Hedge Fund Statistical Aggregate Index | QuantLapse

Hedge Fund Statistical Aggregate Index (LLF)

A Quantitative Macro-Technical Fusion Model for Cross-Asset Systemic Momentum Forecasting

Overview

The Hedge Fund Statistical Aggregate Index (LLF) is an institutional-grade, multi-layered quantitative framework designed to model systemic capital flow dynamics across crypto and macroeconomic markets. It integrates high-frequency technical structure analysis with global macro-liquidity aggregates, producing a unified signal that reflects both micro-trend strength and macroeconomic liquidity expansion or contraction.

This system was engineered to replicate the capital allocation logic of multi-asset hedge fund algorithms — aggregating rate-of-change metrics, volatility-adjusted momentum scores, and liquidity proxies into a single, normalized market sentiment index.


How Its Used

• More accurate on a swing trading setup
  
• Not used for scalping purposes
  
• Used in longer term traders, from days to weeks, weeks to month, or month to years
  
• Not used for mean reversion, overbought oversold valuation
  
• Programed for high correlation assets
  
• Could be used in stock market
  
• Specifically designed for cryptocurrency
  

How to Interpret. How Signal is Produced


For this example, the based asset is BTCUSD, If the trend calculations sense or predict a negative trend or positive trend on BTCUSD, it will produce a signal on your current ticker. If the strategy is predicting BTCUSD would be going down, it will produce a sell signal on your current chart, for this example, SOLUSD . Vice Versa.

Colors: Green/Teal = High probability trending upwards
Colors: Pink/Red= High probability trending downwards
Buy/Long Signals: Buy when previous bar was trending down and current bar close is trending up.
Sell/Short Signals: Buy when previous bar was trending up and current bar close is trending down.

WARNING: IT WILL NOT EXIT YOU OUT OF A TRADE UNTIL THE BASE ASSET SIGNAL TURNS BUY OR SELL

Core Structure

The LLF architecture is composed of three synergistic domains:

1. Technical Layer (Short- to Medium-Term Price Mechanics)
   Utilizes a suite of advanced mathematical transformations and adaptive algorithms to detect structural inflection points in market behavior.
   These include:
   
   Spectral Decomposition models for cycle extraction and volatility adaptation.
   
   Adaptive trend-based oscillators that dynamically recalibrate to volatility and momentum asymmetries.
   
   Volatility-indexed moving average systems for noise suppression and lag reduction.
   
   Market median convergence engines to determine equilibrium bias and structural drift.
   The ensemble produces a multi-factor technical sentiment score reflecting short-term directional confidence.
   
   
2. Higher Timeframe Technical Layer
   A macro-synchronized extension of the primary model, integrating:
   
   Multi-timeframe momentum coherence testing.
   
   Cross-trend confirmation weighting between medium and high timeframe systems.
   This provides alignment validation — filtering false short-term volatility impulses that
   oppose dominant cyclical structure.
   
   
3. Macroeconomic Liquidity Layer (Global Monetary & Systemic Inputs)
   This layer models the behavior of global liquidity expansion, credit conditions, and systemic monetary flow. It synthesizes live economic data through TradingView’s macroeconomic database to produce a normalized liquidity index.
   Key components include:
   
   Global M2 Money Supply Composite — tracking the aggregate liquidity expansion across the
   U.S., Eurozone, Japan, China, and the U.K., adjusted for currency weightings.
   
   Global Liquidity Index (GLI) — derived from a composite of sovereign bond yields (e.g.,
   CN10Y), USD strength (DXY), high-yield spreads (BAMLH0A0HYM2), and central bank
   balance sheets (Fed, BoJ, PBoC, ECB).
   
   Net Liquidity Index — tracking the Federal Reserve’s total balance sheet net of reverse
   repos, Treasury balances, and foreign deposits.
   
   CNY Sovereign Yield × M2 Correlation Factor — serving as a global credit elasticity proxy,
   measuring the relationship between Chinese sovereign yields and domestic monetary
   supply as a leading indicator of risk-on global liquidity.
   
   These components are dynamically weighted to measure real-time macro expansion or contraction — effectively identifying inflection points where global liquidity regimes transition, impacting risk-asset performance.
   

Aggregation & Signal Architecture

The LLF model unifies its three primary domains (Technical, HigherTF Technical, and Macroeconomic) through a Bayesian-style weighted aggregation process, resulting in an Overall Statistical Signal (OSS).

The OSS computes:

Cross-domain coherence (when technical and macro conditions align).

Rate of Change (RoC) differentials within a rolling 3-bar statistical window.

Normalized z-score deviations from baseline liquidity equilibrium.

Outputs are presented as:

Rate of Change Status: Indicates acceleration or deceleration of systemic conditions.

System Value: Normalized composite strength metric (-1.00 to +1.00).

System Signal: Trade direction derived from cross-confirmation among components.

Interpretation

• Technical Index Reflects real-time momentum, volatility symmetry, and price dislocation probability.
  
• HigherTF Technical Validates multi-timeframe momentum coherence. Filters false short-term moves.
  
• Macroeconomic Index Measures global liquidity, credit expansion, and monetary flow trends. Determines macro regime bias.
  Overall Signal: Aggregates all three domains to derive a probabilistic directional bias. Hedge-fund-level risk-on/risk-off classification.
  

System Dynamics

When global liquidity expansion (rising M2, GLI, or net liquidity) coincides with positive technical alignment across timeframes, LLF transitions to a Buy/Long regime — identifying systemic risk-on phases.
Conversely, when macro contraction or liquidity withdrawal synchronizes with technical deterioration, LLF transitions to a Sell/Short regime, signaling systemic deleveraging risk.

The Rate of Change Engine continuously measures short-horizon deltas between component scores to forecast momentum fatigue, trend acceleration, or liquidity inflection reversals.

Backtesting and Metrics

Integrated with the OfficalQLBacktestingMetrics provides institutional-grade analytics, including:

Sharpe & Sortino Ratios

Profit Factor & Trade Efficiency

Maximum Drawdown and Net Profit Tracking

Omega Ratio and Equity Curve Visualization

This performance analysis framework ensures statistical robustness and confirms the model’s capability to outperform passive benchmarks under varying macro regimes.

Summary

The Hedge Fund Statistical Aggregate Index (LLF) functions as a quantitative macro-sentiment engine — bridging technical precision with macroeconomic foresight.
By fusing market microstructure analytics with global liquidity intelligence, it provides an elite framework for detecting early-phase regime shifts, enabling traders, analysts, and funds to position ahead of systemic capital rotations.