//! Comprehensive Backtesting Workflow Integration Tests
//!
//! This module provides complete end-to-end backtesting workflow testing covering:
//! - Backtesting engine initialization and configuration
//! - Historical data loading and validation
//! - Strategy execution and ML model integration
//! - Performance analytics and result storage
//! - Multi-timeframe and multi-strategy testing
//! - Resource management and memory efficiency
#![allow(unused_crate_dependencies)]

use std::collections::HashMap;
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::sync::RwLock;
use uuid::Uuid;

// use trading_engine::prelude::*; // REMOVED - prelude does not exist
// use risk::prelude::*; // REMOVED - prelude does not exist
use ml::prelude::*;
use data::prelude::*;

/// Comprehensive backtesting test suite
pub struct BacktestingTestSuite {
    backtest_engine: Arc<BacktestEngine>,
    data_manager: Arc<DataManager>,
    strategy_manager: Arc<StrategyManager>,
    performance_analyzer: Arc<PerformanceAnalyzer>,
    test_config: BacktestingTestConfig,
}

/// Configuration for backtesting tests
#[derive(Debug, Clone)]
pub struct BacktestingTestConfig {
    pub test_start_date: chrono::DateTime<chrono::Utc>,
    pub test_end_date: chrono::DateTime<chrono::Utc>,
    pub test_symbols: Vec<String>,
    pub initial_capital: Decimal,
    pub max_backtest_duration_seconds: u64,
    pub min_trades_per_day: usize,
    pub max_drawdown_percent: f64,
    pub min_sharpe_ratio: f64,
}

impl Default for BacktestingTestConfig {
    fn default() -> Self {
        Self {
            test_start_date: chrono::Utc::now() - chrono::Duration::days(30),
            test_end_date: chrono::Utc::now() - chrono::Duration::days(1),
            test_symbols: vec!["EURUSD".to_string(), "GBPUSD".to_string(), "USDJPY".to_string()],
            initial_capital: Decimal::new(100_000, 0), // $100,000
            max_backtest_duration_seconds: 300, // 5 minutes max
            min_trades_per_day: 10,
            max_drawdown_percent: 20.0, // 20% max drawdown
            min_sharpe_ratio: 1.0, // Minimum Sharpe ratio
        }
    }
}

/// Backtesting execution result
#[derive(Debug, Clone)]
pub struct BacktestResult {
    pub backtest_id: Uuid,
    pub strategy_name: String,
    pub execution_time: Duration,
    pub total_trades: usize,
    pub winning_trades: usize,
    pub losing_trades: usize,
    pub total_pnl: Decimal,
    pub max_drawdown: f64,
    pub sharpe_ratio: f64,
    pub sortino_ratio: f64,
    pub win_rate: f64,
    pub avg_trade_duration: Duration,
    pub memory_usage_mb: f64,
    pub cpu_usage_percent: f64,
}

impl BacktestingTestSuite {
    /// Create new backtesting test suite
    pub async fn new() -> Result<Self, Box<dyn std::error::Error>> {
        let backtest_engine = Arc::new(BacktestEngine::new().await?);
        let data_manager = Arc::new(DataManager::new().await?);
        let strategy_manager = Arc::new(StrategyManager::new().await?);
        let performance_analyzer = Arc::new(PerformanceAnalyzer::new());
        let test_config = BacktestingTestConfig::default();

        Ok(Self {
            backtest_engine,
            data_manager,
            strategy_manager,
            performance_analyzer,
            test_config,
        })
    }

    /// Test complete backtesting workflow
    #[tokio::test]
    pub async fn test_complete_backtesting_workflow() -> Result<(), Box<dyn std::error::Error>> {
        let suite = Self::new().await?;

        // Test multiple strategies
        let strategies = vec![
            "MovingAverageCrossover",
            "MeanReversion",
            "MLTrendFollowing",
            "RiskParity",
        ];

        for strategy_name in strategies {
            let result = suite.test_strategy_backtest(strategy_name.to_string()).await?;
            
            // Validate results
            suite.validate_backtest_result(&result).await?;
            
            println!("Strategy: {} - PnL: ${:.2}, Sharpe: {:.2}, Drawdown: {:.1}%", 
                     result.strategy_name, result.total_pnl, result.sharpe_ratio, result.max_drawdown);
        }

