Reporting and Analytics:

• Client reporting
• Risk reports
• Performance attribution reports
• Portfolio characteristics
• What-if analysis tools
• Real-time dashboards

Hence, Main ReportingAnalytics Class, Acts as the main interface for all reporting and analytics functionality, Integrates all other components into a single, cohesive system

Component Classes: ClientReporting: Handles generation and export of client reports

RiskAnalytics: Implements risk metrics including VaR and tracking error calculations

PerformanceAttribution: Manages performance attribution analysis and factor contribution

PortfolioCharacteristics: Calculates and compares portfolio metrics

WhatIfAnalysis: Enables scenario analysis and impact assessment

RealTimeDashboard: Provides real-time monitoring and metrics updates

here is the reporting_analytics.py

"""
Reporting and Analytics Module for Index Solutions
This module provides comprehensive reporting and analytics capabilities including:
- Client reporting
- Risk reports
- Performance attribution
- Portfolio characteristics
- What-if analysis
- Real-time dashboards
"""

import pandas as pd
import numpy as np
from datetime import datetime
from typing import Dict, List, Optional, Union
from risk_analytics import RiskAnalytics as BaseRiskAnalytics, MarketData, LiquidityMetrics

class ClientReporting:
    def __init__(self):
        self.report_data = {}

    def generate_client_report(self, portfolio_id: str, start_date: datetime, end_date: datetime) -> Dict:
        """Generate comprehensive client report"""
        # Placeholder for client report generation logic
        return {
            "portfolio_id": portfolio_id,
            "period": f"{start_date} to {end_date}",
            "summary": {},
            "details": {}
        }

    def export_report(self, format: str = "pdf"):
        """Export report in specified format"""
        pass

class RiskAnalytics(BaseRiskAnalytics):
    """Extended RiskAnalytics class that adds reporting capabilities"""
    
    def __init__(self, returns: pd.DataFrame, factor_returns: pd.DataFrame = None, benchmark_returns: pd.Series = None):
        super().__init__(returns, factor_returns, benchmark_returns)
        self.risk_reports = {}

    def generate_risk_report(self, portfolio_weights: Dict[str, float], positions: Dict[str, float]) -> Dict:
        """Generate comprehensive risk report"""
        report = {
            "timestamp": datetime.now(),
            "var": self.calculate_var(portfolio_weights),
            "cvar": self.calculate_cvar(portfolio_weights),
            "tracking_error": self.calculate_tracking_error(portfolio_weights),
            "liquidity_metrics": self.calculate_portfolio_liquidity(positions),
            "correlation_regimes": self.detect_correlation_regimes()
        }
        self.risk_reports[str(datetime.now())] = report
        return report

    def get_historical_risk_metrics(self, start_date: datetime = None, end_date: datetime = None) -> Dict:
        """Retrieve historical risk metrics"""
        filtered_reports = {
            timestamp: report
            for timestamp, report in self.risk_reports.items()
            if (not start_date or timestamp >= str(start_date)) and
               (not end_date or timestamp <= str(end_date))
        }
        return filtered_reports

class PerformanceAttribution:
    def __init__(self):
        self.attribution_data = {}

    def calculate_attribution(self, portfolio_data: pd.DataFrame, benchmark_data: pd.DataFrame) -> Dict:
        """Calculate performance attribution"""
        return {
            "allocation_effect": {},
            "selection_effect": {},
            "interaction_effect": {},
            "total_effect": {}
        }

    def analyze_factor_contribution(self, factors: List[str]) -> Dict:
        """Analyze contribution from different factors"""
        pass

class PortfolioCharacteristics:
    def __init__(self):
        self.characteristics = {}

    def calculate_metrics(self, portfolio_data: pd.DataFrame) -> Dict:
        """Calculate portfolio characteristics"""
        return {
            "market_cap": None,
            "pe_ratio": None,
            "dividend_yield": None,
            "sector_weights": {},
            "country_weights": {}
        }

    def compare_to_benchmark(self, benchmark_data: pd.DataFrame) -> Dict:
        """Compare portfolio characteristics to benchmark"""
        pass

class WhatIfAnalysis:
    def __init__(self):
        self.scenarios = {}

    def create_scenario(self, scenario_name: str, changes: Dict) -> None:
        """Create a new what-if scenario"""
        self.scenarios[scenario_name] = changes

    def analyze_scenario(self, scenario_name: str, portfolio_data: pd.DataFrame) -> Dict:
        """Analyze impact of a scenario"""
        pass

class RealTimeDashboard:
    def __init__(self):
        self.dashboard_data = {}
        self.update_frequency = 60  # seconds

    def update_dashboard(self) -> None:
        """Update dashboard with real-time data"""
        pass

    def get_dashboard_metrics(self) -> Dict:
        """Get current dashboard metrics"""
        return {
            "portfolio_value": None,
            "daily_return": None,
            "risk_metrics": {},
            "alerts": []
        }

class ReportingAnalytics:
    def __init__(self, returns: pd.DataFrame = None, factor_returns: pd.DataFrame = None, benchmark_returns: pd.Series = None):
        self.returns = returns
        self.factor_returns = factor_returns
        self.benchmark_returns = benchmark_returns
        
        self.client_reporting = ClientReporting()
        self.risk_analytics = RiskAnalytics(returns, factor_returns, benchmark_returns) if returns is not None else None
        self.performance_attribution = PerformanceAttribution()
        self.portfolio_characteristics = PortfolioCharacteristics()
        self.what_if_analysis = WhatIfAnalysis()
        self.dashboard = RealTimeDashboard()

    def initialize_risk_analytics(self, returns: pd.DataFrame, 
                                factor_returns: pd.DataFrame = None, 
                                benchmark_returns: pd.Series = None) -> None:
        """Initialize or update risk analytics with new data"""
        self.returns = returns
        self.factor_returns = factor_returns
        self.benchmark_returns = benchmark_returns
        self.risk_analytics = RiskAnalytics(returns, factor_returns, benchmark_returns)

    def generate_comprehensive_report(self, portfolio_id: str, 
                                   start_date: datetime, 
                                   end_date: datetime,
                                   include_sections: List[str] = None) -> Dict:
        """
        Generate a comprehensive report including all or specified analytics sections
        
        Args:
            portfolio_id: Unique identifier for the portfolio
            start_date: Start date for the analysis period
            end_date: End date for the analysis period
            include_sections: List of sections to include (None for all)
        
        Returns:
            Dictionary containing all requested analytics and reports
        """
        report = {
            "timestamp": datetime.now(),
            "portfolio_id": portfolio_id,
            "period": {
                "start": start_date,
                "end": end_date
            }
        }
        
        # Add requested or all sections
        if not include_sections or "client_report" in include_sections:
            report["client_report"] = self.client_reporting.generate_client_report(
                portfolio_id, start_date, end_date
            )
            
        # Add other sections as needed
        return report