Advanced Data Analytics for Financial Intelligence

# Confer's Data Analytics Engine Implementation

import pandas as pd
import numpy as np
from typing import Dict, List, Any, Optional
from datetime import datetime, timedelta
import asyncio
from dataclasses import dataclass

@dataclass
class AnalyticsQuery:
    metric: str
    dimensions: List[str]
    filters: Dict[str, Any]
    time_range: Dict[str, datetime]
    aggregation: str = "sum"

class FinancialAnalyticsEngine:
    """Advanced analytics engine for financial data processing and insights."""
    
    def __init__(self, config: Dict[str, Any]):
        self.data_pipeline = DataPipeline()
        self.ml_models = MLModelRegistry()
        self.visualization_engine = VisualizationEngine()
        self.insight_generator = InsightGenerator()
        
    async def process_real_time_data(self, data_stream: Any) -> Dict[str, Any]:
        """Process streaming financial data in real-time."""
        
        # Data ingestion and validation
        validated_data = await self.data_pipeline.validate_stream(data_stream)
        
        # Real-time transformations
        transformed_data = await self.data_pipeline.transform(validated_data)
        
        # Update metrics and KPIs
        metrics = await self._calculate_real_time_metrics(transformed_data)
        
        # Anomaly detection
        anomalies = await self.ml_models.detect_anomalies(transformed_data)
        
        # Generate alerts if needed
        alerts = await self._generate_alerts(anomalies, metrics)
        
        return {
            "timestamp": datetime.now().isoformat(),
            "metrics": metrics,
            "anomalies": anomalies,
            "alerts": alerts,
            "data_quality_score": await self._calculate_data_quality(validated_data)
        }
    
    async def generate_predictive_insights(
        self, 
        historical_data: pd.DataFrame,
        prediction_horizon: int = 30
    ) -> Dict[str, Any]:
        """Generate predictive analytics and forecasts."""
        
        # Feature engineering
        features = await self._engineer_features(historical_data)
        
        # Load and apply ML models
        loan_performance_model = await self.ml_models.get_model("loan_performance")
        market_trend_model = await self.ml_models.get_model("market_trends")
        risk_model = await self.ml_models.get_model("risk_assessment")
        
        # Generate predictions
        loan_predictions = await loan_performance_model.predict(
            features, horizon=prediction_horizon
        )
        
        market_predictions = await market_trend_model.predict(
            features, horizon=prediction_horizon
        )
        
        risk_predictions = await risk_model.predict(
            features, horizon=prediction_horizon
        )
        
        # Combine and analyze predictions
        insights = await self.insight_generator.analyze_predictions({
            "loan_performance": loan_predictions,
            "market_trends": market_predictions,
            "risk_assessment": risk_predictions
        })
        
        return {
            "predictions": {
                "loan_performance": loan_predictions.to_dict(),
                "market_trends": market_predictions.to_dict(),
                "risk_scores": risk_predictions.to_dict()
            },
            "insights": insights,
            "confidence_intervals": await self._calculate_confidence_intervals(
                loan_predictions, market_predictions, risk_predictions
            ),
            "recommendations": await self._generate_recommendations(insights)
        }
    
    async def create_interactive_dashboard(
        self, 
        user_id: str, 
        dashboard_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create personalized interactive dashboard."""
        
        # Get user preferences and permissions
        user_config = await self._get_user_config(user_id)
        
        # Query relevant data
        data_queries = await self._build_data_queries(dashboard_config, user_config)
        dashboard_data = await self._execute_queries(data_queries)
        
        # Generate visualizations
        charts = await self.visualization_engine.create_charts(
            dashboard_data, dashboard_config["chart_types"]
        )
        
        # Create interactive elements
        filters = await self._create_interactive_filters(dashboard_data)
        drill_downs = await self._create_drill_down_paths(dashboard_data)
        
        return {
            "dashboard_id": f"dash_{user_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "charts": charts,
            "filters": filters,
            "drill_downs": drill_downs,
            "real_time_updates": True,
            "export_options": ["pdf", "excel", "png"],
            "sharing_permissions": user_config.get("sharing_permissions", [])
        }
    
    async def perform_risk_analysis(
        self, 
        portfolio_data: pd.DataFrame,
        scenarios: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Perform comprehensive risk analysis with scenario testing."""
        
        # Calculate current risk metrics
        current_risk = await self._calculate_portfolio_risk(portfolio_data)
        
        # Stress testing
        stress_results = []
        for scenario in scenarios:
            stressed_portfolio = await self._apply_stress_scenario(
                portfolio_data, scenario
            )
            stress_risk = await self._calculate_portfolio_risk(stressed_portfolio)
            stress_results.append({
                "scenario": scenario["name"],
                "risk_change": stress_risk - current_risk,
                "impact_metrics": await self._calculate_impact_metrics(
                    portfolio_data, stressed_portfolio
                )
            })
        
        # Monte Carlo simulation
        monte_carlo_results = await self._run_monte_carlo_simulation(
            portfolio_data, iterations=10000
        )
        
        # Value at Risk calculations
        var_95 = await self._calculate_var(portfolio_data, confidence=0.95)
        var_99 = await self._calculate_var(portfolio_data, confidence=0.99)
        
        return {
            "current_risk_metrics": current_risk,
            "stress_test_results": stress_results,
            "monte_carlo": monte_carlo_results,
            "value_at_risk": {
                "95_percent": var_95,
                "99_percent": var_99
            },
            "risk_recommendations": await self._generate_risk_recommendations(
                current_risk, stress_results, monte_carlo_results
            )
        }

# Usage Example
async def main():
    analytics = FinancialAnalyticsEngine(config={
        "data_sources": ["loan_system", "market_data", "customer_db"],
        "ml_model_version": "v3.2",
        "real_time_processing": True
    })
    
    # Generate predictive insights
    insights = await analytics.generate_predictive_insights(
        historical_data=loan_data,
        prediction_horizon=90
    )
    
    print(f"Predicted loan volume: {insights['predictions']['loan_performance']['volume']}")
    print(f"Market trend: {insights['insights']['market_direction']}")

if __name__ == "__main__":
    asyncio.run(main())