        Ok(())
    }

    /// Test backtesting performance and scalability
    #[tokio::test]
    pub async fn test_backtesting_performance() -> Result<(), Box<dyn std::error::Error>> {
        let suite = Self::new().await?;

        // Test with different data sizes
        let test_cases = vec![
            (7, "1 week"),      // 7 days
            (30, "1 month"),    // 30 days  
            (90, "3 months"),   // 90 days
            (180, "6 months"),  // 180 days
        ];

        for (days, description) in test_cases {
            let config = BacktestingTestConfig {
                test_start_date: chrono::Utc::now() - chrono::Duration::days(days),
                test_end_date: chrono::Utc::now() - chrono::Duration::days(1),
                ..suite.test_config.clone()
            };

            let start_time = Instant::now();
            let result = suite.run_performance_test(&config).await?;
            let execution_time = start_time.elapsed();

            // Validate performance requirements
            assert!(
                execution_time.as_secs() <= config.max_backtest_duration_seconds,
                "Backtest duration {}s exceeds limit {}s for {}",
                execution_time.as_secs(), config.max_backtest_duration_seconds, description
            );

            // Memory usage should be reasonable
            assert!(
                result.memory_usage_mb < 1000.0, // Less than 1GB
                "Memory usage {:.1}MB too high for {}",
                result.memory_usage_mb, description
            );

            println!("Performance Test {}: {:.1}s, Memory: {:.1}MB, CPU: {:.1}%",
                     description, execution_time.as_secs_f64(), 
                     result.memory_usage_mb, result.cpu_usage_percent);
        }

        Ok(())
    }

    /// Test ML model integration in backtesting
    #[tokio::test]
    pub async fn test_ml_model_integration() -> Result<(), Box<dyn std::error::Error>> {
        let suite = Self::new().await?;

        // Test different ML models
        let ml_models = vec![
            "TFT",           // Temporal Fusion Transformer
            "MAMBA",         // MAMBA-2 State Space Model
            "LSTM",          // LSTM Neural Network
            "Transformer",   // Transformer model
            "DQN",           // Deep Q-Network
            "PPO",           // Proximal Policy Optimization
        ];

        for model_name in ml_models {
            let result = suite.test_ml_model_backtest(model_name.to_string()).await?;
            
            // ML models should show some predictive capability
            assert!(
                result.sharpe_ratio > 0.5, // Modest requirement for ML models
                "ML model {} Sharpe ratio {:.2} too low",
                model_name, result.sharpe_ratio
            );

            // Should generate reasonable number of trades
            assert!(
                result.total_trades > 50, // At least 50 trades in test period
                "ML model {} generated only {} trades",
                model_name, result.total_trades
            );

            println!("ML Model {}: {} trades, Sharpe: {:.2}, PnL: ${:.2}",
                     model_name, result.total_trades, result.sharpe_ratio, result.total_pnl);
        }

        Ok(())
    }

    /// Test multi-asset backtesting
    #[tokio::test]
    pub async fn test_multi_asset_backtesting() -> Result<(), Box<dyn std::error::Error>> {
        let suite = Self::new().await?;

        // Test portfolio strategies across multiple assets
        let portfolio_config = BacktestingTestConfig {
            test_symbols: vec![
                "EURUSD".to_string(), "GBPUSD".to_string(), "USDJPY".to_string(),
                "AUDUSD".to_string(), "NZDUSD".to_string(), "USDCAD".to_string(),
                "USDCHF".to_string(), "EURGBP".to_string(), "EURJPY".to_string(),
            ],
            ..suite.test_config.clone()
        };

        let result = suite.run_portfolio_backtest(&portfolio_config).await?;

        // Portfolio should show diversification benefits
        assert!(
            result.sharpe_ratio >= suite.test_config.min_sharpe_ratio,
            "Portfolio Sharpe ratio {:.2} below minimum {:.2}",
            result.sharpe_ratio, suite.test_config.min_sharpe_ratio
        );

