POTE/scripts/monitor_market.py
ilia cfaf38b0be Phase 1: Real-Time Market Monitoring System
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
2025-12-15 15:10:49 -05:00

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()