POTE/scripts/analyze_disclosure_timing.py

#!/usr/bin/env python
"""
Analyze congressional trade timing vs market alerts.
Identifies suspicious timing patterns and potential insider trading.
"""

import click
from pathlib import Path
from tabulate import tabulate

from pote.db import get_session
from pote.monitoring.disclosure_correlator import DisclosureCorrelator


@click.command()
@click.option("--days", default=30, help="Analyze trades filed in last N days")
@click.option("--min-score", default=50, help="Minimum timing score to report (0-100)")
@click.option("--official", help="Analyze specific official by name")
@click.option("--ticker", help="Analyze specific ticker")
@click.option("--output", help="Save report to file")
@click.option("--format", type=click.Choice(["text", "json"]), default="text")
def main(days, min_score, official, ticker, output, format):
    """Analyze disclosure timing and detect suspicious patterns."""
    
    session = next(get_session())
    correlator = DisclosureCorrelator(session)
    
    if official:
        # Analyze specific official
        from pote.db.models import Official
        official_obj = session.query(Official).filter(
            Official.name.ilike(f"%{official}%")
        ).first()
        
        if not official_obj:
            click.echo(f"❌ Official '{official}' not found")
            return
        
        click.echo(f"\n📊 Analyzing {official_obj.name}...\n")
        result = correlator.get_official_timing_pattern(official_obj.id)
        
        report = format_official_report(result)
        click.echo(report)
        
    elif ticker:
        # Analyze specific ticker
        click.echo(f"\n📊 Analyzing trades in {ticker.upper()}...\n")
        result = correlator.get_ticker_timing_analysis(ticker.upper())
        
        report = format_ticker_report(result)
        click.echo(report)
        
    else:
        # Analyze recent disclosures
        click.echo(f"\n🔍 Analyzing trades filed in last {days} days...")
        click.echo(f"   Minimum timing score: {min_score}\n")
        
        suspicious_trades = correlator.analyze_recent_disclosures(
            days=days,
            min_timing_score=min_score
        )
        
        if not suspicious_trades:
            click.echo(f"✅ No trades found with timing score >= {min_score}")
            return
        
        report = format_suspicious_trades_report(suspicious_trades)
        click.echo(report)
    
    # Save to file if requested
    if output:
        Path(output).write_text(report)
        click.echo(f"\n💾 Report saved to {output}")


def format_suspicious_trades_report(trades):
    """Format suspicious trades as text report."""
    lines = [
        "=" * 100,
        f"  SUSPICIOUS TRADING TIMING ANALYSIS",
        f"  {len(trades)} Trades with Timing Advantages Detected",
        "=" * 100,
        "",
    ]
    
    for i, trade in enumerate(trades, 1):
        # Determine alert level
        if trade.get("highly_suspicious"):
            alert_emoji = "🚨"
            level = "HIGHLY SUSPICIOUS"
        elif trade["suspicious"]:
            alert_emoji = "🔴"
            level = "SUSPICIOUS"
        else:
            alert_emoji = "🟡"
            level = "NOTABLE"
        
        lines.extend([
            "─" * 100,
            f"{alert_emoji} #{i} - {level} (Timing Score: {trade['timing_score']}/100)",
            "─" * 100,
            f"Official:    {trade['official_name']}",
            f"Ticker:      {trade['ticker']}",
            f"Side:        {trade['side'].upper()}",
            f"Trade Date:  {trade['transaction_date']}",
            f"Filed Date:  {trade['filing_date']}",
            f"Value:       {trade['value_range']}",
            "",
            f"📊 Timing Analysis:",
            f"   Prior Alerts:        {trade['alert_count']}",
            f"   Recent Alerts (7d):  {trade['recent_alert_count']}",
            f"   High Severity:       {trade['high_severity_count']}",
            f"   Avg Severity:        {trade['avg_severity']}/10",
            "",
            f"💡 Assessment: {trade['reason']}",
            "",
        ])
        
        if trade['prior_alerts']:
            lines.append("🔔 Prior Market Alerts:")
            alert_table = []
            for alert in trade['prior_alerts'][:5]:  # Top 5
                alert_table.append([
                    alert['timestamp'],
                    alert['alert_type'].replace('_', ' ').title(),
                    f"{alert['severity']}/10",
                    f"{alert['days_before_trade']} days before",
                ])
            
            lines.append(tabulate(
                alert_table,
                headers=["Timestamp", "Type", "Severity", "Timing"],
                tablefmt="simple"
            ))
            lines.append("")
    
    lines.extend([
        "=" * 100,
        "📈 SUMMARY",
        "=" * 100,
        f"Total Suspicious Trades:      {len(trades)}",
        f"Highly Suspicious:            {sum(1 for t in trades if t.get('highly_suspicious'))}",
        f"Average Timing Score:         {sum(t['timing_score'] for t in trades) / len(trades):.2f}/100",
        "",
        "⚠️  IMPORTANT:",
        "   This analysis is for research and transparency purposes only.",
        "   High timing scores suggest potential issues but are not definitive proof.",
        "   Further investigation may be warranted for highly suspicious patterns.",
        "",
        "=" * 100,
    ])
    
    return "\n".join(lines)


def format_official_report(result):
    """Format official timing pattern report."""
    lines = [
        "=" * 80,
        f"  OFFICIAL TIMING PATTERN ANALYSIS",
        "=" * 80,
        "",
        f"Trade Count:            {result['trade_count']}",
        f"With Prior Alerts:      {result['trades_with_prior_alerts']} ({result['trades_with_prior_alerts']/result['trade_count']*100:.1f}%)" if result['trade_count'] > 0 else "",
        f"Suspicious Trades:      {result['suspicious_trade_count']}",
        f"Highly Suspicious:      {result['highly_suspicious_count']}",
        f"Average Timing Score:   {result['avg_timing_score']}/100",
        "",
        f"📊 Pattern: {result['pattern']}",
        "",
    ]
    
    if result.get('analyses'):
        # Show top suspicious trades
        suspicious = [a for a in result['analyses'] if a['suspicious']]
        if suspicious:
            lines.append("🚨 Most Suspicious Trades:")
            for trade in suspicious[:5]:
                lines.append(
                    f"   {trade['ticker']:6s} {trade['side']:4s} on {trade['transaction_date']} "
                    f"(Score: {trade['timing_score']:.0f}/100, {trade['alert_count']} alerts)"
                )
            lines.append("")
    
    lines.append("=" * 80)
    return "\n".join(lines)


def format_ticker_report(result):
    """Format ticker timing analysis report."""
    lines = [
        "=" * 80,
        f"  TICKER TIMING ANALYSIS: {result['ticker']}",
        "=" * 80,
        "",
        f"Total Trades:           {result['trade_count']}",
        f"With Prior Alerts:      {result['trades_with_alerts']}",
        f"Suspicious Count:       {result['suspicious_count']}",
        f"Average Timing Score:   {result['avg_timing_score']}/100",
        "",
    ]
    
    if result.get('analyses'):
        lines.append("📊 Recent Trades:")
        for trade in result['analyses'][:10]:
            emoji = "🚨" if trade.get('highly_suspicious') else "🔴" if trade['suspicious'] else "🟡" if trade['alert_count'] > 0 else "✅"
            lines.append(
                f"   {emoji} {trade['official_name']:25s} {trade['side']:4s} on {trade['transaction_date']} "
                f"(Score: {trade['timing_score']:.0f}/100)"
            )
        lines.append("")
    
    lines.append("=" * 80)
    return "\n".join(lines)


if __name__ == "__main__":
    main()