        // Drawdown should be controlled
        assert!(
            result.max_drawdown <= suite.test_config.max_drawdown_percent,
            "Portfolio drawdown {:.1}% exceeds limit {:.1}%",
            result.max_drawdown, suite.test_config.max_drawdown_percent
        );

        // Should trade across multiple symbols
        assert!(
            result.total_trades > portfolio_config.test_symbols.len() * 10,
            "Portfolio generated only {} trades across {} symbols",
            result.total_trades, portfolio_config.test_symbols.len()
        );

        Ok(())
    }

    /// Test risk management integration
    #[tokio::test]
    pub async fn test_risk_management_integration() -> Result<(), Box<dyn std::error::Error>> {
        let suite = Self::new().await?;

        // Test with aggressive risk settings
        let high_risk_config = BacktestingTestConfig {
            max_drawdown_percent: 50.0, // Allow higher drawdown for testing
            ..suite.test_config.clone()
        };

        let result = suite.test_risk_managed_backtest(&high_risk_config).await?;

        // Risk management should prevent excessive losses
        assert!(
            result.max_drawdown <= high_risk_config.max_drawdown_percent,
            "Risk management failed: drawdown {:.1}% exceeded limit {:.1}%",
            result.max_drawdown, high_risk_config.max_drawdown_percent
        );

        // Should generate trades but with controlled risk
        assert!(
            result.total_trades > 0,
            "Risk management too restrictive: no trades generated"
        );

        Ok(())
    }

    /// Test backtesting data integrity
    #[tokio::test]
    pub async fn test_data_integrity() -> Result<(), Box<dyn std::error::Error>> {
        let suite = Self::new().await?;

        // Test data loading and validation
        for symbol in &suite.test_config.test_symbols {
            let data = suite.data_manager.load_historical_data(
                symbol.clone(),
                suite.test_config.test_start_date,
                suite.test_config.test_end_date,
            ).await?;

            // Validate data quality
            assert!(!data.is_empty(), "No data loaded for symbol {}", symbol);
            
            // Check for gaps in data
            suite.validate_data_continuity(&data, symbol).await?;
            
            // Check data ranges
            suite.validate_data_ranges(&data, symbol).await?;
        }

        Ok(())
    }

    /// Test concurrent backtesting
    #[tokio::test]
    pub async fn test_concurrent_backtesting() -> Result<(), Box<dyn std::error::Error>> {
        let suite = Self::new().await?;

        let strategies = vec!["Strategy1", "Strategy2", "Strategy3", "Strategy4"];
        let mut tasks = Vec::new();

        // Run multiple backtests concurrently
        for strategy in strategies {
            let suite_clone = suite.clone();
            let strategy_name = strategy.to_string();
            
            let task = tokio::spawn(async move {
                suite_clone.test_strategy_backtest(strategy_name).await
            });
            
            tasks.push(task);
        }

        // Wait for all backtests to complete
        let results = futures::future::try_join_all(tasks).await?;

        // All backtests should complete successfully
        for result in results {
            let backtest_result = result?;
            assert!(backtest_result.total_trades > 0, "Concurrent backtest failed to generate trades");
        }

        Ok(())
    }

    /// Helper method to test a single strategy backtest
    async fn test_strategy_backtest(&self, strategy_name: String) -> Result<BacktestResult, Box<dyn std::error::Error>> {
        let start_time = Instant::now();
        let backtest_id = Uuid::new_v4();

        // Initialize strategy
        let strategy = self.strategy_manager.create_strategy(&strategy_name).await?;
        
        // Load historical data
        let mut all_data = HashMap::new();
        for symbol in &self.test_config.test_symbols {
            let data = self.data_manager.load_historical_data(
                symbol.clone(),
                self.test_config.test_start_date,
                self.test_config.test_end_date,
            ).await?;
            all_data.insert(symbol.clone(), data);
        }

        // Run backtest
        let backtest_config = BacktestConfig {
            initial_capital: self.test_config.initial_capital,
            start_date: self.test_config.test_start_date,
            end_date: self.test_config.test_end_date,
            symbols: self.test_config.test_symbols.clone(),
        };

        let trades = self.backtest_engine.run_backtest(strategy, &all_data, &backtest_config).await?;
        let execution_time = start_time.elapsed();

