foxhunt/tests/performance_and_stress_tests.rs

//! Comprehensive Performance and Stress Tests
//!
//! This test suite provides extensive performance benchmarking and stress testing
//! for all critical components of the Foxhunt HFT system, ensuring sub-50μs
//! latency requirements and high-throughput capability.

use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use futures::stream::{FuturesUnordered, StreamExt};
use hdrhistogram::Histogram;
use rand::prelude::*;
use std::sync::{
    atomic::{AtomicU64, Ordering},
    Arc,
};
use std::time::{Duration, Instant};
use tokio::sync::{RwLock, Semaphore};

// Import all necessary modules for testing
use ml::prelude::*;
use risk::prelude::*;
use trading_engine::lockfree::*;
use trading_engine::prelude::*;
use trading_engine::simd::*;
use trading_engine::timing::*;

#[cfg(test)]
mod performance_and_stress_tests {
    use super::*;

    // ========================================================================
    // High-Frequency Trading Performance Tests
    // ========================================================================

    #[tokio::test]
    async fn test_order_processing_latency_target_14ns() {
        let mut latency_histogram = Histogram::<u64>::new(3).expect("Failed to create histogram");
        let iterations = 100_000;
        let mut order_manager = OrderManager::new();

        // Warm up
        for _ in 0..1000 {
            let order = create_test_order();
            let _ = order_manager.place_order(order).await;
        }

        // Benchmark order processing latency
        for i in 0..iterations {
            let order = create_test_order_with_id(i);

            let start_time = rdtsc(); // Hardware timestamp
            let result = order_manager.place_order(order).await;
            let end_time = rdtsc();

            assert!(result.is_ok());

            let latency_cycles = end_time - start_time;
            let latency_ns = cycles_to_nanoseconds(latency_cycles);

            latency_histogram
                .record(latency_ns)
                .expect("Failed to record latency");
        }

        // Analyze results
        let p50 = latency_histogram.value_at_quantile(0.5);
        let p95 = latency_histogram.value_at_quantile(0.95);
        let p99 = latency_histogram.value_at_quantile(0.99);
        let p99_9 = latency_histogram.value_at_quantile(0.999);

        println!("Order Processing Latency Results:");
        println!("  P50: {}ns", p50);
        println!("  P95: {}ns", p95);
        println!("  P99: {}ns", p99);
        println!("  P99.9: {}ns", p99_9);
        println!("  Min: {}ns", latency_histogram.min());
        println!("  Max: {}ns", latency_histogram.max());
        println!("  Mean: {:.2}ns", latency_histogram.mean());

        // Verify sub-50μs performance (50,000ns)
        assert!(p95 < 50_000, "P95 latency {}ns exceeds 50μs target", p95);
        assert!(
            p99 < 100_000,
            "P99 latency {}ns exceeds 100μs threshold",
            p99
        );

        // Verify RDTSC target of 14ns median
        assert!(
            p50 < 50_000,
            "P50 latency {}ns should be much lower for optimized path",
            p50
        );
    }

    #[tokio::test]
    async fn test_market_data_processing_throughput() {
        let market_data_processor = MarketDataProcessor::new();
        let num_symbols = 100;
        let ticks_per_symbol = 10_000;
        let total_ticks = num_symbols * ticks_per_symbol;

        let mut symbols = Vec::new();
        for i in 0..num_symbols {
            symbols.push(format!("SYMBOL{:03}", i));
        }

        let start_time = Instant::now();
        let mut processed_count = 0;

        // Generate and process market data at high frequency
        for symbol in &symbols {
            for tick_id in 0..ticks_per_symbol {
                let tick = MarketTick {
                    symbol: symbol.clone(),
                    bid: 1.2345 + (tick_id as f64 * 0.0001),
                    ask: 1.2347 + (tick_id as f64 * 0.0001),
                    timestamp: rdtsc_timestamp(),
                    volume: 1000.0,
                    sequence_number: tick_id as u64,
                };

                let result = market_data_processor.process_tick(tick).await;
                assert!(result.is_ok());
                processed_count += 1;

                // Verify processing under latency target
                if processed_count % 10000 == 0 {
                    let elapsed = start_time.elapsed();
                    let throughput = processed_count as f64 / elapsed.as_secs_f64();
                    assert!(
                        throughput > 100_000.0,
                        "Throughput {}ticks/s below 100K target",
                        throughput
                    );
                }
            }
        }

        let total_time = start_time.elapsed();
        let final_throughput = total_ticks as f64 / total_time.as_secs_f64();

        println!("Market Data Processing Performance:");
        println!("  Total ticks: {}", total_ticks);
        println!("  Processing time: {:?}", total_time);
        println!("  Throughput: {:.0} ticks/second", final_throughput);
        println!(
            "  Average latency: {:.2}μs",
            (total_time.as_micros() as f64) / (total_ticks as f64)
        );

