cfaf38b0be
COMPLETE: Real-time unusual activity detection for congressional tickers
New Database Model:
- MarketAlert: Stores unusual market activity alerts
* Tracks volume spikes, price movements, volatility
* JSON details field for flexible data storage
* Severity scoring (1-10 scale)
* Indexed for efficient queries by ticker/timestamp
New Modules:
- src/pote/monitoring/market_monitor.py: Core monitoring engine
* get_congressional_watchlist(): Top 50 most-traded tickers
* check_ticker(): Analyze single stock for unusual activity
* scan_watchlist(): Batch analysis of multiple tickers
* Detection logic:
- Unusual volume (3x average)
- Price spikes/drops (>5%)
- High volatility (2x normal)
* save_alerts(): Persist to database
* get_recent_alerts(): Query historical alerts
- src/pote/monitoring/alert_manager.py: Alert formatting & filtering
* format_alert_text(): Human-readable output
* format_alert_html(): HTML email format
* filter_alerts(): By severity, ticker, type
* generate_summary_report(): Text/HTML reports
Scripts:
- scripts/monitor_market.py: CLI monitoring tool
* Continuous monitoring mode (--interval)
* One-time scan (--once)
* Custom ticker lists or auto-detect congressional watchlist
* Severity filtering (--min-severity)
* Report generation and saving
Migrations:
- alembic/versions/f44014715b40_add_market_alerts_table.py
Documentation:
- docs/11_live_market_monitoring.md: Complete explanation
* Why you can't track WHO is trading
* What IS possible (timing analysis)
* How hybrid monitoring works
* Data sources and APIs
Usage:
# Monitor congressional tickers (one-time scan)
python scripts/monitor_market.py --once
# Continuous monitoring (every 5 minutes)
python scripts/monitor_market.py --interval 300
# Monitor specific tickers
python scripts/monitor_market.py --tickers NVDA,MSFT,AAPL --once
Next Steps (Phase 2):
- Disclosure correlation engine
- Timing advantage calculator
- Suspicious trade flagging
Generic single-database configuration.