An end-to-end Python data pipeline — FRED API extraction, pandas ETL, statistical trend detection, recession early-warning signals, and AI-generated macro briefings. Scroll to explore the data.
Click an indicator. Move your cursor over the chart.
A 0–100 composite gauge synthesising all 7 weighted indicators — normalised against 10 years of historical range, direction-adjusted, and colour-banded into four health states. One number that captures the macro picture.
6-month ARIMA(1,1,1) predictions across every indicator, with 80% and 95% confidence interval bands. Fitted via statsmodels with AIC reported. Turns a retrospective view into a forward-looking one.
Claudia Sahm's recession early-warning indicator — triggered when the 3-month average unemployment rate rises ≥ 0.5pp above its 12-month minimum. Accurate to every US recession since 1970.
On each data refresh, the Claude API generates a 3-sentence plain-English macro briefing citing the actual numbers — so the dashboard explains itself. Optionally enabled via an Anthropic API key.
Linear regression over a rolling 6-month window — flagged only when statistically significant (p < 0.10, R² ≥ 0.30). Suppresses noise so signals actually mean something. YoY expressed as pp-change for rates, % for levels.
Python 3.11+. A free FRED API key from the St. Louis Fed. Anthropic key is optional — it enables the AI briefing panel.
# 1. Clone the repo git clone https://github.com/tarang-tj/economic-pulse-dashboard.git cd economic-pulse-dashboard # 2. Install (statsmodels + anthropic included) pip install -r requirements.txt # 3. Get free FRED key at fred.stlouisfed.org # 4. Add your keys cp .env.example .env # FRED_API_KEY=... (required) # ANTHROPIC_API_KEY=... (optional) # 5. Run python pipeline.py && python app.py