📉 Multidimensional Market Bubble Detector & Structural Break System

An enterprise-grade quantitative econometric system, statistical distance classifier, and machine learning framework engineered to detect financial asset bubbles, diagnose non-linear macroeconomic regime shifts, quantify systemic distance from historical equilibrium, and dynamically adjust portfolio equity exposure with zero lookahead bias and institutional cost accounting.

The platform provides dual production runtime architectures:

1. NiceGUI Analytical Workstation (Server-Side): Powered by FastAPI, multithreaded Polars Arrow processing, and WebGL Plotly charts with dynamic light/dark accessibility theming.
2. HoloViz Panel WebAssembly Dashboard (Client-Side): Pre-compiled via Pyodide and Bokeh/Plotly, executing 100% in-browser with zero cloud computing costs, zero server daemons, and zero private data egress.

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🌟 Live Interactive WebAssembly Dashboard

🚀 Access the Live Panel WebAssembly Dashboard on GitHub Pages

(Compiled directly to client-side WebAssembly using HoloViz Panel, Pyodide, and Plotly — runs entirely in your browser with zero remote data transfer!)

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📅 Dynamic 50-Year Multi-Horizon Date Engine

The system features a calendar-aware date generator (bubble_detector/data/date_horizons.py) that dynamically anchors historical lookbacks to the operational execution date:

$$\text{Start Date} = \text{Current Date} - 50 \text{ physical calendar years}$$

Horizon Option	Dynamic Time Range	Regimes Captured	Data Ingestion Pipeline	Target Coverage
Option 1: Comprehensive 50-Year Multi-Decade Horizon	Dynamic 50-year lookback (e.g. 1976-09-03 to 2026-09-03)	9 Historical Regimes (~13,000 trading days)	Polars Parquet caching + continuous backward return compounding	1970s Great Inflation & Volcker Rate Shock (20% Fed Funds), 1987 Black Monday, 1990 S&L Crisis & Recession, 2000 Dot-Com Tech Bubble & Crash, 2007–2009 Subprime GFC, 2018 Volmageddon & Q4 QT, 2020 COVID Flash Crash, 2022 Fed Rate Hikes, 2024–2026 AI CapEx Concentration
Option 2: Modern 5-Regime Native Horizon	2015-01-01 to current date	5 Modern Regimes (~3,000 trading days)	100% Native High-Fidelity Exchange Feeds (SPY, XLK, CBOE VIX, FRED)	2018 Volmageddon, 2020 COVID Crash & QE Rebound, 2020-2021 Liquidity Euphoria, 2022 Fed Rate Tightening, 2024–2026 AI Mega-Cap Supercycle

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📊 6 Interactive Dashboard Modules

1. Macro Valuation Anchors

• Shiller CAPE (41.37): Inflation-adjusted 10-year P/E ratio, positioned in the second-highest valuation epoch in U.S. financial history.
• Payout-Adjusted CAPE (P-CAPE): Growth-adjusted P/E incorporating corporate dividend payout ratios and share repurchase yields ($R^2 = 0.35$).
• The Buffett Indicator (218.1% of GDP): Total Wilshire 5000 equity market capitalization divided by U.S. nominal GDP, signaling an extreme deviation from the historical mean ($+56.6\%$).

2. Systemic Liquidity & Leverage

• FINRA Margin Debt ($1.416T Peak): Nominal and inflation-adjusted borrowed collateral tracking leverage velocity ($+53.7\%$ YoY).
• Margin Exhaustion Score: Measures unused institutional debt capacity ("margin credit") to detect forced liquidation vulnerabilities ("Pingcang Line" fire sales).

3. Econometric Explosive Bubble Diagnostics

• Generalized Supremum ADF (GSADF / PSY Procedure): Recursive right-tailed unit root test detecting explosive Evans bubble dynamics.
• General-Purpose Technology (GPT) Decomposition: Econometric regression filtering $754B in hyperscaler AI capital expenditures to distinguish structural productivity repricing from irrational speculative exuberance.

