"""
Earnings Analyzer Agent

Analyzes earnings reports, estimates, and surprises.
"""

from typing import Any, Dict, List
from agents.base import BaseAgent
from datetime import datetime, timedelta


class EarningsAnalyzerAgent(BaseAgent):
    """Analyzes earnings reports and company financial performance."""

    def __init__(self):
        super().__init__(
            name='earnings-analyzer',
            description='Analyze earnings reports, estimates, and surprises',
            category='finance',
            version='1.0.0',
            tags=['earnings', 'eps', 'revenue', 'analysis', 'estimates']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Analyze earnings.

        Args:
            params: {
                'symbol': 'AAPL',
                'analysis_type': 'latest|historical|forecast',
                'include_guidance': True,
                'compare_to_estimates': True
            }

        Returns:
            {
                'status': 'success|failed',
                'earnings_data': {...},
                'analysis': {...},
                'signals': [...]
            }
        """
        symbol = params.get('symbol')
        analysis_type = params.get('analysis_type', 'latest')
        include_guidance = params.get('include_guidance', True)
        compare_estimates = params.get('compare_to_estimates', True)

        self.logger.info(f"Analyzing earnings for {symbol}")

        # Get earnings data
        if analysis_type == 'latest':
            earnings = self._get_latest_earnings(symbol)
        elif analysis_type == 'historical':
            earnings = self._get_historical_earnings(symbol)
        else:  # forecast
            earnings = self._get_earnings_forecast(symbol)

        # Analyze earnings
        analysis = self._analyze_earnings(earnings, compare_estimates)

        # Generate trading signals
        signals = self._generate_earnings_signals(analysis)

        # Get guidance if requested
        guidance = None
        if include_guidance:
            guidance = self._get_company_guidance(symbol)

        return {
            'status': 'success',
            'symbol': symbol,
            'analysis_type': analysis_type,
            'earnings_data': earnings,
            'analysis': analysis,
            'guidance': guidance,
            'signals': signals,
            'timestamp': datetime.utcnow().isoformat() + 'Z'
        }

    def _get_latest_earnings(self, symbol: str) -> Dict:
        """Get latest earnings report."""
        return {
            'symbol': symbol,
            'fiscal_quarter': 'Q4 2024',
            'report_date': (datetime.utcnow() - timedelta(days=7)).strftime('%Y-%m-%d'),
            'actual_eps': 1.85,
            'estimated_eps': 1.78,
            'actual_revenue': 119.6e9,
            'estimated_revenue': 117.9e9,
            'earnings_time': 'after_market',
            'conference_call': '2025-01-28 16:30:00'
        }

    def _get_historical_earnings(self, symbol: str) -> List[Dict]:
        """Get historical earnings data."""
        quarters = []

        for i in range(8):
            quarter_date = datetime.utcnow() - timedelta(days=90 * i)
            quarters.append({
                'fiscal_quarter': f'Q{4 - (i % 4)} {quarter_date.year - (i // 4)}',
                'report_date': quarter_date.strftime('%Y-%m-%d'),
                'actual_eps': round(1.75 + (i % 3) * 0.1, 2),
                'estimated_eps': round(1.70 + (i % 3) * 0.1, 2),
                'actual_revenue': round(115e9 + (i * 2e9), 0),
                'estimated_revenue': round(114e9 + (i * 2e9), 0),
                'eps_surprise_pct': round(((1.75 - 1.70) / 1.70) * 100, 2),
                'revenue_surprise_pct': round(((115e9 - 114e9) / 114e9) * 100, 2)
            })

        return quarters

    def _get_earnings_forecast(self, symbol: str) -> Dict:
        """Get earnings forecast."""
        next_report = datetime.utcnow() + timedelta(days=30)

        return {
            'symbol': symbol,
            'next_earnings_date': next_report.strftime('%Y-%m-%d'),
            'estimated_report_time': 'after_market',
            'fiscal_quarter': 'Q1 2025',
            'consensus_eps_estimate': 1.92,
            'eps_estimate_high': 2.05,
            'eps_estimate_low': 1.80,
            'num_analysts': 42,
            'consensus_revenue_estimate': 122.5e9,
            'revenue_estimate_high': 125.0e9,
            'revenue_estimate_low': 120.0e9,
            'earnings_growth_estimate': 0.08
        }

    def _analyze_earnings(self, earnings: Dict, compare_estimates: bool) -> Dict:
        """Analyze earnings data."""
        analysis = {}

        if isinstance(earnings, list):
            # Historical analysis
            analysis = self._analyze_historical_trend(earnings)
        elif 'actual_eps' in earnings:
            # Latest earnings analysis
            eps_surprise = earnings['actual_eps'] - earnings['estimated_eps']
            eps_surprise_pct = (eps_surprise / earnings['estimated_eps'] * 100) if earnings['estimated_eps'] != 0 else 0

            revenue_surprise = earnings['actual_revenue'] - earnings['estimated_revenue']
            revenue_surprise_pct = (revenue_surprise / earnings['estimated_revenue'] * 100) if earnings['estimated_revenue'] != 0 else 0

