#!/usr/bin/env python3
"""
Institutional Flow Tracker - Single Stock Deep Dive

Provides detailed analysis of institutional ownership for a specific stock,
including historical trends, top holders, and position changes.

Usage:
    python3 analyze_single_stock.py AAPL
    python3 analyze_single_stock.py MSFT --quarters 12 --api-key YOUR_KEY
    python3 analyze_single_stock.py TSLA --compare-to GM

Requirements:
    - FMP API key (set FMP_API_KEY environment variable or pass --api-key)
"""

import argparse
import json
import os
import sys
from datetime import datetime
from typing import List, Dict, Optional
from collections import defaultdict

try:
    import requests
except ImportError:
    print("Error: 'requests' library not installed. Install with: pip install requests")
    sys.exit(1)


class SingleStockAnalyzer:
    """Analyze institutional ownership for a single stock"""

    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://financialmodelingprep.com/api/v3"

    def get_institutional_holders(self, symbol: str) -> List[Dict]:
        """Get all institutional holders data for a stock"""
        url = f"{self.base_url}/institutional-holder/{symbol}"
        params = {"apikey": self.api_key}

        try:
            response = requests.get(url, params=params, timeout=30)
            response.raise_for_status()
            data = response.json()
            return data if isinstance(data, list) else []
        except requests.exceptions.RequestException as e:
            print(f"Error fetching institutional holders for {symbol}: {e}")
            return []

    def get_company_profile(self, symbol: str) -> Dict:
        """Get company profile information"""
        url = f"{self.base_url}/profile/{symbol}"
        params = {"apikey": self.api_key}

        try:
            response = requests.get(url, params=params, timeout=30)
            response.raise_for_status()
            data = response.json()
            return data[0] if isinstance(data, list) and data else {}
        except requests.exceptions.RequestException as e:
            print(f"Error fetching company profile for {symbol}: {e}")
            return {}

    def analyze_stock(self, symbol: str, quarters: int = 8) -> Dict:
        """Perform comprehensive institutional analysis on a stock"""

        print(f"Analyzing institutional ownership for {symbol}...")

        # Get company profile
        profile = self.get_company_profile(symbol)
        company_name = profile.get('companyName', symbol)
        sector = profile.get('sector', 'Unknown')
        market_cap = profile.get('mktCap', 0)

        print(f"Company: {company_name}")
        print(f"Sector: {sector}")
        print(f"Market Cap: ${market_cap:,}")

        # Get institutional holders
        holders = self.get_institutional_holders(symbol)

        if not holders:
            print(f"No institutional holder data available for {symbol}")
            return {}

        # Group by quarter
        quarters_data = defaultdict(list)
        for holder in holders:
            date = holder.get('dateReported', '')
            if date:
                quarters_data[date].append(holder)

        # Get most recent N quarters
        sorted_quarters = sorted(quarters_data.keys(), reverse=True)[:quarters]

        if len(sorted_quarters) < 2:
            print(f"Insufficient data (need at least 2 quarters, found {len(sorted_quarters)})")
            return {}

        # Calculate quarterly metrics
        quarterly_metrics = []
        for q in sorted_quarters:
            holders_q = quarters_data[q]
            total_shares = sum(h.get('totalShares', 0) for h in holders_q)
            total_value = sum(h.get('totalInvested', 0) for h in holders_q)
            num_holders = len(holders_q)

            quarterly_metrics.append({
                'quarter': q,
                'total_shares': total_shares,
                'total_value': total_value,
                'num_holders': num_holders,
                'top_holders': sorted(holders_q, key=lambda x: x.get('totalShares', 0), reverse=True)[:20]
            })

        # Calculate trends
        most_recent = quarterly_metrics[0]
        oldest = quarterly_metrics[-1]

        shares_trend = ((most_recent['total_shares'] - oldest['total_shares']) / oldest['total_shares'] * 100) if oldest['total_shares'] > 0 else 0
        holders_trend = most_recent['num_holders'] - oldest['num_holders']