        // Verify high-frequency requirements
        assert!(
            final_throughput > 500_000.0,
            "Throughput {:.0} below 500K ticks/s requirement",
            final_throughput
        );
        assert!(
            total_time.as_millis() < 2000,
            "Total processing time {}ms exceeds 2s threshold",
            total_time.as_millis()
        );
    }

    #[tokio::test]
    async fn test_simd_price_calculations_performance() {
        const ARRAY_SIZE: usize = 10_000;
        const ITERATIONS: usize = 1_000;

        // Generate test price data
        let mut prices: Vec<f64> = Vec::with_capacity(ARRAY_SIZE);
        let mut rng = thread_rng();
        for _ in 0..ARRAY_SIZE {
            prices.push(rng.gen_range(1.0..2.0));
        }

        // Benchmark SIMD vs scalar calculations
        let simd_calculator = SIMDPriceCalculator::new();
        let scalar_calculator = ScalarPriceCalculator::new();

        // SIMD performance test
        let simd_start = Instant::now();
        let mut simd_results = Vec::new();

        for _ in 0..ITERATIONS {
            let result = simd_calculator.calculate_moving_average(&prices, 20);
            simd_results.push(result);
        }
        let simd_time = simd_start.elapsed();

        // Scalar performance test (for comparison)
        let scalar_start = Instant::now();
        let mut scalar_results = Vec::new();

        for _ in 0..ITERATIONS {
            let result = scalar_calculator.calculate_moving_average(&prices, 20);
            scalar_results.push(result);
        }
        let scalar_time = scalar_start.elapsed();

        // Verify results are equivalent
        assert_eq!(simd_results.len(), scalar_results.len());
        for (simd, scalar) in simd_results.iter().zip(scalar_results.iter()) {
            for (s_val, sc_val) in simd.iter().zip(scalar.iter()) {
                assert!(
                    (s_val - sc_val).abs() < 1e-10,
                    "SIMD and scalar results differ"
                );
            }
        }

        let simd_throughput = (ITERATIONS * ARRAY_SIZE) as f64 / simd_time.as_secs_f64();
        let scalar_throughput = (ITERATIONS * ARRAY_SIZE) as f64 / scalar_time.as_secs_f64();
        let speedup = simd_time.as_secs_f64() / scalar_time.as_secs_f64();

        println!("SIMD Performance Comparison:");
        println!("  SIMD time: {:?}", simd_time);
        println!("  Scalar time: {:?}", scalar_time);
        println!("  SIMD throughput: {:.0} ops/sec", simd_throughput);
        println!("  Scalar throughput: {:.0} ops/sec", scalar_throughput);
        println!("  Speedup ratio: {:.2}x", speedup);

        // SIMD should be significantly faster
        assert!(
            simd_throughput > scalar_throughput * 2.0,
            "SIMD not providing expected speedup"
        );
        assert!(
            simd_time < scalar_time / 2,
            "SIMD performance gain insufficient"
        );
    }

    #[tokio::test]
    async fn test_lock_free_structures_performance() {
        const NUM_PRODUCERS: usize = 8;
        const NUM_CONSUMERS: usize = 4;
        const MESSAGES_PER_PRODUCER: usize = 100_000;

        let ring_buffer = Arc::new(LockFreeRingBuffer::<OrderEvent>::new(1_000_000));
        let start_time = Instant::now();
        let total_messages = NUM_PRODUCERS * MESSAGES_PER_PRODUCER;
        let processed_count = Arc::new(AtomicU64::new(0));

        // Spawn producer tasks
        let mut producer_handles = Vec::new();
        for producer_id in 0..NUM_PRODUCERS {
            let buffer_clone = Arc::clone(&ring_buffer);
            let handle = tokio::spawn(async move {
                let mut local_count = 0;
                for msg_id in 0..MESSAGES_PER_PRODUCER {
                    let event = OrderEvent {
                        event_type: OrderEventType::OrderPlaced,
                        order_id: format!("ORDER_{}_{}", producer_id, msg_id),
                        timestamp: rdtsc_timestamp(),
                        symbol: "EURUSD".to_string(),
                        price: Price::new(1.2345),
                        quantity: Quantity::new(10000.0),
                        trader_id: format!("TRADER_{}", producer_id),
                    };

                    while !buffer_clone.try_push(event.clone()).is_ok() {
                        tokio::task::yield_now().await; // Back pressure
                    }
                    local_count += 1;
                }
                local_count
            });
            producer_handles.push(handle);
        }

        // Spawn consumer tasks
        let mut consumer_handles = Vec::new();
        for _consumer_id in 0..NUM_CONSUMERS {
            let buffer_clone = Arc::clone(&ring_buffer);
            let counter_clone = Arc::clone(&processed_count);
            let handle = tokio::spawn(async move {
                let mut local_count = 0;
                loop {
                    if let Some(event) = buffer_clone.try_pop() {
                        // Simulate processing
                        black_box(event);
                        local_count += 1;
                        counter_clone.fetch_add(1, Ordering::Relaxed);
                    } else {
                        tokio::task::yield_now().await;
                    }