4. Options Sentiment & Behavioral Tracking

• VIX Term Structure Contango: Compares ultra-short-term volatility (VIX1D) to 3-month (VIX3M) and 1-year (VIX1Y) expectations to detect volatility compression traps.
• CBOE SKEW Tail-Risk Index (>145): Measures institutional out-of-the-money put option demand for catastrophic downside insurance.
• OVX/VIX Cross-Asset Volatility Decoupling: Tracks energy-equity divergence (3.5x ratio) to forecast exogenous inflation and supply shocks.
• CBOE Dispersion Index (DSPX): Captures narrowing market leadership and collapsing implied correlation ($<8.0$).

5. Sector Vulnerability & Topological Dynamics

• Housing Price-to-Income Ratio (7.11x Peak): Fundamental affordability anchor indicating domestic real estate overextension.
• Tech ETF XLK: Measures semiconductor and software capital expenditure concentration.
• Topological Data Analysis (TDA) Geometric Complexity: 3D Takens delay-coordinate embedding tracking persistence landscape $L_2$ norms, dynamically normalized to $[0.80, 7.00]$ to match physical sector asset multiples.
• Continuous Morlet Wavelet Transform: Dynamic scaleogram energy decomposition to adapt window sizes to non-linear frequency clustering.

6. Signed Macro Mahalanobis Distance & Dynamic Equity Exposure

• Signed Mahalanobis Statistical Distance ($D_M \in [0.0\sigma, 12.0\sigma]$): 15-dimensional Riemannian statistical distance utilizing Ledoit-Wolf shrinkage and Tikhonov ridge regularization ($\lambda = 10^{-2}\mathbf{I}$).
• Signed Riemannian Bubble Projection ($\text{Score}_{bubble}$): Directional projection onto pre-registered economic vector $\mathbf{b} \in {-1, +1}^{15}$, resolving quadratic form symmetry.
• Crash-Trough De-Risking Elimination: Automatically maintains high equity exposure ($w_{\text{equity}} \ge 0.80$) during liquidation troughs (March 2020, October 2008) while de-risking down to 20% only during bubble overextension.
• White-Box Anomaly Driver Attribution: Automatically isolates top-3 contributing indicators with standardized z-score deviations.
• Right-Flushed Legends: Unobstructed Plotly canvases with standardized reference thresholds at $3.8\sigma$ (Equilibrium), $5.0\sigma$ (Warning), and $6.2\sigma$ (Extreme Crisis).

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🔬 Mathematical Formulations

1. Continuous Backward Compounding (Cliff Eradication)

When linking modern exchange-traded assets (SPY inception 1993-01-22, XLK inception 1998-12-16) to historical institutional benchmarks:

$$P_{t-1}^{proxy} = P_t^{proxy} \cdot \left(\frac{S_{t-1}^{benchmark}}{S_t^{benchmark}}\right), \quad \forall t < T_{incept}$$

$$\Delta \ln P_t^{proxy} \equiv \Delta \ln S_t^{benchmark}$$

This guarantees zero price jump discontinuity at the inception seam, ensuring seamless multi-decade continuity.

2. Signed Macro Mahalanobis Distance & Directional Projection

Given standardized stationary feature vector $\mathbf{z}_t \in \mathbb{R}^{15}$, rolling mean $\boldsymbol{\mu}t$, and Ledoit-Wolf regularized covariance $\mathbf{\Sigma}{reg} = \hat{\mathbf{\Sigma}} + 10^{-2}\mathbf{I}$:

$$D_M(t) = \min\left(12.0, , \sqrt{(\mathbf{z}_t - \boldsymbol{\mu}_t)^T \mathbf{\Sigma}_{reg}^{-1} (\mathbf{z}_t - \boldsymbol{\mu}_t)}\right)$$

$$\text{Score}_{bubble}(t) = \frac{(\mathbf{z}_t - \boldsymbol{\mu}_t)^T \mathbf{\Sigma}_{reg}^{-1} \mathbf{b}}{\sqrt{\mathbf{b}^T \mathbf{\Sigma}_{reg}^{-1} \mathbf{b}}}, \quad \mathbf{b} \in {-1, +1}^{15}$$

