Creating a Macro Regime Dashboard with Federal Reserve Economic Data

This Python project builds a realistic macroeconomic forecasting and regime analysis dashboard using economic data from the Federal Reserve Economic Data (FRED) database to monitor changing economic conditions and financial market risks. The system downloads key macroeconomic indicators including the Fed Funds Rate, CPI inflation, unemployment rate, Treasury yields, industrial production, consumer sentiment, and money supply using the pandas_datareader library. Multiple macroeconomic features are engineered such as year-over-year inflation, industrial production growth, money supply growth, and six-month changes in interest rates, unemployment, and Treasury yields to measure economic momentum and policy shifts. A rule-based macro regime classification system is then developed to categorize the economy into states such as “Expansion,” “Inflation / Tightening,” “Recession Risk,” “Late Cycle / Inversion,” and “Neutral / Transition” based on macroeconomic conditions. Several interactive Plotly dashboards are created to visualize inflation regimes, interest rate trends, yield curve behavior, industrial production growth, unemployment conditions, and consumer sentiment over time. The project also generates a standardized macroeconomic heatmap using z-scores to compare changing economic conditions across multiple indicators simultaneously. Machine learning techniques are introduced by applying KMeans clustering to identify hidden macroeconomic regimes and group similar economic environments based on standardized macro features. The dashboard then produces a “Latest Macro Signal” summarizing the most recent economic conditions, including inflation, interest rates, unemployment, industrial production growth, and the current macro regime classification. A macro risk scoring framework is also implemented to quantify economic stress by combining signals such as high inflation, rising interest rates, inverted yield curves, rising unemployment, and weak industrial production. Finally, the project demonstrates how Python, FRED data, machine learning, and Plotly visualizations can be combined to build a professional-style macroeconomic monitoring and forecasting platform similar to systems used by institutional investors, hedge funds, banks, and economic research teams.

Видео Creating a Macro Regime Dashboard with Federal Reserve Economic Data канала Analytics in Practice