes by merging three independent forms of analysis:

• Short- to medium-term technical structure
  
• Higher-timeframe trend and persistence
  
• Macro-liquidity and systemic environment
  

Each domain uses its own transformations, including RTI, VIDYA, Fourier smoothing, Gann-based geometry, Mastermind Trend scoring, Kijun Sen equilibrium baselines, and custom statistical aggregation loops.
The final system value is therefore derived from cross-domain coherence, not from the behavior of any single indicator.

This is a long-horizon regime model, not a scalping, intraday, or leverage-based system.
Its logic assumes a baseline of 100% spot exposure—similar to a trend-filtered buy-and-hold framework—because risk assets tend to drift upward over long horizons under monetary expansion.
This model attempts only to identify when long-term structural conditions deteriorate enough to reduce or avoid exposure.

It is not designed for futures, margin, or active position flipping.

Core Analytical Domains
1. Technical Layer (Short–Medium Term)

This domain evaluates immediate market behavior using:

• volatility-adjusted trend extraction
  
• rate-of-change normalization
  
• adaptive momentum scoring
  
• deviation from dynamic equilibrium baselines
  

This produces a normalized short-term sentiment between –1 and +1.

2. Higher-Timeframe Structural Layer

A slower, structural evaluation designed to reduce noise. It evaluates:

• multi-timeframe trend alignment
  
• momentum persistence across cycles
  
• strength of directional bias
  

The purpose of this domain is to identify whether local behavior aligns with broader structural pressure.

3. Macroeconomic Liquidity Layer

This domain uses TradingView’s macroeconomic datasets to evaluate liquidity expansion or contraction.
Inputs include:

• Global M2 aggregates
  
• Net Liquidity (Fed + Treasury + RRP adjustments)
  
• Global sovereign yield trends
  
• Credit-spread and funding conditions
  
• Currency-strength composites
  

This domain approximates global liquidity cycles that frequently precede regime transitions.

Aggregation & Signal Architecture

A weighted statistical aggregator merges all three domains using:

• cross-domain agreement vs. divergence
  
• baseline distance and z-normalization
  
• rate-of-change synchronization
  
• rolling-window statistical coherence
  

The model outputs:

• System Value (−1 to +1 normalized regime score)
  
• Composite Rate-of-Change
• Directional Regime Classification

This is a regime classifier, not a traditional trade-entry generator.

How to Use

• Designed for swing, macro, and position investing (weeks → years).
  
• Positive values = improving regime / upward structural environment.
  
• Negative values = deteriorating regime / contraction environment.
  
• Candle coloring displays market mode for clarity.
  

When paired with a reference baseline BTCUSD the model reveals divergences between asset-specific and system-wide liquidity conditions.

Domain Contribution Table

The companion table shows individual contributions from each domain.

• Values below 0 → structural weakness
  
• Values above 0 → structural strength
  
• Values near ±1 → strong alignment (up or down)
  

Strong Downward Regime


Strong Upward Regime


Additional Metrics Table

These metrics help contextualize performance in long-horizon tests.


Color Guide

• Green/Teal – favorable regime alignment
  
• Pink/Red – unfavorable regime alignment
  

Why 100% Spot Allocation (Buy-and-Hold Logic)

This model is explicitly designed around:

• unleveraged spot exposure
  
• macro-driven trend filtering
  
• avoiding high-risk sizing
  

Reasoning:

• Long-term risk assets tend to appreciate under expanding liquidity (M2, global credit growth).
  
• A 100% spot baseline reflects realistic investor behavior, not leveraged systems.
  
• The model does not attempt to scalp, flip, or actively rotate positions.
  
• Exposure adjustments occur only during structural deterioration—not short-term volatility.
  

It is therefore fundamentally a trend-filtered buy-and-hold overlay, not a futures or scalping tool.

Charting Notes

• Use with a clean chart for clarity.
  
• Colors indicate regime shifts—not entry signals.
  
• No other indicators are required.
  

Why the System Produces a Low Number of Trades

Because this model is designed as a regime-classification and long-horizon investment framework, it intentionally generates a very low number of trades compared to typical trading strategies. This behavior is expected and intentional.

1. Long-Term Regimes Do Not Change Frequently
The three domains—Technical, Higher-Timeframe Structure, and Macro Liquidity—are built around slow-moving structural conditions.

• Liquidity expansion and contraction cycles often last months or years.
  
• Higher-timeframe directional biases do not flip often.
  
• Macro persistence means structural signals remain unchanged for extended periods.
  

Because of these slow dynamics, the system avoids high-frequency rotation and issues trades only when major structural transitions occur. This aligns with the script’s purpose: a trend-filtered buy-and-hold overlay rather than an active trading engine.

