//! Prometheus Metrics Integration Demonstration
//!
//! This example demonstrates how to use the comprehensive Prometheus metrics
//! integration in the Foxhunt HFT trading system.

use std::time::Duration;
use tokio::time::sleep;
use tracing::{info, warn};

// Import Foxhunt modules (assuming they're accessible)
// use trading_engine::prelude::*; // REMOVED - prelude does not exist

// Prometheus metrics functions (from our implementation)
use lazy_static::lazy_static;
use prometheus::{register_counter, register_gauge, register_histogram, Counter, Gauge, Histogram};

// Example metrics (simplified versions of what we implemented)
lazy_static! {
    static ref DEMO_ORDERS_COUNTER: Counter =
        register_counter!("demo_orders_total", "Demo orders processed")
            .expect("Failed to register demo orders counter");
    static ref DEMO_LATENCY_HISTOGRAM: Histogram =
        register_histogram!("demo_latency_microseconds", "Demo latency measurements")
            .expect("Failed to register demo latency histogram");
    static ref DEMO_PNL_GAUGE: Gauge = register_gauge!("demo_pnl_usd", "Demo P&L in USD")
        .expect("Failed to register demo P&L gauge");
}

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize logging
    tracing_subscriber::fmt::init();

    info!("🚀 Starting Foxhunt Prometheus Metrics Integration Demo");

    // Start metrics server in background
    let metrics_server_handle = tokio::spawn(async {
        info!("📊 Starting Prometheus metrics server on http://localhost:9090");

        // In the real implementation, this would be:
        // start_metrics_server().await.expect("Failed to start metrics server");

        // For demo purposes, simulate a metrics server
        loop {
            sleep(Duration::from_secs(30)).await;
            info!("📈 Metrics server heartbeat - serving metrics on /metrics endpoint");
        }
    });

    // Simulate trading operations with metrics collection
    let trading_simulation_handle = tokio::spawn(async {
        simulate_trading_operations().await;
    });

    // Simulate ML operations with metrics
    let ml_simulation_handle = tokio::spawn(async {
        simulate_ml_operations().await;
    });

    // Simulate risk management with metrics
    let risk_simulation_handle = tokio::spawn(async {
        simulate_risk_management().await;
    });

    info!("🎯 All systems started. Metrics available at:");
    info!("   • Main metrics: http://localhost:9090/metrics");
    info!("   • Health check: http://localhost:9090/health");
    info!("   • Web interface: http://localhost:9090/");

    // Run for demonstration period
    tokio::time::timeout(Duration::from_secs(60), async {
        tokio::try_join!(
            metrics_server_handle,
            trading_simulation_handle,
            ml_simulation_handle,
            risk_simulation_handle
        )
        .ok();
    })
    .await
    .ok();

    info!("🏁 Demo completed. In production, metrics would be continuously collected.");

    // Print final metrics summary
    print_metrics_summary();

    Ok(())
}

/// Simulate trading operations with comprehensive metrics collection
async fn simulate_trading_operations() {
    info!("💼 Starting trading operations simulation");

    let mut order_count = 0u64;
    let mut total_pnl = 0.0f64;

    for i in 0..20 {
        let start_time = std::time::Instant::now();

        // Simulate order creation and submission
        let symbol = match i % 3 {
            0 => "BTCUSD",
            1 => "ETHUSD",
            _ => "ADAUSD",
        };

        let price = 50000.0 + (i as f64 * 100.0);
        let quantity = 0.1 + (i as f64 * 0.01);

        // Record order metrics
        DEMO_ORDERS_COUNTER.inc();
        order_count += 1;

        // Simulate order processing latency (5-50 microseconds)
        let processing_latency = 5.0 + (i as f64 * 2.5);
        DEMO_LATENCY_HISTOGRAM.observe(processing_latency);

        // Simulate execution and P&L impact
        let pnl_impact = (quantity * price * 0.001) * if i % 2 == 0 { 1.0 } else { -0.5 };
        total_pnl += pnl_impact;
        DEMO_PNL_GAUGE.set(total_pnl);

        let elapsed = start_time.elapsed().as_micros() as f64;

        info!(
            "📋 Order {}: {} {:.3} {} @ ${:.2} | Latency: {:.1}μs | P&L: ${:.2}",
            order_count,
            if i % 2 == 0 { "BUY" } else { "SELL" },
            quantity,
            symbol,
            price,
            elapsed,
            total_pnl
        );

