Regime assignments use canonical definitions established 2026-03-07 (k=9, deep-history WCSS elbow analysis). View WCSS analysis.

Macro Economic Regime Clustering (2005–2026)MODERN DATASET

Published 2026-03-07

This analysis identifies distinct macroeconomic regimes by applying unsupervised K-Means clustering to 22 weekly-aggregated macro indicators spanning 2005 to 2026 (1,105 weeks of data). The algorithm discovered 9 distinct regimes, each characterized by a unique combination of growth, inflation, credit, and risk conditions.

Identified Regimes

Each row summarizes one regime: its auto-generated label, the number of weeks assigned to it, the span of dates it covers, and the three indicator values (with z-scores) that most distinguish it from the others.

Visualization

Top panel — PCA Feature Space: Each dot represents one week projected into two principal components (capturing 61.0% of total variance). Color indicates the assigned regime. Dots that form tight, well-separated clusters indicate regimes with distinctive macro fingerprints.

Bottom panel — Regime Timeline: The same regime assignments plotted chronologically, so you can see when each macro regime was in force. The legend below the timeline panel identifies each color.

Regime Fingerprints — Feature Z-Scores

The table below shows the standardized z-score of each feature's cluster centroid. A value of +2.0 means that regime has a feature value roughly 2 standard deviations above the historical average; −2.0 means 2 standard deviations below. Dark red = strongly above average Dark blue = strongly below average

Methodology

Data Sources

Indicators are loaded from pre-fetched S3 Parquet files collected via the Financial Modeling Prep (FMP) API and the FRED (St. Louis Fed) API. The following categories are used:

• FMP Economic Indicators — GDP, Unemployment Rate, CPI, Federal Funds Rate, Initial Jobless Claims, Retail Sales, Consumer Sentiment, Smoothed US Recession Probabilities, Industrial Production Index
• Treasury Yields & Spreads — 3-month, 2-year, and 10-year yields; derived 2s10s and 3m10y term-spread series
• Market Volatility — CBOE VIX (implied vol of S&P 500 options)
• FRED Series — ICE BofA HY OAS, IG OAS, Chicago Fed NFCI, 5-Year Breakeven Inflation, Federal Reserve Balance Sheet (WALCL), M2 Money Supply, Capacity Utilization

Sector performance data and the market risk premium snapshot were excluded. Bitcoin (BTCUSD) was excluded due to absent pre-2010 data.

Weekly Aggregation

All daily series are averaged to week-ending-Friday frequency. Monthly and quarterly series (GDP, CPI, unemployment, etc.) are forward-filled up to 92 days before resampling so that every week carries the most recently released value. Weeks with fewer than 50 % of features populated are dropped. Any remaining individual NaNs are filled with the column median before clustering.

Feature Preprocessing

All 22 features are standardized to zero mean and unit variance (sklearn.preprocessing.StandardScaler) so that indicators with vastly different magnitudes (VIX in 10–80, GDP in $ trillions) contribute equally to the K-Means distance metric.

Clustering Algorithm

K-Means clustering (sklearn.cluster.KMeans, n_init=20, max_iter=500, random_state=42) was run for k = 3 through 8. The optimal k = 9 was selected by maximising the silhouette score — a measure of how similar each point is to its own cluster compared to the nearest other cluster (range −1 to +1; higher is better).

Cluster Labeling

Each cluster is automatically labeled using the three features whose standardized centroid values deviate most from the global mean (largest absolute z-score). A positive deviation uses the feature's descriptive direction tag (e.g. a VIX centroid well above average → "Stressed"); a negative deviation uses "Low <feature name>".

Visualization

Principal Component Analysis (PCA) projects all 22 features into two dimensions for the scatter plot. PC1 and PC2 together capture 61.0% of total variance (40.9% + 20.0%).