//! Copula-Based Dependency Modeling with Machine Learning
//!
//! Implements ML-enhanced copula models for capturing complex dependencies between
//! financial risk factors. Supports various copula families with neural network
//! parameter estimation for dynamic dependency modeling.
//!
//! # Features
//! - Dynamic copula parameter estimation using neural networks
//! - Multiple copula families: Gaussian, t-Copula, Clayton, Gumbel, Frank
//! - Vine copulas for high-dimensional dependencies
//! - Time-varying copula parameters
//! - Tail dependency modeling for extreme events
//! - Conditional copulas for regime-dependent dependencies
//!
//! # Performance Targets
//! - Parameter estimation: <500μs
//! - Dependency simulation: <1ms for 1000 samples
//! - Real-time parameter updates: <100μs
//! - Memory efficiency: <128MB for 100 assets

use std::sync::Arc;

use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use core::types::prelude::*;

use super::*;

    
    #[test]
    fn test_neural_copula_creation() {
        let config = CopulaModelConfig::default();
        let copula_family = CopulaFamily::Gaussian { 
            correlation_matrix: vec![vec![1.0, 0.5], vec![0.5, 1.0]] 
        };
        
        let model = NeuralCopulaModel::new(copula_family, config);
        
        assert_eq!(model.config.n_factors, 10);
        assert!(model.parameter_history.is_empty());
    }
    
    #[test]
    fn test_parameter_constraint() {
        let config = CopulaModelConfig::default();
        let copula_family = CopulaFamily::Clayton { theta: 1.0 };
        let model = NeuralCopulaModel::new(copula_family, config);
        
        // Test Clayton theta constraint (must be > 0)
        let constrained = model.constrain_parameters(vec![-0.5]);
        assert!(constrained[0] > 0.0);
        
        let constrained = model.constrain_parameters(vec![2.0]);
        assert_eq!(constrained[0], 2.0);
    }
    
    #[test]
    fn test_tail_dependence_estimation() {
        let config = CopulaModelConfig::default();
        let copula_family = CopulaFamily::Gaussian { 
            correlation_matrix: vec![vec![1.0, 0.5], vec![0.5, 1.0]] 
        };
        let model = NeuralCopulaModel::new(copula_family, config);
        
        // Create mock market data
        let asset_data = vec![
            AssetMarketData {
                asset_id: "AAPL".to_string(),
                returns: vec![0.01, -0.02, 0.015, -0.01, 0.005],
                mean_return: 0.001,
                volatility: 0.02,
                skewness: 0.1,
                kurtosis: 3.0,
            },
            AssetMarketData {
                asset_id: "MSFT".to_string(),
                returns: vec![0.008, -0.015, 0.012, -0.008, 0.003],
                mean_return: 0.0005,
                volatility: 0.018,
                skewness: -0.1,
                kurtosis: 2.8,
            },
        ];
        
        let market_data = MarketDataWindow {
            asset_data,
            window_start: Utc::now(),
            window_end: Utc::now(),
            n_observations: 5,
        };
        
        let tail_dep = model.estimate_tail_dependence(&market_data);
        
        assert!(tail_dep.upper_tail >= 0.0 && tail_dep.upper_tail <= 1.0);
        assert!(tail_dep.lower_tail >= 0.0 && tail_dep.lower_tail <= 1.0);
    }
    
    #[test]
    fn test_copula_simulation() {
        let config = CopulaModelConfig::default();
        let copula_family = CopulaFamily::Clayton { theta: 2.0 };
        let model = NeuralCopulaModel::new(copula_family, config);
        
        let parameters = CopulaParameters {
            parameters: vec![2.0],
            confidence_intervals: vec![(1.5, 2.5)],
            tail_dependence: TailDependence {
                upper_tail: 0.0,
                lower_tail: 0.5,
                asymmetry: -0.5,
                upper_tail_ci: (0.0, 0.1),
                lower_tail_ci: (0.4, 0.6),
            },
            gof_statistics: GoodnessOfFitStats {
                cramer_von_mises: 0.1,
                anderson_darling: 0.8,
                kolmogorov_smirnov: 0.05,
                aic: 100.0,
                bic: 105.0,
                p_value: 0.15,
            },
            timestamp: Utc::now(),
            confidence_score: 0.85,
        };
        
        let simulation_result = model.simulate_dependencies(1000, &parameters);
        
        assert_eq!(simulation_result.simulated_uniforms.len(), 1000);
        assert_eq!(simulation_result.simulated_uniforms[0].len(), model.config.n_factors);
        assert!(simulation_result.simulation_time_us > 0);
        
        // Check that all values are in [0, 1]
        for sample in &simulation_result.simulated_uniforms {
            for &value in sample {
                assert!(value >= 0.0 && value <= 1.0);
            }
        }
    }
    
    #[test]
    fn test_performance_requirements() {
        let config = CopulaModelConfig::default();
        let copula_family = CopulaFamily::Gaussian { 
            correlation_matrix: vec![vec![1.0, 0.3], vec![0.3, 1.0]] 
        };
        let mut model = NeuralCopulaModel::new(copula_family, config);
        
        // Create minimal market data for performance test
        let asset_data = vec![
            AssetMarketData {
                asset_id: "TEST1".to_string(),
                returns: vec![0.01; 100],
                mean_return: 0.01,
                volatility: 0.02,
                skewness: 0.0,
                kurtosis: 3.0,
            },
            AssetMarketData {
                asset_id: "TEST2".to_string(),
                returns: vec![0.008; 100],
                mean_return: 0.008,
                volatility: 0.018,
                skewness: 0.0,
                kurtosis: 3.0,
            },
        ];
        
        let market_data = MarketDataWindow {
            asset_data,
            window_start: Utc::now(),
            window_end: Utc::now(),
            n_observations: 100,
        };
        
        let start = std::time::Instant::now();
        let parameters = model.estimate_parameters(&market_data);
        let estimation_time = start.elapsed();
        
        // Should complete parameter estimation in <500μs
        assert!(estimation_time.as_micros() < 500);
        
        let start = std::time::Instant::now();
        let simulation = model.simulate_dependencies(1000, &parameters);
        let simulation_time = start.elapsed();
        
        // Should complete simulation in <1ms
        assert!(simulation_time.as_millis() < 1);
        assert!(simulation.simulation_time_us < 1000);
    }