Scale Invariance Research: Consolidated Report

Complete End-to-End Analysis with Live Data

Generated: 2025-11-09 20:42:33


Abstract

This report presents a comprehensive scale invariance analysis across multiple assets.

Data Sources:

  • All financial data downloaded LIVE from Yahoo Finance API
  • NO hardcoded data points
  • ALL data cached locally for reproducibility
  • Total Assets Analyzed: 19


    Overall Comparison

    Consolidated Comparison

    Genesis Sensitivity Overview

    Scale invariance strength varies with the assumed genesis date. The chart below shows how R² responds across all tested dates for each asset where genesis optimization was performed.

    Genesis Sensitivity

    Summary Table

    Asset Category Exponent Pattern Points Date Range Genesis Source
    Internet Users Technology 0.9776 2.38 ⚡ Power-law 20 2005-01-01 to 2024-01-01 1983-01-01 (Provided) World Bank API (IT.NET.USER.ZS)
    Broadband Subscriptions Technology 0.9580 2.03 ⚡ Power-law 20 2005-01-01 to 2024-01-01 1990-01-01 (Provided) World Bank API (IT.NET.BBND.P2)
    McDonald's Restaurants Business 0.9301 1.10 ⚡ Power-law 130 2009-12-31 to 2025-09-30 1955-04-15 (Provided) Downloaded from SEC EDGAR (CIK: 0000063908), us
    Electricity Access Infrastructure 0.9188 1.06 ⚡ Power-law 26 1998-01-01 to 2023-01-01 1880-01-01 (Provided) World Bank API (EG.ELC.ACCS.ZS)
    Bitcoin Financial Asset 0.9079 5.74 ⚡ Power-law 5566 2010-07-18 to 2025-11-09 2009-01-03 (Provided) btcpricehistory.csv (repository data)
    Microsoft Corp. Financial Asset 0.8151 4.91 ⚡ Power-law 9993 1986-03-13 to 2025-11-07 1975-04-04 (Provided) Yahoo Finance API (yfinance Python library)
    Mobile Subscriptions Technology 0.7794 2.23 ✅ Saturated (103%) 36 1984-01-01 to 2024-01-01 1983-10-13 (Provided) World Bank API (IT.CEL.SETS.P2)
    Tesla Inc. Financial Asset 0.7543 5.05 ✅ Saturated (171%) 3866 2010-06-29 to 2025-11-07 2003-07-01 (Provided) Yahoo Finance API (yfinance Python library)
    Alphabet Inc. Financial Asset 0.7494 2.18 ⚡ Power-law 5341 2004-08-19 to 2025-11-07 1998-09-04 (Provided) Yahoo Finance API (yfinance Python library)
    DTP3 Immunization Health 0.7149 0.05 ✅ Saturated (757%) 22 2003-01-01 to 2024-01-01 2002-05-26 (Optimized vs published) WHO API (WHS4_100)
    Measles Immunization Health 0.6815 0.04 ✅ Saturated (735%) 22 2003-01-01 to 2024-01-01 2002-05-26 (Optimized vs published) WHO API (WHS4_544)
    Gold ETF (SPDR) (Early Period) Financial Asset - Split 0.5747 0.33 🔹 Early 2639 2004-11-18 to 2015-05-14 2004-11-18 (Provided) Yahoo Finance API (yfinance Python library)
    S&P 500 ETF (Early Period) Financial Asset - Split 0.5739 0.36 🔹 Early 4126 1993-01-29 to 2009-06-16 1993-01-22 (Provided) Yahoo Finance API (yfinance Python library)
    Gold ETF (SPDR) Financial Asset 0.5594 0.34 🏒 Hockey (3.6x) 5277 2004-11-18 to 2025-11-07 2004-11-18 (Provided) Yahoo Finance API (yfinance Python library)
    S&P 500 ETF (Late Period) Financial Asset - Split 0.5422 0.37 🔹 Late 4126 2009-06-17 to 2025-11-07 2009-06-17 (Hockey Stick Inflection) Yahoo Finance API (yfinance Python library)
    S&P 500 ETF Financial Asset 0.4041 0.48 🏒 Hockey (7.7x) 8252 1993-01-29 to 2025-11-07 1993-01-22 (Provided) Yahoo Finance API (yfinance Python library)
    Apple Inc. Financial Asset 0.4032 2.87 📈 S-curve (89%) 11319 1980-12-12 to 2025-11-07 1976-04-01 (Provided) Yahoo Finance API (yfinance Python library)
    Ethereum Financial Asset 0.2740 1.90 ✅ Saturated (152%) 2921 2017-11-09 to 2025-11-07 2015-07-30 (Provided) Yahoo Finance API (yfinance Python library)
    Gold ETF (SPDR) (Late Period) Financial Asset - Split 0.1743 0.12 🔹 Late 2638 2015-05-15 to 2025-11-07 2015-05-15 (Hockey Stick Inflection) Yahoo Finance API (yfinance Python library)