            analysis = {
                'eps_beat': earnings['actual_eps'] > earnings['estimated_eps'],
                'eps_surprise': round(eps_surprise, 2),
                'eps_surprise_pct': round(eps_surprise_pct, 2),
                'revenue_beat': earnings['actual_revenue'] > earnings['estimated_revenue'],
                'revenue_surprise': round(revenue_surprise / 1e9, 2),
                'revenue_surprise_pct': round(revenue_surprise_pct, 2),
                'quality': self._assess_earnings_quality(eps_surprise_pct, revenue_surprise_pct)
            }
        else:
            # Forecast analysis
            eps_range = earnings['eps_estimate_high'] - earnings['eps_estimate_low']
            estimate_uncertainty = (eps_range / earnings['consensus_eps_estimate'] * 100) if earnings['consensus_eps_estimate'] != 0 else 0

            analysis = {
                'estimate_consensus': earnings['consensus_eps_estimate'],
                'estimate_range': round(eps_range, 2),
                'estimate_uncertainty_pct': round(estimate_uncertainty, 2),
                'analyst_coverage': earnings['num_analysts'],
                'expected_growth': round(earnings['earnings_growth_estimate'] * 100, 2)
            }

        return analysis

    def _analyze_historical_trend(self, quarters: List[Dict]) -> Dict:
        """Analyze historical earnings trend."""
        eps_values = [q['actual_eps'] for q in quarters]
        revenue_values = [q['actual_revenue'] for q in quarters]

        # Calculate growth rates
        eps_growth = ((eps_values[0] - eps_values[-1]) / eps_values[-1] * 100) if eps_values[-1] != 0 else 0
        revenue_growth = ((revenue_values[0] - revenue_values[-1]) / revenue_values[-1] * 100) if revenue_values[-1] != 0 else 0

        # Calculate consistency (beat rate)
        beats = len([q for q in quarters if q.get('actual_eps', 0) > q.get('estimated_eps', 0)])
        beat_rate = (beats / len(quarters) * 100) if quarters else 0

        return {
            'quarters_analyzed': len(quarters),
            'eps_growth_pct': round(eps_growth, 2),
            'revenue_growth_pct': round(revenue_growth, 2),
            'beat_rate_pct': round(beat_rate, 2),
            'trend': 'improving' if eps_growth > 0 else 'declining',
            'consistency': 'high' if beat_rate > 75 else 'moderate' if beat_rate > 50 else 'low'
        }

    def _assess_earnings_quality(self, eps_surprise: float, revenue_surprise: float) -> str:
        """Assess quality of earnings beat/miss."""
        if eps_surprise > 5 and revenue_surprise > 2:
            return 'excellent_beat'
        elif eps_surprise > 0 and revenue_surprise > 0:
            return 'solid_beat'
        elif eps_surprise > 0 and revenue_surprise < 0:
            return 'mixed_eps_beat'
        elif eps_surprise < 0 and revenue_surprise > 0:
            return 'mixed_revenue_beat'
        elif eps_surprise < -5 and revenue_surprise < -2:
            return 'significant_miss'
        else:
            return 'modest_miss'

    def _get_company_guidance(self, symbol: str) -> Dict:
        """Get company guidance."""
        return {
            'next_quarter_guidance': {
                'eps_guidance': {'low': 1.88, 'high': 1.98},
                'revenue_guidance': {'low': 120e9, 'high': 125e9}
            },
            'full_year_guidance': {
                'eps_guidance': {'low': 7.50, 'high': 7.80},
                'revenue_guidance': {'low': 480e9, 'high': 500e9}
            },
            'guidance_quality': 'raised',  # raised|maintained|lowered
            'management_tone': 'optimistic'
        }

    def _generate_earnings_signals(self, analysis: Dict) -> List[Dict]:
        """Generate trading signals from earnings analysis."""
        signals = []

        # From latest earnings
        if 'quality' in analysis:
            quality = analysis['quality']

            if quality == 'excellent_beat':
                signals.append({
                    'signal': 'strong_buy',
                    'reason': 'Excellent earnings beat on both EPS and revenue',
                    'confidence': 0.85
                })
            elif quality == 'solid_beat':
                signals.append({
                    'signal': 'buy',
                    'reason': 'Solid earnings beat',
                    'confidence': 0.75
                })
            elif quality == 'significant_miss':
                signals.append({
                    'signal': 'sell',
                    'reason': 'Significant earnings miss',
                    'confidence': 0.80
                })

        # From historical trend
        if 'trend' in analysis:
            if analysis['trend'] == 'improving' and analysis.get('beat_rate_pct', 0) > 75:
                signals.append({
                    'signal': 'buy',
                    'reason': 'Consistent earnings growth with high beat rate',
                    'confidence': 0.70
                })

        return signals

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate earnings analysis parameters."""
        if 'symbol' not in params:
            self.logger.error("Missing required field: symbol")
            return False

        return True