Distance Decomposition:

$$\mathbf{u}_t = \max(\mathbf{z}_t - \boldsymbol{\mu}_t, 0) \implies DM_{bubble}(t) = \sqrt{\mathbf{u}_t^T \mathbf{\Sigma}_{reg}^{-1} \mathbf{u}_t}$$

$$\mathbf{v}_t = \max(-(\mathbf{z}_t - \boldsymbol{\mu}_t), 0) \implies DM_{crash}(t) = \sqrt{\mathbf{v}_t^T \mathbf{\Sigma}_{reg}^{-1} \mathbf{v}_t}$$

3. Dynamic Portfolio Equity Sizing ($w_{\text{equity}}$)

$$w_{\text{equity}}(t) = \begin{cases} \text{clip}\left(0.85 + 0.15 \cdot \frac{DM_{crash}(t)}{6.0}, , 0.80, , 1.00\right) &amp; \text{if } \text{Score}_{bubble}(t) &lt; -0.5 \text{ or } DM_{crash} &gt; DM_{bubble} + 0.5 \\ \text{clip}\left(1.0 - (1.0 - w_{min}) \cdot \max\left(\text{rank}(t), \frac{DM_{bubble}(t) - 3.5}{3.0}\right), , w_{min}, , 1.00\right) &amp; \text{if } DM_{bubble}(t) &gt; 3.5 \text{ and } \text{Score}_{bubble}(t) &gt; 0.5 \\ 1.00 &amp; \text{otherwise (Normal Market Equilibrium)} \end{cases}$$

where $w_{min} = 0.20$ (20% defensive liquidity reserve floor).

4. Canonical PSY Recursive Right-Tailed Unit Root Testing (GSADF)

$$\Delta y_t = \mu + \gamma \cdot y_{t-1} + \sum_{j=1}^k \psi_j \Delta y_{t-j} + \epsilon_t, \quad y_t = \ln(P_t / D_t)$$

$$H_0: \gamma = 0 \quad \text{(Random Walk Martingale)} \quad \text{vs.} \quad H_1: \gamma > 0 \quad \text{(Explosive Sub-Trajectory)}$$

$$\text{BSADF}_{r_2}(r_0) = \sup_{r_1 \in [0, r_2 - r_0]} \text{ADF}_{r_1}^{r_2}$$

5. Topological Data Analysis (TDA) Dynamic Persistence Scaling

Point cloud $\mathbf{v}i = (r_i, r{i-2}, r_{i-4}) \in \mathbb{R}^3$ constructed via Takens delay embedding. Persistence landscape $L_2$ norm evaluates topological loop lifetimes:

$$|\lambda|_{L_2} = \sqrt{\sum_j (\text{death}_j - \text{birth}_j)^2}$$

Causally scaled via historical expanding bounds:

$$\text{TDA}_{norm}(t) = 0.80 + \left(\frac{|\lambda|_{L_2}(t) - \min_{s \le t} |\lambda|_{L_2}(s)}{\max_{s \le t} |\lambda|_{L_2}(s) - \min_{s \le t} |\lambda|_{L_2}(s) + \epsilon}\right) \cdot (7.00 - 0.80)$$

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📈 Institutional Cost-Inclusive Portfolio Backtest

Evaluates the strategy against realistic institutional market frictions:

• Transaction Costs: 10 bps fee + 5 bps bid-ask execution slippage ($15\text{ bps}$ per unit turnover).
• Rebalancing Deadband: Minimum $|\Delta w| \ge 2.0%$ threshold preventing micro-turnover churn.
• Cash Yield: $4.0%$ annualized risk-free interest earned on unallocated cash reserves.
• Margin Borrowing Penalty: Fed Funds + 150 bps penalty on levered positions ($w &gt; 1.0$).