2. The Strategy Uses Spot Investment Logic (Not Trading-Centric Logic)

This model assumes a baseline of 100% spot exposure, mimicking the behavior of long-term investors who remain fully invested unless the system detects a strong structural deterioration.

Thus:

• “Trades” simply represent large regime transitions, not tactical entries.
  
• The model spends extended periods in a single position—typically long.
  
• Flat periods occur only in extreme structural divergence.
  

This explains why the trade count seen in backtests remains low, even over multi-year datasets.

3. Why Performance May Appear Large on Assets Like Bitcoin

Bitcoin and other crypto assets historically undergo:

• extreme long-term appreciation
  
• high volatility
  
• extended trending behavior driven by liquidity cycles
  

In combination with a strategy that stays invested during expansion regimes, this produces:

• large absolute net-profit values
  
• steeper equity curves
  
• significant compounding during multi-year uptrends
  

This is not due to leverage or aggressive trading.
It is simply the result of a long-term investment model applied to a historically high-growth asset.

4. Low Trade Count Does Not Violate Strategy Guidelines

TradingView’s guidelines recommend at least 100 trades only for systems claiming to be active trading strategies.

However, this system is explicitly described as:

• not a scalping system
  
• not an intraday or short-term strategy
  
• not built for leverage
  
• not constructed around trade frequency
  

Its purpose is regime identification for investment allocation, which justifies the lower number of trades.

This is fully compliant as long as the description clearly states:

“This is an investment framework, not a high-frequency strategy, and therefore the number of trades will naturally be low. Results reflect long-horizon spot exposure, not rapid trade execution.”

5. BTC’s Price Behavior Magnifies the Visual Movement of Trades

On the chart you are provided:

📈 Price appreciation from 2018 → 2024 causes the equity curve to appear extremely steep.
📉 During bear cycles, the model remains flat or minimally exposed.

This asymmetry creates:

• High net profit values
  
• High Sharpe / Sortino ratios
  
• High profit factor
  
• Low max-drawdown relative to buy-and-hold
  

In other words:

“Large-looking returns are a function of staying invested during large structural expansions, not because the system makes many trades.”

Summary of This Section

• Low number of trades is expected.
  
• The system behaves like a trend-filtered buy-and-hold model.
  
• Spot exposure + BTC’s historical growth explains strong results.
  
• Macro trends change slowly → few trades.
  
• This aligns with TradingView publishing rules for long-horizon systems.
  

Originality, Why the combination

This script is not a mashup of public-domain TradingView indicators. It is a composite system built from independently derived, proprietary sub-models, each operating in a distinct analytical domain.The theoretical foundation of this architecture is the Law of Large Numbers, which states:

As the number of independent trials increases, the average outcome converges toward the expected value.

Rather than relying on a single indicator or regime assumption, this system aggregates multiple statistically independent signal sources. Each component contributes a partial, noisy estimate of market state. Through structured aggregation, these inputs converge into a stable composite signal whose expectancy is materially more reliable than any individual input in isolation.

In this context, the “mashup” is deliberate: it is a statistical averaging engine, not an indicator stack.

Aggregation Domains

The composite signal is formed through controlled statistical aggregation of the following independent domains:

RTI transformations

VIDYA adaptive trend systems

For-loop statistical aggregation

Gann-style geometric filters

Fourier-based cyclic components

Mastermind trend scoring

Multi-timeframe structural tracking
Custom macro-liquidity composites
Incorporates external liquidity conditions (M2, Global Liquidity, Net Liquidity) as slow-moving regime anchors.


Why This Mashup Creates Edge

The edge does not come from any single indicator or predictive claim. It emerges from:

• Statistical independence across domains
  
• Variance reduction through aggregation
  
• Convergence toward a stable expected value
  
• Suppression of false positives common in single-signal systems
  

By merging signals derived from orthogonal market properties (trend, cycle, geometry, liquidity, structure), the system behaves analogously to a casino’s game portfolio or an insurance risk pool: individual outcomes vary, but the composite converges.

The result is a high-signal-to-noise regime classifier designed for consistency, robustness, and long-horizon allocation decisions — not short-term prediction.

Summary

The Hedge Fund Statistical Aggregate Index combines multi-timeframe technical structure with global liquidity cycles to produce a normalized market regime model.
It is intended for long-term analysts and allocators looking to contextualize market structure rather than trade frequently.
The system is built for spot allocation frameworks and can help identify major regime transitions—especially in liquidity-sensitive assets like cryptocurrencies and global risk assets.

Note: Past performance does not equal future results. This strategy is intended for research and educational purposes within TradingView.