        // Calculate performance metrics
        let performance = self.performance_analyzer.analyze_trades(&trades).await?;

        Ok(BacktestResult {
            backtest_id,
            strategy_name,
            execution_time,
            total_trades: trades.len(),
            winning_trades: trades.iter().filter(|t| t.pnl > Decimal::ZERO).count(),
            losing_trades: trades.iter().filter(|t| t.pnl < Decimal::ZERO).count(),
            total_pnl: trades.iter().map(|t| t.pnl).sum(),
            max_drawdown: performance.max_drawdown,
            sharpe_ratio: performance.sharpe_ratio,
            sortino_ratio: performance.sortino_ratio,
            win_rate: performance.win_rate,
            avg_trade_duration: performance.avg_trade_duration,
            memory_usage_mb: self.get_memory_usage(),
            cpu_usage_percent: self.get_cpu_usage(),
        })
    }

    /// Validate backtest result against requirements
    async fn validate_backtest_result(&self, result: &BacktestResult) -> Result<(), Box<dyn std::error::Error>> {
        // Execution time validation
        assert!(
            result.execution_time.as_secs() <= self.test_config.max_backtest_duration_seconds,
            "Backtest execution time {}s exceeds limit {}s",
            result.execution_time.as_secs(), self.test_config.max_backtest_duration_seconds
        );

        // Trade count validation
        let test_days = (self.test_config.test_end_date - self.test_config.test_start_date).num_days() as usize;
        let min_total_trades = test_days * self.test_config.min_trades_per_day;
        
        assert!(
            result.total_trades >= min_total_trades,
            "Strategy {} generated {} trades, expected at least {}",
            result.strategy_name, result.total_trades, min_total_trades
        );

        // Risk validation
        assert!(
            result.max_drawdown <= self.test_config.max_drawdown_percent,
            "Strategy {} drawdown {:.1}% exceeds limit {:.1}%",
            result.strategy_name, result.max_drawdown, self.test_config.max_drawdown_percent
        );

        Ok(())
    }

    // Additional helper methods...
    async fn test_ml_model_backtest(&self, model_name: String) -> Result<BacktestResult, Box<dyn std::error::Error>> {
        // Implementation for ML-specific backtesting
        self.test_strategy_backtest(format!("ML_{}", model_name)).await
    }

    async fn run_performance_test(&self, config: &BacktestingTestConfig) -> Result<BacktestResult, Box<dyn std::error::Error>> {
        // Performance-focused test implementation
        self.test_strategy_backtest("PerformanceTest".to_string()).await
    }

    async fn run_portfolio_backtest(&self, config: &BacktestingTestConfig) -> Result<BacktestResult, Box<dyn std::error::Error>> {
        // Portfolio backtesting implementation
        self.test_strategy_backtest("Portfolio".to_string()).await
    }

    async fn test_risk_managed_backtest(&self, config: &BacktestingTestConfig) -> Result<BacktestResult, Box<dyn std::error::Error>> {
        // Risk management focused test
        self.test_strategy_backtest("RiskManaged".to_string()).await
    }

    async fn validate_data_continuity(&self, data: &[MarketTick], symbol: &str) -> Result<(), Box<dyn std::error::Error>> {
        // Validate data has no significant gaps
        if data.len() < 2 { return Ok(()); }
        
        for window in data.windows(2) {
            let time_gap = window[1].timestamp.signed_duration_since(window[0].timestamp);
            assert!(
                time_gap.num_minutes() <= 5, // No gaps larger than 5 minutes
                "Data gap of {} minutes found in {} data",
                time_gap.num_minutes(), symbol
            );
        }
        Ok(())
    }

    async fn validate_data_ranges(&self, data: &[MarketTick], symbol: &str) -> Result<(), Box<dyn std::error::Error>> {
        // Validate price and volume ranges are reasonable
        for tick in data {
            assert!(tick.bid > Decimal::ZERO, "Invalid bid price for {}", symbol);
            assert!(tick.ask > tick.bid, "Ask <= bid for {}", symbol);
            assert!(tick.volume >= Decimal::ZERO, "Negative volume for {}", symbol);
        }
        Ok(())
    }

    fn get_memory_usage(&self) -> f64 {
        // Mock memory usage calculation
        250.5 // MB
    }

    fn get_cpu_usage(&self) -> f64 {
        // Mock CPU usage calculation
        15.3 // Percent
    }
}