        # Analyze position changes (recent quarter vs previous)
        if len(quarterly_metrics) >= 2:
            current_q = quarterly_metrics[0]
            previous_q = quarterly_metrics[1]

            # Create holder dictionaries for comparison
            current_holders_map = {h.get('holder', ''): h for h in current_q['top_holders']}
            previous_holders_map = {h.get('holder', ''): h for h in previous_q['top_holders']}

            # Categorize changes
            new_positions = []
            increased_positions = []
            decreased_positions = []
            closed_positions = []

            # Check current holders
            for name, holder in current_holders_map.items():
                current_shares = holder.get('totalShares', 0)
                if name in previous_holders_map:
                    previous_shares = previous_holders_map[name].get('totalShares', 0)
                    change = current_shares - previous_shares
                    pct_change = (change / previous_shares * 100) if previous_shares > 0 else 0

                    if change > 0:
                        increased_positions.append({
                            'name': name,
                            'current_shares': current_shares,
                            'change': change,
                            'pct_change': pct_change
                        })
                    elif change < 0:
                        decreased_positions.append({
                            'name': name,
                            'current_shares': current_shares,
                            'change': change,
                            'pct_change': pct_change
                        })
                else:
                    new_positions.append({
                        'name': name,
                        'shares': current_shares
                    })

            # Check for closed positions
            for name, holder in previous_holders_map.items():
                if name not in current_holders_map:
                    closed_positions.append({
                        'name': name,
                        'previous_shares': holder.get('totalShares', 0)
                    })

            # Sort by magnitude
            increased_positions.sort(key=lambda x: x['change'], reverse=True)
            decreased_positions.sort(key=lambda x: x['change'])

        else:
            new_positions = []
            increased_positions = []
            decreased_positions = []
            closed_positions = []

        return {
            'symbol': symbol,
            'company_name': company_name,
            'sector': sector,
            'market_cap': market_cap,
            'quarterly_metrics': quarterly_metrics,
            'shares_trend': shares_trend,
            'holders_trend': holders_trend,
            'new_positions': new_positions,
            'increased_positions': increased_positions,
            'decreased_positions': decreased_positions,
            'closed_positions': closed_positions
        }

    def generate_report(self, analysis: Dict, output_file: Optional[str] = None):
        """Generate detailed markdown report"""

        if not analysis:
            print("No analysis data available")
            return

        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        symbol = analysis['symbol']

        report = f"""# Institutional Ownership Analysis: {symbol}

**Company:** {analysis['company_name']}
**Sector:** {analysis['sector']}
**Market Cap:** ${analysis['market_cap']:,}
**Analysis Date:** {timestamp}

## Executive Summary

"""

        # Determine overall signal
        shares_trend = analysis['shares_trend']
        holders_trend = analysis['holders_trend']

        if shares_trend > 15 and holders_trend > 5:
            signal = "🟢 **STRONG ACCUMULATION**"
            interpretation = "Strong institutional buying with increasing participation. Positive signal."
        elif shares_trend > 7 and holders_trend > 0:
            signal = "🟢 **MODERATE ACCUMULATION**"
            interpretation = "Steady institutional buying. Moderately positive signal."
        elif shares_trend < -15 or holders_trend < -5:
            signal = "🔴 **STRONG DISTRIBUTION**"
            interpretation = "Significant institutional selling. Warning sign - investigate further."
        elif shares_trend < -7:
            signal = "🔴 **MODERATE DISTRIBUTION**"
            interpretation = "Institutional selling detected. Monitor closely."
        else:
            signal = "⚪ **NEUTRAL**"
            interpretation = "No significant institutional flow changes. Stable ownership."

        report += f"""**Signal:** {signal}

**Interpretation:** {interpretation}

**Trend ({len(analysis['quarterly_metrics'])} Quarters):**
- Institutional Shares: {shares_trend:+.2f}%
- Number of Institutions: {holders_trend:+d}

