Automated Stock Analysis Engine

The Challenge

Manually monitoring dozens of stocks for technical crossover patterns is time-consuming and impossible to do consistently — markets move fast and signals can appear at any time of day. A private client needed a reliable system to track Golden Crossover (bullish) and Black Cross (bearish) signals across their watchlist without constant manual effort.

Additionally, they needed smart notification management — not just raw alerts, but signals with cooldown logic to prevent alert fatigue.

The Solution

I built a full-stack Python application with a FastAPI backend, a real-time monitoring engine, and a dynamic frontend dashboard.

• Signal Engine: Calculates 50-day and 200-day DMAs on live stock data; fires alerts when crossover conditions are met
• Buffered Alert System: 5% buffer around crossover zones prevents false positives from minor price fluctuations
• Cooldown Mechanism: Prevents duplicate alerts for the same signal within a configurable time window
• Email Reports: Formatted HTML emails with signal type, stock name, price, and DMA levels sent within seconds of detection
• Penny Stock Filter: Automatically excludes low-value stocks that generate noisy signals
• Dashboard: Real-time web UI showing current watchlist, DMA levels, and alert history

The Results

The system has been running continuously, catching every major crossover event on the watchlist. The buffered alert system eliminated false positive alerts that were a problem with simpler threshold-based approaches.

Tech Stack