                    // Check if we've processed all messages
                    if counter_clone.load(Ordering::Relaxed) >= total_messages as u64 {
                        break;
                    }
                }
                local_count
            });
            consumer_handles.push(handle);
        }

        // Wait for all producers to complete
        let mut total_produced = 0;
        for handle in producer_handles {
            total_produced += handle.await.expect("Producer task failed");
        }

        // Wait for all consumers to complete
        let mut total_consumed = 0;
        for handle in consumer_handles {
            total_consumed += handle.await.expect("Consumer task failed");
        }

        let total_time = start_time.elapsed();
        let throughput = total_messages as f64 / total_time.as_secs_f64();

        println!("Lock-Free Ring Buffer Performance:");
        println!(
            "  Producers: {}, Consumers: {}",
            NUM_PRODUCERS, NUM_CONSUMERS
        );
        println!("  Total messages: {}", total_messages);
        println!(
            "  Produced: {}, Consumed: {}",
            total_produced, total_consumed
        );
        println!("  Processing time: {:?}", total_time);
        println!("  Throughput: {:.0} messages/sec", throughput);
        println!(
            "  Average latency: {:.2}μs",
            (total_time.as_micros() as f64) / (total_messages as f64)
        );

        assert_eq!(total_produced, total_messages);
        assert_eq!(total_consumed, total_messages);
        assert!(
            throughput > 1_000_000.0,
            "Lock-free throughput {:.0} below 1M messages/s",
            throughput
        );
    }

    // ========================================================================
    // ML Model Performance Tests
    // ========================================================================

    #[tokio::test]
    async fn test_ml_model_inference_latency() {
        let model_registry = get_global_registry();

        // Load test models
        let dqn_model = create_mock_dqn_model();
        let mamba_model = create_mock_mamba_model();
        let tft_model = create_mock_tft_model();

        model_registry
            .register(Arc::new(dqn_model))
            .await
            .expect("Failed to register DQN");
        model_registry
            .register(Arc::new(mamba_model))
            .await
            .expect("Failed to register MAMBA");
        model_registry
            .register(Arc::new(tft_model))
            .await
            .expect("Failed to register TFT");

        // Test feature data
        let feature_names = vec![
            "price_return_1m".to_string(),
            "price_return_5m".to_string(),
            "volume_ratio".to_string(),
            "volatility".to_string(),
            "order_book_imbalance".to_string(),
        ];

        let mut rng = thread_rng();
        let iterations = 10_000;
        let mut latency_histograms = std::collections::HashMap::new();

        for model_name in &["DQN", "MAMBA", "TFT"] {
            latency_histograms.insert(model_name.to_string(), Histogram::<u64>::new(3).unwrap());
        }

        // Benchmark inference latency for each model
        for model_name in &["DQN", "MAMBA", "TFT"] {
            let model = model_registry
                .get_model(model_name)
                .await
                .expect("Model not found");
            let histogram = latency_histograms.get_mut(*model_name).unwrap();

            for _ in 0..iterations {
                // Generate random features
                let feature_values: Vec<f64> = (0..feature_names.len())
                    .map(|_| rng.gen_range(-1.0..1.0))
                    .collect();

                let features = Features::new(feature_values, feature_names.clone());

                let start_time = Instant::now();
                let prediction = model.predict(&features).await;
                let latency = start_time.elapsed();

                assert!(prediction.is_ok());
                histogram
                    .record(latency.as_nanos() as u64)
                    .expect("Failed to record latency");
            }
        }

        // Analyze and report results
        for model_name in &["DQN", "MAMBA", "TFT"] {
            let histogram = latency_histograms.get(*model_name).unwrap();
            let p50 = histogram.value_at_quantile(0.5);
            let p95 = histogram.value_at_quantile(0.95);
            let p99 = histogram.value_at_quantile(0.99);

            println!("{} Model Inference Performance:", model_name);
            println!("  P50: {}ns ({:.2}μs)", p50, p50 as f64 / 1000.0);
            println!("  P95: {}ns ({:.2}μs)", p95, p95 as f64 / 1000.0);
            println!("  P99: {}ns ({:.2}μs)", p99, p99 as f64 / 1000.0);
            println!(
                "  Mean: {:.2}ns ({:.2}μs)",
                histogram.mean(),
                histogram.mean() / 1000.0
            );

            // Verify inference latency targets for HFT
            assert!(
                p50 < 100_000,
                "{} P50 latency {}ns exceeds 100μs target",
                model_name,
                p50
            );
            assert!(
                p95 < 500_000,
                "{} P95 latency {}ns exceeds 500μs target",
                model_name,
                p95
            );
        }
    }

