Economic Analytics

An end-to-end Python economic-intelligence platform for studying inflation, employment, economic growth, interest rates, wages, and consumer spending. It combines public API ingestion, SQLite, Pandas/NumPy analysis, and an interactive Plotly Dash dashboard.

Features

• Integrates FRED, World Bank, and BLS public economic data into normalized SQLite tables.
• Cleans missing values, removes duplicates, aligns time series, and engineers year-over-year changes and z-score anomaly signals.
• Supports correlations, trends, country comparisons, downloadable data, KPI cards, a searchable table, and visual analysis.
• Runs with synthetic demo data if an API is unavailable, so reviewers can launch it immediately.

Architecture

Public APIs → fetchers.py → cleaning/feature engineering → SQLite → Dash dashboard

Quick start

1. Install Python 3.10+.
2. Create a virtual environment:

   python -m venv .venv
   source .venv/bin/activate       # Windows: .venv\Scripts\activate
   pip install -r requirements.txt

3. Copy .env.example to .env and place your newly regenerated FRED key in it.
4. Load public data:

   python scripts/load_data.py

5. Start the dashboard:

   python app.py

6. Open http://127.0.0.1:8050 in your browser.

Database model

indicators holds canonical metadata. observations stores one country, indicator, date, value, and source per row. The primary key prevents duplicate observations during repeated loads.

## Data sources

- FRED: US CPI, unemployment, GDP, federal funds rate, PCE, and earnings.
- World Bank: international GDP growth, inflation, unemployment, and consumption.
- BLS: US total nonfarm employment.

Public APIs can revise historical values. This project is for analysis and education, not investment advice.