    Business

    McDonald's Restaurants

  • **R² (Scale Invariance):** 0.9301
  • **Power Law Exponent:** 1.10
  • **Data Points:** 130
  • **Date Range:** 2009-12-31 to 2025-09-30
  • **Log Residual Std Dev:** 0.0117
  • **Data Source:** Downloaded from SEC EDGAR (CIK: 0000063908), us
  • **Genesis Date:** 1955-04-15 (Provided)
  • **Power Law Fit:** Value ~= 0.5871 * t^1.1022
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.9767 vs Linear R²: 0.9729
  • **Current Value:** 0.0% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • McDonald's Restaurants Scatter Plot McDonald's Restaurants Log-Log Fit

    Financial Asset

    Bitcoin

  • **R² (Scale Invariance):** 0.9079
  • **Power Law Exponent:** 5.74
  • **Data Points:** 5,566
  • **Date Range:** 2010-07-18 to 2025-11-09
  • **Log Residual Std Dev:** 0.6935
  • **Data Source:** btcpricehistory.csv (repository data)
  • **Genesis Date:** 2009-01-03 (Provided)
  • **Power Law Fit:** Value ~= 2.143e-17 * t^5.7385
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.8811 vs Linear R²: 0.6086
  • **Current Value:** 0.0% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Bitcoin Scatter Plot Bitcoin Log-Log Fit

    Microsoft Corp.

  • **R² (Scale Invariance):** 0.8151
  • **Power Law Exponent:** 4.91
  • **Data Points:** 9,993
  • **Date Range:** 1986-03-13 to 2025-11-07
  • **Log Residual Std Dev:** 0.5848
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 1975-04-04 (Provided)
  • **Power Law Fit:** Value ~= 2.596e-19 * t^4.9138
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.9792 vs Linear R²: 0.5089
  • **Current Value:** 67.9% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Microsoft Corp. Scatter Plot Microsoft Corp. Log-Log Fit

    Tesla Inc.

  • **R² (Scale Invariance):** 0.7543
  • **Power Law Exponent:** 5.05
  • **Data Points:** 3,866
  • **Date Range:** 2010-06-29 to 2025-11-07
  • **Log Residual Std Dev:** 0.5591
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 2003-07-01 (Provided)
  • **Power Law Fit:** Value ~= 5.278e-18 * t^5.0530
  • Saturation Analysis (S-Curve Detection):

  • **📈 S-CURVE DETECTED** (Sigmoid R² = 0.8865 vs Linear R² = 0.6847)
  • **Current Progress:** 171.4% of estimated maximum
  • **Saturation Level:** 2.51e+02 (estimated carrying capacity)
  • **✅ HAS REACHED SATURATION** (≥95% of maximum)
  • **Growth Pattern:** Decelerating (-244.1% slowdown in recent period)
  • Tesla Inc. Scatter Plot Tesla Inc. Log-Log Fit Tesla Inc. S-Curve Saturation

    Alphabet Inc.

  • **R² (Scale Invariance):** 0.7494
  • **Power Law Exponent:** 2.18
  • **Data Points:** 5,341
  • **Date Range:** 2004-08-19 to 2025-11-07
  • **Log Residual Std Dev:** 0.3312
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 1998-09-04 (Provided)
  • **Power Law Fit:** Value ~= 2.253e-07 * t^2.1758
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.9511 vs Linear R²: 0.7689
  • **Current Value:** 0.0% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Alphabet Inc. Scatter Plot Alphabet Inc. Log-Log Fit

    Gold ETF (SPDR)