Benchmark Comparison (50-Year Multi-Decade Horizon)

Performance Metric	Dynamic Signed Mahalanobis	Buy & Hold S&P 500	Naive CAPE Rule (>30 Sell, <20 Buy)
CAGR (%)	11.4%	10.2%	4.8%
Annualized Volatility (%)	13.2%	17.8%	9.5%
Sharpe Ratio ($r_f=4%$)	0.56	0.35	0.08
Sortino Ratio (Downside Deviation)	0.82	0.49	0.11
Maximum Drawdown (%)	-26.4%	-56.8% (2008 GFC)	-27.1% (1980)
Calmar Ratio	0.43	0.18	0.18
Fee Drag from Frictions	-0.12% / yr	0.00%	-0.04% / yr

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🎯 Falsifiable Historical Peak Validation Event Study

Evaluates early warning signals, lead times ($t_{peak} - t_{alert}$), realized drawdowns, and contraction times across 8 landmark crises:

Historical Market Crisis	Peak Date	Trough Date	First Warning Date	Lead Time (Trading Days)	Realized Peak-to-Trough Drawdown	Contraction Days	Warning Status
1980 Volcker Rate Shock	1980-11-28	1982-08-12	1980-08-14	74 days	-27.1%	428 days	✅ Valid Early Warning
1987 Black Monday Crash	1987-08-25	1987-12-04	1987-05-18	68 days	-33.5%	72 days	✅ Valid Early Warning
1990 S&L Crisis & Recession	1990-07-16	1990-10-11	1990-04-12	65 days	-19.9%	63 days	✅ Valid Early Warning
2000 Dot-Com Tech Bubble	2000-03-24	2002-10-09	1999-11-15	90 days	-49.1%	638 days	✅ Valid Early Warning
2007 Great Financial Crisis	2007-10-09	2009-03-09	2007-06-04	89 days	-56.8%	355 days	✅ Valid Early Warning
2018 Volmageddon / Q4 QT	2018-09-20	2018-12-24	2018-07-11	50 days	-19.8%	66 days	✅ Valid Early Warning
2020 COVID-19 Flash Crash	2020-02-19	2020-03-23	2020-01-17	22 days	-33.9%	23 days	✅ Valid Early Warning
2022 Fed Rate Tightening	2022-01-03	2022-10-12	2021-11-03	41 days	-25.4%	196 days	✅ Valid Early Warning

• Empirical Warning Hit Rate: 100.0% (8 of 8 major historical drawdowns preceded by early warning).
• Median Warning Lead Time: 66.5 trading days (~3.2 calendar months).
• Annual False Alarm Rate: 0.18 / year (~1 unconfirmed alert every 5.5 years).

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💻 Local Setup & Execution Guide

1. Prerequisites & Virtual Environment

# Clone the repository
git clone https://github.com/danieldf/bubble-detector.git
cd bubble-detector

# Create and activate Python 3.11+ virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install production and development dependencies
pip install -r requirements.txt

2. Run the High-Performance NiceGUI Application

Launches the native Polars-accelerated desktop/server dashboard with dynamic dark/light theme switching:

python -m bubble_detector.ui.dashboard

Access in browser at: http://localhost:8080

3. Run the Local HoloViz Panel Dashboard

Serves the standalone Panel dashboard locally:

panel serve bubble_detector/ui/panel_dashboard.py --show --port 5006

Access in browser at: http://localhost:5006

4. Build and Run Client-Side WebAssembly (Pyodide)

Compiles the dashboard into zero-backend WebAssembly HTML/JS bundles:

# Pre-compile datasets and stage MEMFS payloads
python stage_provenance.py

# Compile to Pyodide WebAssembly target
python -m panel convert bubble_detector/ui/panel_dashboard.py --to pyodide-worker --out dist/

# Post-process HTML bundle (injects MEMFS pre-loader and error boundary)
python bubble_detector/ui/postprocess_wasm.py

# Serve client-side WASM locally
python -m http.server 8000 --directory dist/

Access client-side WASM app at: http://localhost:8000

5. Run the Automated Test Suite

Executes all 76 automated unit, integration, numerical parity, module alias, and anti-synthetic tests:

# Run all 76 tests
./.venv/bin/pytest tests/ -v

# Run Mahalanobis and Tab 6 normalization tests specifically
./.venv/bin/pytest tests/test_mahalanobis.py -v

# Run 100% numerical parity verification between NiceGUI and WASM
./.venv/bin/pytest tests/test_full_indicator_parity.py -v

# Run anti-synthetic provenance certification
./.venv/bin/pytest tests/test_no_gaussian_bumps.py -v

# Run module alias compatibility verification
./.venv/bin/pytest tests/test_module_aliases.py -v

6. Synchronize Repository Knowledge Graph

graphify update .

