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
117 lines
3.9 KiB
Python
Executable File
117 lines
3.9 KiB
Python
Executable File
#!/usr/bin/env python
|
|
"""
|
|
Real-time market monitoring for congressional tickers.
|
|
Run this continuously or on a schedule to detect unusual activity.
|
|
"""
|
|
|
|
import logging
|
|
import time
|
|
from datetime import datetime
|
|
from pathlib import Path
|
|
|
|
import click
|
|
|
|
from pote.db import get_session
|
|
from pote.monitoring.alert_manager import AlertManager
|
|
from pote.monitoring.market_monitor import MarketMonitor
|
|
|
|
logging.basicConfig(
|
|
level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
|
|
)
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@click.command()
|
|
@click.option("--tickers", help="Comma-separated list of tickers (default: congressional watchlist)")
|
|
@click.option("--interval", default=300, help="Scan interval in seconds (default: 300 = 5 minutes)")
|
|
@click.option("--once", is_flag=True, help="Run once and exit (no continuous monitoring)")
|
|
@click.option("--min-severity", default=5, help="Minimum severity to report (1-10)")
|
|
@click.option("--save-report", help="Save report to file")
|
|
@click.option("--lookback", default=5, help="Days of history to analyze (default: 5)")
|
|
def main(tickers, interval, once, min_severity, save_report, lookback):
|
|
"""Monitor market for unusual activity in congressional tickers."""
|
|
|
|
session = next(get_session())
|
|
monitor = MarketMonitor(session)
|
|
alert_mgr = AlertManager(session)
|
|
|
|
# Parse tickers if provided
|
|
ticker_list = None
|
|
if tickers:
|
|
ticker_list = [t.strip().upper() for t in tickers.split(",")]
|
|
logger.info(f"Monitoring {len(ticker_list)} specified tickers")
|
|
else:
|
|
logger.info("Monitoring congressional watchlist")
|
|
|
|
def run_scan():
|
|
"""Run a single scan."""
|
|
logger.info("=" * 80)
|
|
logger.info(f"Starting market scan at {datetime.now()}")
|
|
logger.info("=" * 80)
|
|
|
|
try:
|
|
# Scan for unusual activity
|
|
alerts = monitor.scan_watchlist(tickers=ticker_list, lookback_days=lookback)
|
|
|
|
if alerts:
|
|
logger.info(f"\n🔔 Found {len(alerts)} alerts!")
|
|
|
|
# Save to database
|
|
monitor.save_alerts(alerts)
|
|
|
|
# Get MarketAlert objects for reporting
|
|
from pote.db.models import MarketAlert
|
|
|
|
alert_objects = (
|
|
session.query(MarketAlert)
|
|
.order_by(MarketAlert.timestamp.desc())
|
|
.limit(len(alerts))
|
|
.all()
|
|
)
|
|
|
|
# Filter by severity
|
|
filtered = alert_mgr.filter_alerts(alert_objects, min_severity=min_severity)
|
|
|
|
if filtered:
|
|
# Generate report
|
|
report = alert_mgr.generate_summary_report(filtered, format="text")
|
|
print("\n" + report)
|
|
|
|
# Save report if requested
|
|
if save_report:
|
|
Path(save_report).write_text(report)
|
|
logger.info(f"Report saved to {save_report}")
|
|
else:
|
|
logger.info(f"No alerts above severity {min_severity}")
|
|
else:
|
|
logger.info("✅ No unusual activity detected")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error during scan: {e}", exc_info=True)
|
|
|
|
logger.info("=" * 80)
|
|
logger.info(f"Scan complete at {datetime.now()}")
|
|
logger.info("=" * 80)
|
|
|
|
# Run scan
|
|
run_scan()
|
|
|
|
# Continuous monitoring mode
|
|
if not once:
|
|
logger.info(f"\n🔄 Continuous monitoring enabled (interval: {interval}s)")
|
|
logger.info("Press Ctrl+C to stop\n")
|
|
|
|
try:
|
|
while True:
|
|
time.sleep(interval)
|
|
run_scan()
|
|
except KeyboardInterrupt:
|
|
logger.info("\n\n⏹️ Monitoring stopped by user")
|
|
else:
|
|
logger.info("\n✅ Single scan complete (use --interval for continuous monitoring)")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|
|
|