  • **R² (Scale Invariance):** 0.5594
  • **Power Law Exponent:** 0.34
  • **Data Points:** 5,277
  • **Date Range:** 2004-11-18 to 2025-11-07
  • **Log Residual Std Dev:** 0.2191
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 2004-11-18 (Provided)
  • **Power Law Fit:** Value ~= 8.49 * t^0.3366
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.7333 vs Linear R²: 0.6669
  • **Current Value:** 0.0% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Hockey Stick Pattern Detected:

  • **🏒 INFLECTION POINT:** 2015-05-15
  • **Early Period (Blade):**
  • - R² = 0.7670

    - Exponent = 0.37

    - Data points = 2638

  • **Late Period (Shaft):**
  • - R² = 0.8114

    - Exponent = 1.34

    - Data points = 2638

  • **Growth Acceleration:** 3.6x faster in late period
  • **Model Improvement:** Split model R² = 0.8504 vs Single R² = 0.7523 (+0.0981)
  • **Analysis:** Asset shows distinct regime change - early slow growth followed by acceleration
  • Gold ETF (SPDR) Scatter Plot Gold ETF (SPDR) Log-Log Fit Gold ETF (SPDR) Early Period (Hockey Stick Blade) Gold ETF (SPDR) Late Period (Hockey Stick Shaft)

    S&P 500 ETF

  • **R² (Scale Invariance):** 0.4041
  • **Power Law Exponent:** 0.48
  • **Data Points:** 8,252
  • **Date Range:** 1993-01-29 to 2025-11-07
  • **Log Residual Std Dev:** 0.5001
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 1993-01-22 (Provided)
  • **Power Law Fit:** Value ~= 2.132 * t^0.4757
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.9778 vs Linear R²: 0.7296
  • **Current Value:** 0.0% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Hockey Stick Pattern Detected:

  • **🏒 INFLECTION POINT:** 2009-06-17
  • **Early Period (Blade):**
  • - R² = 0.7576

    - Exponent = 0.39

    - Data points = 4126

  • **Late Period (Shaft):**
  • - R² = 0.9794

    - Exponent = 3.03

    - Data points = 4126

  • **Growth Acceleration:** 7.7x faster in late period
  • **Model Improvement:** Split model R² = 0.9600 vs Single R² = 0.7052 (+0.2547)
  • **Analysis:** Asset shows distinct regime change - early slow growth followed by acceleration
  • S&P 500 ETF Scatter Plot S&P 500 ETF Log-Log Fit S&P 500 ETF Early Period (Hockey Stick Blade) S&P 500 ETF Late Period (Hockey Stick Shaft)

    Apple Inc.

  • **R² (Scale Invariance):** 0.4032
  • **Power Law Exponent:** 2.87
  • **Data Points:** 11,319
  • **Date Range:** 1980-12-12 to 2025-11-07
  • **Log Residual Std Dev:** 1.4324
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 1976-04-01 (Provided)
  • **Power Law Fit:** Value ~= 9.735e-12 * t^2.8716
  • Saturation Analysis (S-Curve Detection):

  • **📈 S-CURVE DETECTED** (Sigmoid R² = 0.9805 vs Linear R² = 0.4632)
  • **Current Progress:** 88.7% of estimated maximum
  • **Saturation Level:** 3.03e+02 (estimated carrying capacity)
  • **Estimated Time to 95% Saturation:** ~4.1 years
  • **Growth Pattern:** Decelerating (-5078.8% slowdown in recent period)
  • Apple Inc. Scatter Plot Apple Inc. Log-Log Fit Apple Inc. S-Curve Saturation

    Ethereum

  • **R² (Scale Invariance):** 0.2740
  • **Power Law Exponent:** 1.90
  • **Data Points:** 2,921
  • **Date Range:** 2017-11-09 to 2025-11-07
  • **Log Residual Std Dev:** 0.7583
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 2015-07-30 (Provided)
  • **Power Law Fit:** Value ~= 0.0004909 * t^1.8979
  • Saturation Analysis (S-Curve Detection):

  • **📈 S-CURVE DETECTED** (Sigmoid R² = 0.7000 vs Linear R² = 0.5743)
  • **Current Progress:** 152.3% of estimated maximum
  • **Saturation Level:** 2.26e+03 (estimated carrying capacity)
  • **✅ HAS REACHED SATURATION** (≥95% of maximum)
  • **Growth Pattern:** Decelerating (22.3% slowdown in recent period)
  • Ethereum Scatter Plot Ethereum Log-Log Fit Ethereum S-Curve Saturation