// Clone implementation for test suite
impl Clone for BacktestingTestSuite {
    fn clone(&self) -> Self {
        Self {
            backtest_engine: Arc::clone(&self.backtest_engine),
            data_manager: Arc::clone(&self.data_manager),
            strategy_manager: Arc::clone(&self.strategy_manager),
            performance_analyzer: Arc::clone(&self.performance_analyzer),
            test_config: self.test_config.clone(),
        }
    }
}

// Mock implementations for testing
pub struct BacktestEngine;
pub struct DataManager;
pub struct StrategyManager;
pub struct PerformanceAnalyzer;
pub struct Strategy;
pub struct BacktestConfig {
    pub initial_capital: Decimal,
    pub start_date: chrono::DateTime<chrono::Utc>,
    pub end_date: chrono::DateTime<chrono::Utc>,
    pub symbols: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct MarketTick {
    pub timestamp: chrono::DateTime<chrono::Utc>,
    pub symbol: String,
    pub bid: Decimal,
    pub ask: Decimal,
    pub volume: Decimal,
}

#[derive(Debug, Clone)]
#[cfg_attr(feature = "database", derive(sqlx::FromRow))]
pub struct Trade {
    pub id: Uuid,
    pub symbol: String,
    pub quantity: Decimal,
    pub price: Decimal,
    pub pnl: Decimal,
    pub timestamp: chrono::DateTime<chrono::Utc>,
}

#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    pub max_drawdown: f64,
    pub sharpe_ratio: f64,
    pub sortino_ratio: f64,
    pub win_rate: f64,
    pub avg_trade_duration: Duration,
}

impl BacktestEngine {
    pub async fn new() -> Result<Self, Box<dyn std::error::Error>> { Ok(Self) }
    
    pub async fn run_backtest(
        &self, 
        _strategy: Strategy, 
        _data: &HashMap<String, Vec<MarketTick>>, 
        _config: &BacktestConfig
    ) -> Result<Vec<Trade>, Box<dyn std::error::Error>> {
        // Mock backtest execution
        Ok(vec![
            Trade {
                id: Uuid::new_v4(),
                symbol: "EURUSD".to_string(),
                quantity: Decimal::new(10000, 0),
                price: Decimal::new(11000, 4),
                pnl: Decimal::new(150, 0),
                timestamp: chrono::Utc::now(),
            }
        ])
    }
}

impl DataManager {
    pub async fn new() -> Result<Self, Box<dyn std::error::Error>> { Ok(Self) }
    
    pub async fn load_historical_data(
        &self, 
        _symbol: String, 
        _start: chrono::DateTime<chrono::Utc>, 
        _end: chrono::DateTime<chrono::Utc>
    ) -> Result<Vec<MarketTick>, Box<dyn std::error::Error>> {
        // Mock data loading
        Ok(vec![
            MarketTick {
                timestamp: chrono::Utc::now(),
                symbol: "EURUSD".to_string(),
                bid: Decimal::new(10995, 4),
                ask: Decimal::new(11005, 4),
                volume: Decimal::new(1000000, 0),
            }
        ])
    }
}

impl StrategyManager {
    pub async fn new() -> Result<Self, Box<dyn std::error::Error>> { Ok(Self) }
    
    pub async fn create_strategy(&self, _name: &str) -> Result<Strategy, Box<dyn std::error::Error>> {
        Ok(Strategy)
    }
}

impl PerformanceAnalyzer {
    pub fn new() -> Self { Self }
    
    pub async fn analyze_trades(&self, _trades: &[Trade]) -> Result<PerformanceMetrics, Box<dyn std::error::Error>> {
        Ok(PerformanceMetrics {
            max_drawdown: 5.2,
            sharpe_ratio: 1.8,
            sortino_ratio: 2.1,
            win_rate: 0.65,
            avg_trade_duration: Duration::from_secs(3600),
        })
    }
}