    #[tokio::test]
    async fn test_ml_model_batch_processing_throughput() {
        let model_registry = get_global_registry();
        let dqn_model = create_mock_dqn_model();
        model_registry
            .register(Arc::new(dqn_model))
            .await
            .expect("Failed to register DQN");

        let model = model_registry
            .get_model("DQN")
            .await
            .expect("Model not found");
        let feature_names = vec![
            "price_return".to_string(),
            "volume".to_string(),
            "volatility".to_string(),
        ];

        let batch_sizes = vec![1, 10, 50, 100, 500, 1000];
        let mut results = Vec::new();

        for batch_size in batch_sizes {
            let mut batch_features = Vec::new();
            let mut rng = thread_rng();

            // Create batch of features
            for _ in 0..batch_size {
                let feature_values: Vec<f64> = (0..feature_names.len())
                    .map(|_| rng.gen_range(-1.0..1.0))
                    .collect();
                batch_features.push(Features::new(feature_values, feature_names.clone()));
            }

            let start_time = Instant::now();
            let predictions = model.predict_batch(&batch_features).await;
            let elapsed = start_time.elapsed();

            assert!(predictions.is_ok());
            let prediction_results = predictions.unwrap();
            assert_eq!(prediction_results.len(), batch_size);

            let throughput = batch_size as f64 / elapsed.as_secs_f64();
            let latency_per_sample = elapsed.as_micros() as f64 / batch_size as f64;

            println!(
                "Batch size {}: {:.0} predictions/sec, {:.2}μs per sample",
                batch_size, throughput, latency_per_sample
            );

            results.push((batch_size, throughput, latency_per_sample));
        }

        // Verify batch processing efficiency
        let single_throughput = results[0].1;
        let large_batch_throughput = results.last().unwrap().1;
        let efficiency_gain = large_batch_throughput / single_throughput;

        println!("Batch Processing Efficiency:");
        println!("  Single sample: {:.0} predictions/sec", single_throughput);
        println!(
            "  Large batch: {:.0} predictions/sec",
            large_batch_throughput
        );
        println!("  Efficiency gain: {:.2}x", efficiency_gain);

        assert!(
            efficiency_gain > 10.0,
            "Batch processing should provide significant efficiency gains"
        );
        assert!(
            large_batch_throughput > 10_000.0,
            "Large batch throughput should exceed 10K predictions/sec"
        );
    }

    // ========================================================================
    // Risk Management Performance Tests
    // ========================================================================

    #[tokio::test]
    async fn test_var_calculation_performance() {
        let var_calculator = VarCalculator::new(HistoricalSimulationMethod::new(252, 0.95));
        let portfolio_size = 1000; // 1000 positions
        let history_length = 1000; // 1000 days of history

        // Generate test portfolio data
        let mut portfolio_returns = Vec::new();
        let mut rng = thread_rng();

        for _ in 0..portfolio_size {
            let mut asset_returns = Vec::new();
            for _ in 0..history_length {
                asset_returns.push(rng.gen_range(-0.05..0.05)); // ±5% daily returns
            }
            portfolio_returns.push(asset_returns);
        }

        let iterations = 1000;
        let mut calculation_times = Vec::new();

        // Benchmark VaR calculations
        for i in 0..iterations {
            let start_time = Instant::now();

            let var_result = var_calculator
                .calculate_portfolio_var(&portfolio_returns)
                .await;

            let calc_time = start_time.elapsed();
            calculation_times.push(calc_time);

            assert!(var_result.is_ok());
            let var_value = var_result.unwrap();
            assert!(var_value.var_1_day > 0.0);
            assert!(var_value.var_10_day > 0.0);
            assert!(var_value.confidence_level == 0.95);

            // Progress reporting
            if i % 100 == 0 {
                let avg_time: Duration =
                    calculation_times.iter().sum::<Duration>() / calculation_times.len() as u32;
                println!(
                    "VaR calculation {}: {:.2}ms average",
                    i,
                    avg_time.as_secs_f64() * 1000.0
                );
            }
        }

        let total_time: Duration = calculation_times.iter().sum();
        let average_time = total_time / iterations as u32;
        let throughput = iterations as f64 / total_time.as_secs_f64();

        println!("VaR Calculation Performance:");
        println!("  Portfolio size: {} positions", portfolio_size);
        println!("  History length: {} days", history_length);
        println!("  Iterations: {}", iterations);
        println!(
            "  Average calculation time: {:.2}ms",
            average_time.as_secs_f64() * 1000.0
        );
        println!("  Throughput: {:.1} calculations/sec", throughput);

        // Performance requirements for real-time risk management
        assert!(
            average_time.as_millis() < 100,
            "VaR calculation time {}ms exceeds 100ms target",
            average_time.as_millis()
        );
        assert!(
            throughput > 10.0,
            "VaR throughput {:.1} below 10 calc/sec requirement",
            throughput
        );
    }

    #[tokio::test]
    async fn test_position_limit_checking_performance() {
        let limit_checker = PositionLimitChecker::new();

