"""
Trend Analyzer Agent

Analyzes trends and patterns in time series and sequential data
using statistical methods and machine learning.
"""

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


class TrendAnalyzerAgent(BaseAgent):
    """
    Analyzes trends and patterns in data.

    Supports:
    - Time series trend analysis
    - Seasonal pattern detection
    - Cyclical pattern identification
    - Trend decomposition
    - Change point detection
    - Multiple smoothing methods
    """

    def __init__(self):
        super().__init__(
            name='trend-analyzer',
            description='Analyze trends and patterns in data',
            category='data',
            version='1.0.0',
            tags=['trends', 'time-series', 'patterns', 'forecasting']
        )

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

        Args:
            params: {
                'data_source': str,
                'metric': str,
                'time_column': str,
                'time_period': {
                    'start_date': str,
                    'end_date': str,
                    'granularity': 'hourly|daily|weekly|monthly|quarterly|yearly'
                },
                'methods': List[str],  # ['moving_average', 'exponential_smoothing', 'decomposition']
                'options': {
                    'detect_seasonality': bool,
                    'detect_cycles': bool,
                    'detect_change_points': bool,
                    'window_size': int,
                    'confidence_level': float
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'metric': str,
                'trend_direction': 'increasing|decreasing|stable|fluctuating',
                'trend_strength': float,
                'seasonality_detected': bool,
                'seasonal_period': int,
                'cycles_detected': int,
                'change_points': List[Dict[str, Any]],
                'statistics': Dict[str, Any],
                'execution_time_seconds': float,
                'visualizations': List[str],
                'insights': List[str],
                'predictions': Dict[str, Any]
            }
        """
        data_source = params.get('data_source')
        metric = params.get('metric')
        time_period = params.get('time_period', {})
        methods = params.get('methods', ['moving_average'])
        options = params.get('options', {})

        self.logger.info(
            f"Analyzing trends for metric '{metric}' in '{data_source}'"
        )

        # Mock trend analysis
        return {
            'status': 'success',
            'data_source': data_source,
            'metric': metric,
            'time_column': params.get('time_column', 'date'),
            'time_period': {
                'start_date': time_period.get('start_date', '2025-01-01'),
                'end_date': time_period.get('end_date', '2025-11-16'),
                'granularity': time_period.get('granularity', 'daily'),
                'total_periods': 320
            },
            'trend_direction': 'increasing',
            'trend_strength': 0.78,
            'trend_percentage': 23.5,
            'average_growth_rate': 0.73,
            'execution_time_seconds': 4.1,
            'methods_applied': methods,
            'seasonality_detected': True,
            'seasonal_period': 7,  # Weekly seasonality
            'seasonal_strength': 0.65,
            'seasonal_pattern': 'Weekly cycle with peaks on weekends',
            'cycles_detected': 2,
            'cycle_periods': [30, 90],  # Monthly and quarterly cycles
            'change_points': [
                {
                    'date': '2025-03-15',
                    'type': 'significant_increase',
                    'magnitude': 0.35,
                    'confidence': 0.92,
                    'description': 'Sharp upward trend change detected'
                },
                {
                    'date': '2025-08-01',
                    'type': 'acceleration',
                    'magnitude': 0.18,
                    'confidence': 0.85,
                    'description': 'Growth rate acceleration observed'
                }
            ] if options.get('detect_change_points') else [],
            'statistics': {
                'mean': 5240.5,
                'median': 5180.0,
                'std_dev': 842.3,
                'min': 3200,
                'max': 7800,
                'coefficient_of_variation': 0.16,
                'autocorrelation': 0.82,
                'stationarity': 'non-stationary'
            },
            'decomposition': {
                'trend_component': 'increasing_linear',
                'seasonal_component': 'weekly_pattern',
                'residual_variance': 0.12,
                'explained_variance': 0.88
            },
            'moving_averages': {
                '7_day_ma': 5240.5,
                '30_day_ma': 5180.2,
                '90_day_ma': 4950.8
            },
            'volatility': {
                'daily_volatility': 0.15,
                'weekly_volatility': 0.08,
                'monthly_volatility': 0.05
            },
            'visualizations': [
                'trend_line_chart.png',
                'seasonal_decomposition.png',
                'moving_averages.png',
                'change_points.png'
            ],
            'insights': [
                'Strong upward trend observed over the period (+23.5%)',
                'Weekly seasonality with consistent weekend peaks',
                'Two major change points detected in March and August',
                'Growth rate accelerating in recent months',
                'Low volatility indicates stable, predictable growth'
            ],
            'predictions': {
                'next_7_days_avg': 7650,
                'next_30_days_avg': 7820,
                'confidence_interval_95': [7200, 8400]
            },
            'recommendations': [
                'Continue monitoring weekend performance drivers',
                'Investigate factors behind March growth acceleration',
                'Capitalize on upward trend with strategic investments',
                'Monitor for potential trend reversal signals',
                'Consider seasonal adjustments in planning'
            ]
        }

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

        if 'metric' not in params:
            self.logger.error("Missing required field: metric")
            return False

        return True