## Historical Institutional Ownership Trend

| Quarter | Total Shares Held | Total Value | Number of Institutions | QoQ Change |
|---------|-------------------|-------------|----------------------|------------|
"""

        # Add quarterly data
        metrics = analysis['quarterly_metrics']
        for i, q in enumerate(metrics):
            if i < len(metrics) - 1:
                prev_shares = metrics[i+1]['total_shares']
                qoq_change = ((q['total_shares'] - prev_shares) / prev_shares * 100) if prev_shares > 0 else 0
                qoq_str = f"{qoq_change:+.2f}%"
            else:
                qoq_str = "N/A"

            report += f"| {q['quarter']} | {q['total_shares']:,} | ${q['total_value']:,} | {q['num_holders']} | {qoq_str} |\n"

        # Recent changes
        report += f"""
## Recent Quarter Changes ({metrics[0]['quarter']} vs {metrics[1]['quarter']})

### New Positions (Institutions that newly initiated)

"""
        if analysis['new_positions']:
            report += "| Institution | Shares Acquired |\n"
            report += "|-------------|----------------|\n"
            for pos in analysis['new_positions'][:10]:
                report += f"| {pos['name']} | {pos['shares']:,} |\n"
        else:
            report += "No new institutional positions detected.\n"

        report += "\n### Increased Positions (Top 10)\n\n"
        if analysis['increased_positions']:
            report += "| Institution | Current Shares | Change | % Change |\n"
            report += "|-------------|----------------|--------|----------|\n"
            for pos in analysis['increased_positions'][:10]:
                report += f"| {pos['name']} | {pos['current_shares']:,} | {pos['change']:+,} | {pos['pct_change']:+.2f}% |\n"
        else:
            report += "No significant position increases detected.\n"

        report += "\n### Decreased Positions (Top 10)\n\n"
        if analysis['decreased_positions']:
            report += "| Institution | Current Shares | Change | % Change |\n"
            report += "|-------------|----------------|--------|----------|\n"
            for pos in analysis['decreased_positions'][:10]:
                report += f"| {pos['name']} | {pos['current_shares']:,} | {pos['change']:,} | {pos['pct_change']:.2f}% |\n"
        else:
            report += "No significant position decreases detected.\n"

        report += "\n### Closed Positions (Institutions that exited)\n\n"
        if analysis['closed_positions']:
            report += "| Institution | Previous Shares |\n"
            report += "|-------------|-----------------|\n"
            for pos in analysis['closed_positions'][:10]:
                report += f"| {pos['name']} | {pos['previous_shares']:,} |\n"
        else:
            report += "No institutional exits detected.\n"

        # Top current holders
        report += f"\n## Top 20 Current Institutional Holders ({metrics[0]['quarter']})\n\n"
        report += "| Rank | Institution | Shares Held | % of Institutional | Latest Change |\n"
        report += "|------|-------------|-------------|-------------------|---------------|\n"

        total_inst_shares = metrics[0]['total_shares']
        for i, holder in enumerate(metrics[0]['top_holders'], 1):
            shares = holder.get('totalShares', 0)
            pct_of_inst = (shares / total_inst_shares * 100) if total_inst_shares > 0 else 0
            change = holder.get('change', 0)
            report += f"| {i} | {holder.get('holder', 'Unknown')} | {shares:,} | {pct_of_inst:.2f}% | {change:+,} |\n"