    Financial Asset - Split

    Gold ETF (SPDR) (Early Period)

  • **R² (Scale Invariance):** 0.5747
  • **Power Law Exponent:** 0.33
  • **Data Points:** 2,639
  • **Date Range:** 2004-11-18 to 2015-05-14
  • **Log Residual Std Dev:** 0.2031
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 2004-11-18 (Provided)
  • **Power Law Fit:** Value ~= 9.028 * t^0.3268
  • Gold ETF (SPDR) (Early Period) Scatter Plot Gold ETF (SPDR) (Early Period) Log-Log Fit

    S&P 500 ETF (Early Period)

  • **R² (Scale Invariance):** 0.5739
  • **Power Law Exponent:** 0.36
  • **Data Points:** 4,126
  • **Date Range:** 1993-01-29 to 2009-06-16
  • **Log Residual Std Dev:** 0.2184
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 1993-01-22 (Provided)
  • **Power Law Fit:** Value ~= 3.759 * t^0.3627
  • S&P 500 ETF (Early Period) Scatter Plot S&P 500 ETF (Early Period) Log-Log Fit

    S&P 500 ETF (Late Period)

  • **R² (Scale Invariance):** 0.5422
  • **Power Law Exponent:** 0.37
  • **Data Points:** 4,126
  • **Date Range:** 2009-06-17 to 2025-11-07
  • **Log Residual Std Dev:** 0.3355
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 2009-06-17 (Hockey Stick Inflection)
  • **Power Law Fit:** Value ~= 12.2 * t^0.3742
  • S&P 500 ETF (Late Period) Scatter Plot S&P 500 ETF (Late Period) Log-Log Fit

    Gold ETF (SPDR) (Late Period)

  • **R² (Scale Invariance):** 0.1743
  • **Power Law Exponent:** 0.12
  • **Data Points:** 2,638
  • **Date Range:** 2015-05-15 to 2025-11-07
  • **Log Residual Std Dev:** 0.2204
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 2015-05-15 (Hockey Stick Inflection)
  • **Power Law Fit:** Value ~= 65.03 * t^0.1215
  • Gold ETF (SPDR) (Late Period) Scatter Plot Gold ETF (SPDR) (Late Period) Log-Log Fit

    Health

    DTP3 Immunization

  • **R² (Scale Invariance):** 0.7149
  • **Power Law Exponent:** 0.05
  • **Data Points:** 22
  • **Date Range:** 2003-01-01 to 2024-01-01
  • **Log Residual Std Dev:** 0.0177
  • **Data Source:** WHO API (WHS4_100)
  • **Genesis Date:** 2002-05-26 (Optimized vs published)
  • **Power Law Fit:** Value ~= 56.33 * t^0.0473
  • Saturation Analysis (S-Curve Detection):

  • **📈 S-CURVE DETECTED** (Sigmoid R² = 0.9094 vs Linear R² = 0.6163)
  • **Current Progress:** 757.5% of estimated maximum
  • **Saturation Level:** 1.12e+01 (estimated carrying capacity)
  • **✅ HAS REACHED SATURATION** (≥95% of maximum)
  • **Growth Pattern:** Decelerating (17.9% slowdown in recent period)
  • Genesis Date Optimization:

  • **Optimal Genesis:** 2002-05-26 (R² = 0.7195)
  • **Note:** No published genesis date available for comparison
  • **Tested:** 11 candidate dates across range 2002-05-26 to 2004-11-11
  • DTP3 Immunization Scatter Plot DTP3 Immunization Log-Log Fit DTP3 Immunization S-Curve Saturation

    Measles Immunization

  • **R² (Scale Invariance):** 0.6815
  • **Power Law Exponent:** 0.04
  • **Data Points:** 22
  • **Date Range:** 2003-01-01 to 2024-01-01
  • **Log Residual Std Dev:** 0.0186
  • **Data Source:** WHO API (WHS4_544)
  • **Genesis Date:** 2002-05-26 (Optimized vs published)
  • **Power Law Fit:** Value ~= 58.51 * t^0.0426
  • Saturation Analysis (S-Curve Detection):