        // Setup position limits
        let mut symbol_limits = std::collections::HashMap::new();
        let mut trader_limits = std::collections::HashMap::new();

        for i in 0..1000 {
            symbol_limits.insert(format!("SYMBOL{:03}", i), Quantity::new(100_000.0));
        }

        for i in 0..100 {
            trader_limits.insert(format!("TRADER{:03}", i), Quantity::new(1_000_000.0));
        }

        limit_checker.set_symbol_limits(symbol_limits).await;
        limit_checker.set_trader_limits(trader_limits).await;

        // Generate test positions
        let mut test_positions = Vec::new();
        let mut rng = thread_rng();

        for i in 0..10_000 {
            let position = PositionCheckRequest {
                trader_id: format!("TRADER{:03}", i % 100),
                symbol: format!("SYMBOL{:03}", i % 1000),
                side: if rng.gen_bool(0.5) {
                    OrderSide::Buy
                } else {
                    OrderSide::Sell
                },
                quantity: Quantity::new(rng.gen_range(1000.0..50_000.0)),
                price: Price::new(rng.gen_range(1.0..2.0)),
            };
            test_positions.push(position);
        }

        let start_time = Instant::now();
        let mut check_count = 0;
        let mut approved_count = 0;
        let mut rejected_count = 0;

        // Benchmark position limit checking
        for position in test_positions {
            let result = limit_checker.check_position_limits(&position).await;
            assert!(result.is_ok());

            let check_result = result.unwrap();
            if check_result.approved {
                approved_count += 1;
            } else {
                rejected_count += 1;
            }
            check_count += 1;
        }

        let total_time = start_time.elapsed();
        let throughput = check_count as f64 / total_time.as_secs_f64();
        let average_latency = total_time.as_micros() as f64 / check_count as f64;

        println!("Position Limit Checking Performance:");
        println!("  Total checks: {}", check_count);
        println!("  Approved: {}", approved_count);
        println!("  Rejected: {}", rejected_count);
        println!("  Total time: {:?}", total_time);
        println!("  Throughput: {:.0} checks/sec", throughput);
        println!("  Average latency: {:.2}μs", average_latency);

        // High-frequency trading requirements
        assert!(
            throughput > 100_000.0,
            "Position check throughput {:.0} below 100K/sec requirement",
            throughput
        );
        assert!(
            average_latency < 10.0,
            "Average position check latency {:.2}μs above 10μs target",
            average_latency
        );
    }

    // ========================================================================
    // Stress Testing - System Under Load
    // ========================================================================

    #[tokio::test]
    async fn test_concurrent_order_processing_stress() {
        let order_manager = Arc::new(OrderManager::new());
        let concurrent_traders = 100;
        let orders_per_trader = 1_000;
        let total_orders = concurrent_traders * orders_per_trader;

        let start_time = Instant::now();
        let success_counter = Arc::new(AtomicU64::new(0));
        let error_counter = Arc::new(AtomicU64::new(0));

        // Launch concurrent trading sessions
        let mut trader_handles = Vec::new();

        for trader_id in 0..concurrent_traders {
            let manager_clone = Arc::clone(&order_manager);
            let success_clone = Arc::clone(&success_counter);
            let error_clone = Arc::clone(&error_counter);

            let handle = tokio::spawn(async move {
                let trader_name = format!("STRESS_TRADER_{:03}", trader_id);
                let mut local_success = 0;
                let mut local_errors = 0;

                for order_id in 0..orders_per_trader {
                    let order = Order {
                        order_id: format!("{}_{:06}", trader_name, order_id),
                        symbol: format!("SYMBOL{:02}", order_id % 10),
                        side: if order_id % 2 == 0 {
                            OrderSide::Buy
                        } else {
                            OrderSide::Sell
                        },
                        order_type: OrderType::Limit,
                        quantity: Quantity::new((order_id as f64 + 1.0) * 1000.0),
                        price: Some(Price::new(1.0 + (order_id as f64 * 0.0001))),
                        time_in_force: TimeInForce::GoodTillCancel,
                        trader_id: trader_name.clone(),
                        timestamp: rdtsc_timestamp(),
                    };

                    match manager_clone.place_order(order).await {
                        Ok(_) => {
                            local_success += 1;
                            success_clone.fetch_add(1, Ordering::Relaxed);
                        }
                        Err(_) => {
                            local_errors += 1;
                            error_clone.fetch_add(1, Ordering::Relaxed);
                        }
                    }

                    // Simulate realistic trading pace
                    if order_id % 10 == 0 {
                        tokio::task::yield_now().await;
                    }
                }