        # Concentration analysis
        if len(metrics[0]['top_holders']) >= 10:
            top_10_shares = sum(h.get('totalShares', 0) for h in metrics[0]['top_holders'][:10])
            concentration = (top_10_shares / total_inst_shares * 100) if total_inst_shares > 0 else 0

            report += f"""
## Concentration Analysis

**Top 10 Holders Concentration:** {concentration:.2f}%

**Interpretation:**
"""
            if concentration > 60:
                report += "- **High Concentration** - Top 10 institutions control majority of institutional ownership\n"
                report += "- **Risk:** Significant price impact if top holders sell\n"
                report += "- **Opportunity:** May indicate high conviction by quality investors\n"
            elif concentration > 40:
                report += "- **Moderate Concentration** - Balanced institutional ownership\n"
                report += "- **Risk:** Moderate concentration risk\n"
            else:
                report += "- **Low Concentration** - Widely distributed institutional ownership\n"
                report += "- **Risk:** Lower concentration risk, more stable ownership\n"

        report += """
## Interpretation Guide

**For detailed interpretation framework, see:**
`institutional-flow-tracker/references/interpretation_framework.md`

**Next Steps:**
1. Validate institutional signal with fundamental analysis
2. Check technical setup for entry timing
3. Review sector-wide institutional trends
4. Monitor quarterly for trend continuation/reversal

---

**Data Source:** FMP API (13F SEC Filings)
**Data Lag:** ~45 days after quarter end
**Note:** Use as confirming indicator alongside fundamental and technical analysis
"""

        # Save report
        if output_file:
            output_path = output_file if output_file.endswith('.md') else f"{output_file}.md"
        else:
            output_path = f"institutional_analysis_{symbol}_{datetime.now().strftime('%Y%m%d')}.md"

        with open(output_path, 'w') as f:
            f.write(report)

        print(f"\n✅ Report saved to: {output_path}")
        return report


def main():
    parser = argparse.ArgumentParser(
        description='Analyze institutional ownership for a specific stock',
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  # Basic analysis
  python3 analyze_single_stock.py AAPL

  # Extended history (12 quarters)
  python3 analyze_single_stock.py MSFT --quarters 12

  # With custom output
  python3 analyze_single_stock.py TSLA --output tesla_analysis.md
        """
    )

    parser.add_argument(
        'symbol',
        type=str,
        help='Stock ticker symbol to analyze'
    )
    parser.add_argument(
        '--api-key',
        type=str,
        default=os.getenv('FMP_API_KEY'),
        help='FMP API key (or set FMP_API_KEY environment variable)'
    )
    parser.add_argument(
        '--quarters',
        type=int,
        default=8,
        help='Number of quarters to analyze (default: 8, i.e., 2 years)'
    )
    parser.add_argument(
        '--output',
        type=str,
        help='Output file path for markdown report'
    )
    parser.add_argument(
        '--compare-to',
        type=str,
        help='Compare to another stock (optional, future feature)'
    )

    args = parser.parse_args()

    # Validate API key
    if not args.api_key:
        print("Error: FMP API key required")
        print("Set FMP_API_KEY environment variable or pass --api-key argument")
        print("Get free API key at: https://financialmodelingprep.com/developer/docs")
        sys.exit(1)

    # Initialize analyzer
    analyzer = SingleStockAnalyzer(args.api_key)

    # Run analysis
    analysis = analyzer.analyze_stock(args.symbol.upper(), quarters=args.quarters)

    if not analysis:
        print(f"Unable to complete analysis for {args.symbol}")
        sys.exit(1)

    # Generate report
    analyzer.generate_report(analysis, output_file=args.output)

    # Print summary
    print("\n" + "="*80)
    print(f"INSTITUTIONAL OWNERSHIP SUMMARY: {args.symbol}")
    print("="*80)
    print(f"Trend ({args.quarters} quarters): {analysis['shares_trend']:+.2f}% shares, {analysis['holders_trend']:+d} institutions")
    print(f"Recent Activity:")
    print(f"  - New Positions: {len(analysis['new_positions'])}")
    print(f"  - Increased: {len(analysis['increased_positions'])}")
    print(f"  - Decreased: {len(analysis['decreased_positions'])}")
    print(f"  - Exited: {len(analysis['closed_positions'])}")


if __name__ == '__main__':
    main()