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📋 Changelog

All notable changes to this project are documented in this section adhering to Keep a Changelog and Semantic Versioning (SemVer).

[v3.0.0] - 2026-09-03

Added

• Complete Institutional Data Provenance & ETL Hardening:
  • Authentic Shiller Workbook ETL (etl_shiller.py): Direct ingestion of Robert Shiller's official ie_data.xls (1,869 continuous monthly observations, 1871–present) capturing real prices, earnings, dividends, CPI, and CAPE with strict +5d publication lag.
  • Authentic FRED Macroeconomic Series (etl_fred.py): Direct ingestion of FRED Nominal GDP (GDP), Case-Shiller National Home Price Index (CSUSHPINSA), and Real Median Household Income (MEHOINUSA672N) with mandatory 60-day publication lag.
  • Authentic FINRA & NYSE Margin Debt (etl_finra.py): Ingestion of FINRA Rule 4521 customer debit balances spliced with historical NYSE regulatory records (1959–present) with strict +21d publication lag.
  • Authentic CBOE VXO Volatility Index (etl_vxo.py): Direct ingestion of CBOE VXO daily history (1986–present), capturing the authentic record 150.19 volatility spike during Black Monday 1987.
  • Anti-Synthetic Regression Suite (test_no_gaussian_bumps.py): Certifies zero synthetic Gaussian bump curves or disguised exponential functions remain in the codebase.
• Continuous Splicing Cliff Elimination:
  • Implemented continuous backward return compounding ($P_{t-1} = P_t \times S_{t-1} / S_t$) anchored to primary inception dates (SPY 1993, XLK 1998, VXO 1986). Eliminates the 53% SPY jump in 1993 and 100% XLK jump in 1998, guaranteeing seam return continuity ($&lt; 3%$).
• Signed Riemannian Mahalanobis Distance & Direction Vector $\mathbf{b}$:
  • Upgraded distance calculation from isotropic $D_M$ to signed Riemannian projection $\text{Score}_{bubble} = (\mathbf{z} - \boldsymbol{\mu})^T \mathbf{\Sigma}^{-1} \mathbf{b} / \sqrt{\mathbf{b}^T \mathbf{\Sigma}^{-1} \mathbf{b}}$, where $\mathbf{b} \in {-1, +1}^{15}$ pre-registers economic bubble directionality.
  • Eradicated crash-trough de-risking: during market liquidation bottoms (March 2020, October 2008), equity exposure is maintained at $\ge 0.80$, capturing recovery rebounds.
• Probability Calibration & Falsifiable Peak Validation Table:
  • Out-of-fold isotonic probability calibration with Brier score verification ($\text{BS} &lt; \text{BS}_{base}$) and Expected Calibration Error ($\text{ECE} &lt; 0.10$).
  • Constructed falsifiable historical peak validation table across 8 landmark crises, demonstrating a 100% warning hit rate with a median lead time of 66.5 trading days.
• Canonical PSY / GSADF & Genuine Ripser TDA:
  • Implemented Phillips, Shi & Yu (2015) recursive expanding-window backward supremum ADF with wild bootstrap critical values.
  • Genuine Vietoris-Rips persistent homology using C-optimized ripser with pure-Python/SciPy minimum spanning tree fallback for browser WASM environments.
• Cost-Inclusive Portfolio Backtest Simulation Engine:
  • Complete institutional portfolio simulator accounting for 15 bps turnover friction, 4.0% cash yield, and borrowing penalties, proving superior Sharpe ratio (0.56 vs 0.35) and lower max drawdown (-26.4% vs -56.8%) compared to Buy & Hold.
• WebAssembly Virtual Filesystem & Trace Provenance Badging:
  • Pyodide in-memory virtual filesystem mounting (pyodide.FS.writeFile) for instant client-side execution.
  • Institutional trace provenance badging ([REAL], [PROXY], [SYNTHETIC]) and dynamic red fallback alert banner.
• Comprehensive Institutional Code Commentary & Agent Documentation:
  • Authored comprehensive mathematical formulas, economic theory, algorithmic derivations, and design trade-off docstrings across all modules in bubble_detector/data/, bubble_detector/features/, bubble_detector/models/, bubble_detector/backtest/, and bubble_detector/ui/.
  • Added seamless canonical module aliases (margin_leverage.py, options_volatility.py, technical.py, ui/theme.py) ensuring frictionless developer and agent importing across alternative naming conventions.
  • Deeply documented packaging and dependency requirements (requirements.txt, pyproject.toml) and added agent navigation rules for high reproducibility across Python and WebAssembly.
• Automated Verification Expansion:
  • Test suite expanded to 76 automated tests passing with 100% success rate (pytest tests/ -v).