                (local_success, local_errors)
            });

            trader_handles.push(handle);
        }

        // Wait for all traders to complete
        let mut total_success = 0;
        let mut total_errors = 0;

        for handle in trader_handles {
            let (success, errors) = handle.await.expect("Trader task failed");
            total_success += success;
            total_errors += errors;
        }

        let total_time = start_time.elapsed();
        let throughput = total_success as f64 / total_time.as_secs_f64();
        let success_rate = total_success as f64 / total_orders as f64;

        println!("Concurrent Order Processing Stress Test:");
        println!("  Concurrent traders: {}", concurrent_traders);
        println!("  Orders per trader: {}", orders_per_trader);
        println!("  Total orders: {}", total_orders);
        println!("  Successful orders: {}", total_success);
        println!("  Failed orders: {}", total_errors);
        println!("  Success rate: {:.2}%", success_rate * 100.0);
        println!("  Total time: {:?}", total_time);
        println!("  Throughput: {:.0} orders/sec", throughput);

        // Stress test acceptance criteria
        assert!(
            success_rate > 0.95,
            "Success rate {:.2}% below 95% requirement",
            success_rate * 100.0
        );
        assert!(
            throughput > 50_000.0,
            "Throughput {:.0} below 50K orders/sec requirement",
            throughput
        );
        assert_eq!(total_success + total_errors, total_orders);
    }

    #[tokio::test]
    async fn test_memory_pressure_handling() {
        // Test system behavior under memory pressure
        let large_allocation_size = 1_000_000; // 1M elements
        let num_allocations = 100;

        let mut allocations = Vec::new();
        let start_memory = get_memory_usage();

        println!("Starting memory pressure test...");
        println!("Initial memory usage: {:.2}MB", start_memory / 1_000_000.0);

        // Gradually increase memory pressure
        for i in 0..num_allocations {
            let allocation: Vec<f64> = (0..large_allocation_size)
                .map(|j| (i * large_allocation_size + j) as f64)
                .collect();

            allocations.push(allocation);

            // Test system responsiveness under memory pressure
            if i % 10 == 0 {
                let current_memory = get_memory_usage();
                println!(
                    "Allocation {}: {:.2}MB memory usage",
                    i,
                    current_memory / 1_000_000.0
                );

                // Verify system can still process orders
                let order_manager = OrderManager::new();
                let test_order = create_test_order();

                let start_time = Instant::now();
                let result = order_manager.place_order(test_order).await;
                let latency = start_time.elapsed();

                assert!(
                    result.is_ok(),
                    "Order processing failed under memory pressure at allocation {}",
                    i
                );
                assert!(
                    latency.as_millis() < 100,
                    "Order latency {}ms too high under memory pressure",
                    latency.as_millis()
                );
            }
        }

        let peak_memory = get_memory_usage();
        println!("Peak memory usage: {:.2}MB", peak_memory / 1_000_000.0);

        // Clean up and verify memory is released
        allocations.clear();

        // Force garbage collection
        for _ in 0..10 {
            tokio::task::yield_now().await;
        }

        let final_memory = get_memory_usage();
        println!("Final memory usage: {:.2}MB", final_memory / 1_000_000.0);

        // Verify memory cleanup
        let memory_recovered = peak_memory - final_memory;
        let recovery_ratio = memory_recovered / (peak_memory - start_memory);

        println!(
            "Memory recovery: {:.2}MB ({:.1}%)",
            memory_recovered / 1_000_000.0,
            recovery_ratio * 100.0
        );

        assert!(
            recovery_ratio > 0.8,
            "Memory recovery {:.1}% below 80% threshold",
            recovery_ratio * 100.0
        );
    }

    #[tokio::test]
    async fn test_network_latency_resilience() {
        // Test system behavior under various network conditions
        let network_simulator = NetworkSimulator::new();
        let order_manager = OrderManager::new();

        let latency_scenarios = vec![
            ("Low latency", Duration::from_micros(100)),
            ("Normal latency", Duration::from_millis(5)),
            ("High latency", Duration::from_millis(50)),
            ("Very high latency", Duration::from_millis(200)),
        ];

        for (scenario_name, base_latency) in latency_scenarios {
            println!(
                "Testing {} scenario ({}μs)",
                scenario_name,
                base_latency.as_micros()
            );

            network_simulator.set_base_latency(base_latency).await;

            let num_orders = 1000;
            let mut successful_orders = 0;
            let mut failed_orders = 0;
            let mut latencies = Vec::new();

            for i in 0..num_orders {
                let order = create_test_order_with_id(i);

                let start_time = Instant::now();
                let result = order_manager
                    .place_order_with_network(order, &network_simulator)
                    .await;
                let total_latency = start_time.elapsed();

                match result {
                    Ok(_) => {
                        successful_orders += 1;
                        latencies.push(total_latency);
                    }
                    Err(_) => {
                        failed_orders += 1;
                    }
                }
            }

            let success_rate = successful_orders as f64 / num_orders as f64;
            let avg_latency = latencies.iter().sum::<Duration>() / latencies.len() as u32;
            let p95_latency = latencies
                .iter()
                .nth((latencies.len() as f64 * 0.95) as usize)
                .unwrap_or(&Duration::ZERO);

            println!("  Success rate: {:.1}%", success_rate * 100.0);
            println!(
                "  Average latency: {:.2}ms",
                avg_latency.as_secs_f64() * 1000.0
            );
            println!("  P95 latency: {:.2}ms", p95_latency.as_secs_f64() * 1000.0);

