Hey Supabase community! 🎉
I built iGotFomo — a free stock market analytics platform that's fundamentally PostgreSQL-powered. Wanted to share our architecture and how PostgreSQL handles everything.
We store and query:
stock_ohlc_data)-- Dynamic signal scoring with IC-weighted composites
SELECT symbol,
SUM(signal_weight * CASE WHEN signal_active THEN 1 ELSE 0 END) as convergence_score
FROM convergence_signals
GROUP BY symbol
ORDER BY convergence_score DESC;
CREATE TABLE IF NOT EXISTS in service initialize() methodsraw_data columns)ON CONFLICT DO UPDATE for upsert patterns everywhere| Tool | Link |
|---|---|
| Stock Screener (6K+ stocks) | igotfomo.com/stocks/screener |
| Market Pulse Dashboard | igotfomo.com/tools/market-pulse.html |
| Investment Calculator | igotfomo.com/tools/stock-calculator.html |
| Live Ticker Widget | igotfomo.com/widgets/ |
All data stays local — zero external API calls during analysis. PostgreSQL handles the entire data pipeline.
Anyone else using PostgreSQL for heavy analytical workloads? Would love to compare approaches!
The user shares their experience building a financial analytics platform, iGotFomo, using PostgreSQL as the primary database. They detail the architecture, data handling, and key patterns used in their system, including real-time data processing and machine learning feedback loops. The user seeks to compare approaches with others using PostgreSQL for similar analytical workloads.