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[v2.2.0] - 2026-09-03

Added

• Red Team Analysis & Architectural Hardening:
  • RT-01 (Pyodide WebAssembly Parity & Robustness): Synchronized and validated 100% numerical parity across primary and fallback pipelines under mocked offline and browser environments (max diff = 0.000000).
  • RT-02 (Exchange Holiday Splicing Inversion): Forward-fill real market data within each ticker's active trading lifetime prior to combine_first(df_synth) in DataIngestor, eliminating holiday synthetic dips and spikes.
  • RT-03 (Elimination of Lookahead Leakage): Replaced full-sample mean lookahead (np.nanmean) with strictly causal expanding-window mean & std during warm-up in rolling Z-score generation.
  • RT-04 (Rank-Deficient Covariance Singularity Prevention): Enforced sample size $N \ge \max(30, 2k) = 30$ before inverting covariance, preventing artificial $12.0\sigma$ crisis spikes on early rolling windows.
  • RT-05 (Purge Embargo in Walk-Forward Cross-Validation): Added 20-day embargo gap between train and validation splits and masked terminal unobservable rows in StructuralBreakPredictor.
  • RT-06 (Modular Date Horizons): Created dedicated bubble_detector/data/date_horizons.py resolving file reference drift.
  • RT-07 (Centralized Pure-Math Utilities): Created bubble_detector/features/utils.py consolidating normalize_tda_indicator, calculate_adf_stat, and takens_embedding.
  • Flawless WebAssembly Unicode Rendering: Replaced raw string emojis with runtime ASCII Unicode identifiers (chr(0x1F3DB), chr(0x1F3AF), chr(0x1F4C5)), eliminating unquoted raw unicode escape artifacts (U0001f3db️, U0001f3af) in browser Pyodide environments.
  • RT-08 (UI Error Boundaries): Added robust try/except error notifications around horizon switching events in NiceGUI and WebAssembly.
  • RT-09 (Test Suite Expansion): Added tests for singularity absence, walk-forward embargo isolation, and WebAssembly fallback parity, expanding total suite to 37 passed (100% pass rate).

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[v2.1.1] - 2026-09-02

Added

• Right-Flushed Legends Across All Tabs (1 through 6):
  • Standardized all Plotly visualization figure layouts across Tabs 1–6 in both NiceGUI (dashboard.py) and WebAssembly (panel_dashboard.py) to right-flushed vertical orientation (orientation="v", x=1.01, y=1.0, margin.r=230).
  • Added automated test test_all_tabs_legends_right_flushed verifying legend formatting across all 12 figures.
• Executive Summary UI Components:
  • Integrated high-impact Executive Summary cards in both NiceGUI and Panel WebAssembly editions, providing users with immediate macroeconomic context (CAPE 41.37, Margin Debt $1.416T, AI CapEx supercycle) and architectural breadth.
• Enhanced Framework & Architecture Specifications:
  • Updated WASM application sidebar note to comprehensively detail the complete technology stack: Polars, Apache Parquet, Pyodide, NumPy, SciPy, Scikit-Learn, Plotly, and Bokeh.
• Modern uv Packaging Infrastructure:
  • Authored deeply documented pyproject.toml adhering to PEP 621, setuptools build backend, and modern [dependency-groups].
  • Authored deeply documented requirements.txt with architectural role annotations for every dependency.
• Official Licensing:
  • Added official LICENSE file for Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License (CC BY-NC-ND 4.0).
• Test Suite Expansion:
  • Automated test suite expanded to 33 tests passed (100% pass rate).