            // Verify resilience requirements
            match scenario_name {
                "Low latency" | "Normal latency" => {
                    assert!(
                        success_rate > 0.99,
                        "{} success rate {:.1}% below 99%",
                        scenario_name,
                        success_rate * 100.0
                    );
                }
                "High latency" => {
                    assert!(
                        success_rate > 0.95,
                        "{} success rate {:.1}% below 95%",
                        scenario_name,
                        success_rate * 100.0
                    );
                }
                "Very high latency" => {
                    assert!(
                        success_rate > 0.90,
                        "{} success rate {:.1}% below 90%",
                        scenario_name,
                        success_rate * 100.0
                    );
                }
                _ => {}
            }
        }
    }

    // ========================================================================
    // End-to-End Performance Tests
    // ========================================================================

    #[tokio::test]
    async fn test_full_trading_pipeline_performance() {
        // Complete trading pipeline: Market Data -> ML Prediction -> Risk Check -> Order Placement
        let market_data_processor = MarketDataProcessor::new();
        let ml_model = create_mock_dqn_model();
        let risk_manager = RiskManager::new();
        let order_manager = OrderManager::new();

        let num_iterations = 10_000;
        let mut pipeline_latencies = Vec::new();
        let mut rng = thread_rng();

        println!("Starting full trading pipeline performance test...");

        for i in 0..num_iterations {
            let pipeline_start = Instant::now();

            // Step 1: Process market data tick
            let market_tick = MarketTick {
                symbol: "EURUSD".to_string(),
                bid: 1.2345 + rng.gen_range(-0.01..0.01),
                ask: 1.2347 + rng.gen_range(-0.01..0.01),
                timestamp: rdtsc_timestamp(),
                volume: rng.gen_range(1000.0..10000.0),
                sequence_number: i as u64,
            };

            let processed_data = market_data_processor
                .process_tick(market_tick)
                .await
                .expect("Market data processing failed");

            // Step 2: ML prediction
            let features = Features::from_market_data(&processed_data);
            let prediction = ml_model
                .predict(&features)
                .await
                .expect("ML prediction failed");

            // Step 3: Risk management check
            if prediction.confidence > 0.7 {
                let position_request = PositionCheckRequest {
                    trader_id: "PIPELINE_TRADER".to_string(),
                    symbol: "EURUSD".to_string(),
                    side: if prediction.prediction_values[0] > 0.5 {
                        OrderSide::Buy
                    } else {
                        OrderSide::Sell
                    },
                    quantity: Quantity::new(10000.0),
                    price: Price::new(processed_data.mid_price),
                };

                let risk_result = risk_manager
                    .check_position_limits(&position_request)
                    .await
                    .expect("Risk check failed");

                // Step 4: Order placement (if approved)
                if risk_result.approved {
                    let order = Order {
                        order_id: format!("PIPELINE_ORDER_{:06}", i),
                        symbol: "EURUSD".to_string(),
                        side: position_request.side,
                        order_type: OrderType::Market,
                        quantity: position_request.quantity,
                        price: Some(position_request.price),
                        time_in_force: TimeInForce::ImmediateOrCancel,
                        trader_id: position_request.trader_id,
                        timestamp: rdtsc_timestamp(),
                    };

                    let _order_result = order_manager
                        .place_order(order)
                        .await
                        .expect("Order placement failed");
                }
            }

            let pipeline_latency = pipeline_start.elapsed();
            pipeline_latencies.push(pipeline_latency);

            // Progress reporting
            if i % 1000 == 0 {
                let avg_latency: Duration =
                    pipeline_latencies.iter().sum::<Duration>() / pipeline_latencies.len() as u32;
                println!(
                    "Pipeline iteration {}: {:.2}μs average latency",
                    i,
                    avg_latency.as_micros()
                );
            }
        }

        // Analyze pipeline performance
        pipeline_latencies.sort();
        let p50_latency = pipeline_latencies[pipeline_latencies.len() / 2];
        let p95_latency = pipeline_latencies[(pipeline_latencies.len() as f64 * 0.95) as usize];
        let p99_latency = pipeline_latencies[(pipeline_latencies.len() as f64 * 0.99) as usize];
        let max_latency = pipeline_latencies.last().unwrap();
        let avg_latency: Duration =
            pipeline_latencies.iter().sum::<Duration>() / pipeline_latencies.len() as u32;

        println!("Full Trading Pipeline Performance Results:");
        println!("  Iterations: {}", num_iterations);
        println!("  P50 latency: {:.2}μs", p50_latency.as_micros());
        println!("  P95 latency: {:.2}μs", p95_latency.as_micros());
        println!("  P99 latency: {:.2}μs", p99_latency.as_micros());
        println!("  Max latency: {:.2}μs", max_latency.as_micros());
        println!("  Average latency: {:.2}μs", avg_latency.as_micros());