  • **📈 S-CURVE DETECTED** (Sigmoid R² = 0.8641 vs Linear R² = 0.5063)
  • **Current Progress:** 734.6% of estimated maximum
  • **Saturation Level:** 1.14e+01 (estimated carrying capacity)
  • **✅ HAS REACHED SATURATION** (≥95% of maximum)
  • **Growth Pattern:** Decelerating (-7.8% slowdown in recent period)
  • Genesis Date Optimization:

  • **Optimal Genesis:** 2002-05-26 (R² = 0.6767)
  • **Note:** No published genesis date available for comparison
  • **Tested:** 11 candidate dates across range 2002-05-26 to 2004-11-11
  • Measles Immunization Scatter Plot Measles Immunization Log-Log Fit Measles Immunization S-Curve Saturation

    Infrastructure

    Electricity Access

  • **R² (Scale Invariance):** 0.9188
  • **Power Law Exponent:** 1.06
  • **Data Points:** 26
  • **Date Range:** 1998-01-01 to 2023-01-01
  • **Log Residual Std Dev:** 0.0102
  • **Data Source:** World Bank API (EG.ELC.ACCS.ZS)
  • **Genesis Date:** 1880-01-01 (Provided)
  • **Power Law Fit:** Value ~= 0.0008957 * t^1.0629
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.9805 vs Linear R²: 0.9766
  • **Current Value:** 0.5% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Electricity Access Scatter Plot Electricity Access Log-Log Fit

    Technology

    Internet Users

  • **R² (Scale Invariance):** 0.9776
  • **Power Law Exponent:** 2.38
  • **Data Points:** 20
  • **Date Range:** 2005-01-01 to 2024-01-01
  • **Log Residual Std Dev:** 0.0368
  • **Data Source:** World Bank API (IT.NET.USER.ZS)
  • **Genesis Date:** 1983-01-01 (Provided)
  • **Power Law Fit:** Value ~= 8.467e-09 * t^2.3786
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.9951 vs Linear R²: 0.9913
  • **Current Value:** 40.4% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Internet Users Scatter Plot Internet Users Log-Log Fit

    Broadband Subscriptions

  • **R² (Scale Invariance):** 0.9580
  • **Power Law Exponent:** 2.03
  • **Data Points:** 20
  • **Date Range:** 2005-01-01 to 2024-01-01
  • **Log Residual Std Dev:** 0.0575
  • **Data Source:** World Bank API (IT.NET.BBND.P2)
  • **Genesis Date:** 1990-01-01 (Provided)
  • **Power Law Fit:** Value ~= 1.065e-07 * t^2.0251
  • Broadband Subscriptions Scatter Plot Broadband Subscriptions Log-Log Fit

    Mobile Subscriptions

  • **R² (Scale Invariance):** 0.7794
  • **Power Law Exponent:** 2.23
  • **Data Points:** 36
  • **Date Range:** 1984-01-01 to 2024-01-01
  • **Log Residual Std Dev:** 1.0850
  • **Data Source:** World Bank API (IT.CEL.SETS.P2)
  • **Genesis Date:** 1983-10-13 (Provided)
  • **Power Law Fit:** Value ~= 3.676e-08 * t^2.2330
  • Saturation Analysis (S-Curve Detection):

  • **📈 S-CURVE DETECTED** (Sigmoid R² = 0.9992 vs Linear R² = 0.9330)
  • **Current Progress:** 103.0% of estimated maximum
  • **Saturation Level:** 1.09e+02 (estimated carrying capacity)
  • **✅ HAS REACHED SATURATION** (≥95% of maximum)
  • **Growth Pattern:** Decelerating (61.4% slowdown in recent period)
  • Mobile Subscriptions Scatter Plot Mobile Subscriptions Log-Log Fit Mobile Subscriptions S-Curve Saturation

    Data Sources

    All data downloaded from live APIs:

  • **Yahoo Finance API**: Financial assets (stocks, crypto, ETFs)
  • Data cached in `data_cache/` directory
  • Each cache file includes metadata with source URL and download date

  • Methodology

    Scale invariance is measured by:

    1. Selecting random pairs of dates (t₁, t₂)

    2. Computing time_ratio = (t₂ - t₀) / (t₁ - t₀)

    3. Computing value_ratio = V(t₂) / V(t₁)

    4. Fitting power law in log-log space

    5. Measuring R² (goodness of fit)

    High R² (>0.95) indicates true scale invariance and power law behavior.