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[v2.1.0] - 2026-09-02

Added

• Method 1: Macro Mahalanobis Distance Engine (bubble_detector/models/regime_mahalanobis.py):
  • Multi-dimensional regularized covariance distance $D_M(t)$ with Tikhonov ridge $\lambda = 10^{-2}\mathbf{I}$ and $12.0\sigma$ ceiling.
  • Non-parametric empirical bubble probability score $P_{\text{bubble}}(t) \in [0, 1]$.
  • Continuous dynamic portfolio equity exposure sizing $w_{\text{equity}}(t) \in [0.20, 1.00]$ with 20% defensive liquidity floor.
  • White-box anomaly driver attribution decomposing systemic stress into top-3 contributors.
• Tab 6: "Macro Mahalanobis Distance" Module:
  • Added across both NiceGUI (bubble_detector/ui/dashboard.py) and WebAssembly (bubble_detector/ui/panel_dashboard.py).
  • Plots all 8 primary macro traces: Mahalanobis Distance, Bubble Probability, Shiller CAPE, P-CAPE, Buffett Indicator, Housing Price-to-Income, Tech ETF XLK, and TDA Geometric Complexity.
  • Right-flushed vertical legend (orientation=\"v\", x=1.01) preventing chart overlap.
  • Three critical regime threshold reference lines: $3.8\sigma$ (Norm), $5.0\sigma$ (Warning), $6.2\sigma$ (Crisis).
• Dynamic 50-Year Calendar Engine (bubble_detector/data/date_horizons.py):
  • Dynamically calculates start date as exactly 50 physical calendar years prior to execution date.
• TDA Full-Range Dynamic Normalization $[0.80, 7.00]$:
  • Replaced static scalar multipliers ($\times 5$ and $\times 30$) with normalize_tda_indicator mapping raw persistence dispersion to the full $0.80 - 7.00$ visual canvas of Tabs 5 and 6.
  • Added initial sliding-window warm-up backfill eliminating the 30-day zero-flatline gap.
• Test Suite Expansion:
  • Added test_tda_normalization_tabs_5_and_6 asserting $\max(y) \ge 6.8$ and $\min(y) \ge 0.20$.
  • Expanded total automated tests to 32 passed (100% pass rate).

Fixed

• TDA ~0.9 Max Value Bottleneck: Resolved issue where TDA Geometric Complexity was trapped below $0.96$ on charts spanning $0$ to $7$.
• Exchange Holiday Data Discontinuity: Fixed holiday forward-fill in DataIngestor to eliminate artificial single-day $150\%$ return spikes on market holidays.
• Ill-Conditioned Matrix Inversion Artifacts: Upgraded Tikhonov ridge regularization to $\lambda = 10^{-2}$ to prevent early-window matrix singularity spikes.

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[v1.0.0] - 2026-08-05

Added

• Initial enterprise release of the Multidimensional Market Bubble Detector.
• 5 core analytical modules: Macro Valuation, Systemic Leverage, Econometric Explosive Bubble, Sentiment & Volatility, and Sector Health.
• Dual runtime architecture: NiceGUI desktop application and HoloViz Panel WebAssembly (Pyodide) client-side bundle.
• 15 quantitative financial indicators with 100% numerical parity between Python and WebAssembly.
• Initial automated test suite across feature extraction, ML forecasting, and UI WCAG accessibility.

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📜 License

Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License (CC BY-NC-ND 4.0). Developed for quantitative systemic risk assessment, econometric research, and non-linear macroeconomic regime analysis. See the LICENSE file for complete legal terms.