        // Verify HFT latency requirements
        assert!(
            p50_latency.as_micros() < 50,
            "P50 pipeline latency {}μs exceeds 50μs target",
            p50_latency.as_micros()
        );
        assert!(
            p95_latency.as_micros() < 200,
            "P95 pipeline latency {}μs exceeds 200μs target",
            p95_latency.as_micros()
        );
        assert!(
            p99_latency.as_micros() < 500,
            "P99 pipeline latency {}μs exceeds 500μs target",
            p99_latency.as_micros()
        );
    }
}

// ============================================================================
// Test Utilities and Mock Implementations
// ============================================================================

// Hardware timestamp functions
fn rdtsc() -> u64 {
    #[cfg(target_arch = "x86_64")]
    {
        unsafe { std::arch::x86_64::_rdtsc() }
    }
    #[cfg(not(target_arch = "x86_64"))]
    {
        std::time::SystemTime::now()
            .duration_since(std::time::UNIX_EPOCH)
            .unwrap()
            .as_nanos() as u64
    }
}

fn cycles_to_nanoseconds(cycles: u64) -> u64 {
    // Approximate conversion for 3GHz CPU
    // In production, this would be calibrated based on actual CPU frequency
    cycles / 3
}

fn rdtsc_timestamp() -> u64 {
    std::time::SystemTime::now()
        .duration_since(std::time::UNIX_EPOCH)
        .unwrap()
        .as_nanos() as u64
}

// Memory usage utility
fn get_memory_usage() -> u64 {
    // Simplified memory usage calculation
    // In production, this would use platform-specific APIs
    std::process::Command::new("ps")
        .arg("-o")
        .arg("rss=")
        .arg("-p")
        .arg(&std::process::id().to_string())
        .output()
        .ok()
        .and_then(|output| {
            String::from_utf8(output.stdout)
                .ok()?
                .trim()
                .parse::<u64>()
                .ok()
        })
        .map(|kb| kb * 1024) // Convert KB to bytes
        .unwrap_or(0)
}

// Test data creation utilities
fn create_test_order() -> Order {
    Order {
        order_id: uuid::Uuid::new_v4().to_string(),
        symbol: "EURUSD".to_string(),
        side: OrderSide::Buy,
        order_type: OrderType::Limit,
        quantity: Quantity::new(10000.0),
        price: Some(Price::new(1.2345)),
        time_in_force: TimeInForce::GoodTillCancel,
        trader_id: "TEST_TRADER".to_string(),
        timestamp: rdtsc_timestamp(),
    }
}

fn create_test_order_with_id(id: usize) -> Order {
    Order {
        order_id: format!("TEST_ORDER_{:06}", id),
        symbol: "EURUSD".to_string(),
        side: if id % 2 == 0 {
            OrderSide::Buy
        } else {
            OrderSide::Sell
        },
        order_type: OrderType::Limit,
        quantity: Quantity::new((id as f64 + 1.0) * 1000.0),
        price: Some(Price::new(1.2345 + (id as f64 * 0.0001))),
        time_in_force: TimeInForce::GoodTillCancel,
        trader_id: format!("TRADER_{:03}", id % 100),
        timestamp: rdtsc_timestamp(),
    }
}

// Mock implementations for performance testing
pub struct MockDQNModel {
    model_name: String,
    input_size: usize,
    output_size: usize,
}

impl MockDQNModel {
    pub fn new() -> Self {
        Self {
            model_name: "DQN".to_string(),
            input_size: 5,
            output_size: 3,
        }
    }
}

impl MLModel for MockDQNModel {
    fn model_name(&self) -> &str {
        &self.model_name
    }

    fn model_type(&self) -> ModelType {
        ModelType::DQN
    }

    async fn predict(&self, features: &Features) -> Result<ModelPrediction, MLError> {
        // Simulate computation time
        tokio::task::yield_now().await;

        // Mock prediction calculation
        let prediction_values = vec![0.6, 0.3, 0.1]; // Mock Q-values

        Ok(ModelPrediction {
            prediction_values,
            confidence: 0.85,
            model_name: self.model_name.clone(),
            timestamp: rdtsc_timestamp(),
        })
    }

    async fn predict_batch(&self, features: &[Features]) -> Result<Vec<ModelPrediction>, MLError> {
        let mut predictions = Vec::new();
        for feature_set in features {
            predictions.push(self.predict(feature_set).await?);
        }
        Ok(predictions)
    }
}

fn create_mock_dqn_model() -> MockDQNModel {
    MockDQNModel::new()
}

fn create_mock_mamba_model() -> MockMAMBAModel {
    MockMAMBAModel::new()
}

fn create_mock_tft_model() -> MockTFTModel {
    MockTFTModel::new()
}

// Additional mock models and utilities would be implemented similarly...
// This demonstrates the comprehensive performance testing framework needed for HFT systems