#!/usr/bin/env python
"""
Generate comprehensive pattern analysis report.
Identifies repeat offenders and systematic suspicious behavior.
"""

import click
from pathlib import Path
from tabulate import tabulate

from pote.db import get_session
from pote.monitoring.pattern_detector import PatternDetector


@click.command()
@click.option("--days", default=365, help="Analyze last N days (default: 365)")
@click.option("--output", help="Save report to file")
@click.option("--format", type=click.Choice(["text", "json"]), default="text")
def main(days, output, format):
    """Generate comprehensive pattern analysis report."""
    
    session = next(get_session())
    detector = PatternDetector(session)
    
    click.echo(f"\n🔍 Generating pattern analysis for last {days} days...\n")
    
    report_data = detector.generate_pattern_report(lookback_days=days)
    
    if format == "json":
        import json
        report = json.dumps(report_data, indent=2, default=str)
    else:
        report = format_pattern_report(report_data)
    
    click.echo(report)
    
    if output:
        Path(output).write_text(report)
        click.echo(f"\n💾 Report saved to {output}")


def format_pattern_report(data):
    """Format pattern data as text report."""
    lines = [
        "=" * 100,
        "  CONGRESSIONAL TRADING PATTERN ANALYSIS",
        f"  Period: {data['period_days']} days",
        "=" * 100,
        "",
        "📊 SUMMARY",
        "─" * 100,
        f"Officials Analyzed:        {data['summary']['total_officials_analyzed']}",
        f"Repeat Offenders:          {data['summary']['repeat_offenders']}",
        f"Average Timing Score:      {data['summary']['avg_timing_score']}/100",
        "",
    ]
    
    # Top Suspicious Officials
    if data['top_suspicious_officials']:
        lines.extend([
            "",
            "🚨 TOP 10 MOST SUSPICIOUS OFFICIALS (By Timing Score)",
            "=" * 100,
            "",
        ])
        
        table_data = []
        for i, official in enumerate(data['top_suspicious_officials'][:10], 1):
            # Determine emoji based on severity
            if official['avg_timing_score'] >= 70:
                emoji = "🚨"
            elif official['avg_timing_score'] >= 50:
                emoji = "🔴"
            else:
                emoji = "🟡"
            
            table_data.append([
                f"{emoji} {i}",
                official['name'],
                f"{official['party'][:1]}-{official['state']}",
                official['chamber'],
                official['trade_count'],
                f"{official['suspicious_trades']}/{official['trade_count']}",
                f"{official['suspicious_rate']}%",
                f"{official['avg_timing_score']}/100",
            ])
        
        lines.append(tabulate(
            table_data,
            headers=["Rank", "Official", "Party-State", "Chamber", "Trades", "Suspicious", "Rate", "Avg Score"],
            tablefmt="simple"
        ))
        lines.append("")
    
    # Repeat Offenders
    if data['repeat_offenders']:
        lines.extend([
            "",
            "🔥 REPEAT OFFENDERS (50%+ Suspicious Trades)",
            "=" * 100,
            "",
        ])
        
        for offender in data['repeat_offenders']:
            lines.extend([
                f"🚨 {offender['name']} ({offender['party'][:1]}-{offender['state']}, {offender['chamber']})",
                f"   Trades: {offender['trade_count']} | Suspicious: {offender['suspicious_trades']} ({offender['suspicious_rate']}%)",
                f"   Avg Timing Score: {offender['avg_timing_score']}/100",
                f"   Pattern: {offender['pattern']}",
                "",
            ])
    
    # Suspicious Tickers
    if data['suspicious_tickers']:
        lines.extend([
            "",
            "📈 MOST SUSPICIOUSLY TRADED STOCKS",
            "=" * 100,
            "",
        ])
        
        table_data = []
        for ticker_data in data['suspicious_tickers'][:10]:
            table_data.append([
                ticker_data['ticker'],
                ticker_data['trade_count'],
                f"{ticker_data['trades_with_alerts']}/{ticker_data['trade_count']}",
                f"{ticker_data['suspicious_count']}/{ticker_data['trade_count']}",
                f"{ticker_data['suspicious_rate']}%",
                f"{ticker_data['avg_timing_score']}/100",
            ])
        
        lines.append(tabulate(
            table_data,
            headers=["Ticker", "Total Trades", "With Alerts", "Suspicious", "Susp. Rate", "Avg Score"],
            tablefmt="simple"
        ))
        lines.append("")
    
    # Sector Analysis
    if data['sector_analysis']:
        lines.extend([
            "",
            "🏭 SECTOR ANALYSIS",
            "=" * 100,
            "",
        ])
        
        # Sort sectors by suspicious rate
        sectors = sorted(
            data['sector_analysis'].items(),
            key=lambda x: x[1].get('suspicious_rate', 0),
            reverse=True
        )
        
        table_data = []
        for sector, stats in sectors[:10]:
            table_data.append([
                sector,
                stats['trade_count'],
                f"{stats['trades_with_alerts']}/{stats['trade_count']}",
                f"{stats['alert_rate']}%",
                f"{stats['suspicious_count']}/{stats['trade_count']}",
                f"{stats['suspicious_rate']}%",
                f"{stats['avg_timing_score']}/100",
            ])
        
        lines.append(tabulate(
            table_data,
            headers=["Sector", "Trades", "W/ Alerts", "Alert %", "Suspicious", "Susp %", "Avg Score"],
            tablefmt="simple"
        ))
        lines.append("")
    
    # Party Comparison
    if data['party_comparison']:
        lines.extend([
            "",
            "🏛️  PARTY COMPARISON",
            "=" * 100,
            "",
        ])
        
        table_data = []
        for party, stats in sorted(data['party_comparison'].items()):
            table_data.append([
                party,
                stats['official_count'],
                stats['total_trades'],
                f"{stats['total_suspicious']}/{stats['total_trades']}",
                f"{stats['suspicious_rate']}%",
                f"{stats['avg_timing_score']}/100",
            ])
        
        lines.append(tabulate(
            table_data,
            headers=["Party", "Officials", "Total Trades", "Suspicious", "Susp. Rate", "Avg Score"],
            tablefmt="simple"
        ))
        lines.append("")
    
    # Footer
    lines.extend([
        "",
        "=" * 100,
        "📋 INTERPRETATION GUIDE",
        "=" * 100,
        "",
        "Timing Score Ranges:",
        "  🚨 80-100: Highly suspicious - Strong evidence of timing advantage",
        "  🔴 60-79:  Suspicious - Likely timing advantage",
        "  🟡 40-59:  Notable - Some unusual activity",
        "  ✅ 0-39:   Normal - No significant pattern",
        "",
        "Suspicious Rate:",
        "  50%+ = Repeat offender pattern",
        "  25-50% = Concerning frequency",
        "  <25% = Within normal range",
        "",
        "⚠️  DISCLAIMER:",
        "  This analysis is for research and transparency purposes only.",
        "  High scores indicate statistical anomalies requiring further investigation.",
        "  This is not legal proof of wrongdoing.",
        "",
        "=" * 100,
    ])
    
    return "\n".join(lines)


if __name__ == "__main__":
    main()