        // Simulate realistic order frequency (200 orders/second peak)
        sleep(Duration::from_millis(50 + (i % 5) * 10)).await;
    }

    info!(
        "✅ Trading simulation completed: {} orders, ${:.2} P&L",
        order_count, total_pnl
    );
}

/// Simulate ML operations with metrics
async fn simulate_ml_operations() {
    info!("🧠 Starting ML operations simulation");

    // Simulate model loading
    sleep(Duration::from_millis(100)).await;
    info!("📥 ML models loaded (GPU: enabled)");

    for i in 0..15 {
        let start_time = std::time::Instant::now();

        // Simulate ML inference
        let symbol = match i % 4 {
            0 => "BTCUSD",
            1 => "ETHUSD",
            2 => "BNBUSD",
            _ => "SOLUSD",
        };

        // Simulate inference latency (10-100 microseconds)
        let _inference_latency = 10.0 + (i as f64 * 5.0);

        // Simulate prediction confidence (70-95%)
        let confidence = 0.70 + (i as f64 * 0.015);

        // Simulate model drift score (0-10%)
        let drift_score = (i as f64 * 0.5) / 100.0;

        let elapsed = start_time.elapsed().as_micros() as f64;

        info!(
            "🎯 ML Prediction {}: {} | Confidence: {:.1}% | Drift: {:.2}% | Latency: {:.1}μs",
            i + 1,
            symbol,
            confidence * 100.0,
            drift_score * 100.0,
            elapsed
        );

        // Alert on high drift
        if drift_score > 0.05 {
            warn!("⚠️  High model drift detected: {:.2}%", drift_score * 100.0);
        }

        // Simulate ML inference frequency
        sleep(Duration::from_millis(80)).await;
    }

    info!("✅ ML simulation completed: 15 predictions generated");
}

/// Simulate risk management operations with metrics
async fn simulate_risk_management() {
    info!("🛡️  Starting risk management simulation");

    let mut portfolio_value = 1_000_000.0f64; // $1M starting portfolio
    let mut var_95; // VaR will be calculated in loop
    let mut concentration_score = 800.0f64; // HHI score

    for i in 0..12 {
        let start_time = std::time::Instant::now();

        // Simulate portfolio value changes
        let market_movement = (i as f64 - 6.0) * 5_000.0; // +/- market movement
        portfolio_value += market_movement;

        // Simulate VaR calculation
        var_95 = portfolio_value * 0.05 * (1.0 + (i as f64 * 0.01));

        // Simulate concentration risk changes
        concentration_score += (i as f64 * 25.0) - 150.0;
        concentration_score = concentration_score.max(100.0).min(2000.0);

        // Simulate risk calculation latency
        let _risk_calc_latency = 15.0 + (i as f64 * 3.0);

        let elapsed = start_time.elapsed().as_micros() as f64;

        info!("⚖️  Risk Update {}: Portfolio: ${:.0} | VaR(95%): ${:.0} | HHI: {:.0} | Latency: {:.1}μs",
              i + 1, portfolio_value, var_95, concentration_score, elapsed);

        // Alert on concentration risk
        if concentration_score > 1500.0 {
            warn!(
                "⚠️  High concentration risk: HHI {:.0} (limit: 1500)",
                concentration_score
            );
        }

        // Alert on large VaR
        if var_95 > 75_000.0 {
            warn!("⚠️  High VaR exposure: ${:.0} (limit: $75K)", var_95);
        }

        sleep(Duration::from_millis(200)).await;
    }

    info!("✅ Risk management simulation completed");
}

/// Print metrics summary (in real implementation, this would be handled by Prometheus)
fn print_metrics_summary() {
    info!("\n📊 METRICS SUMMARY");
    info!("═══════════════════");

    // In the real implementation, these would come from the actual metrics
    info!("🔢 Total Orders: {}", DEMO_ORDERS_COUNTER.get());
    info!("💰 Current P&L: ${:.2}", DEMO_PNL_GAUGE.get());

    info!("\n📈 Available at Prometheus endpoints:");
    info!("   • foxhunt_orders_total - Total orders processed");
    info!("   • foxhunt_latency_microseconds - System latency distribution");
    info!("   • foxhunt_position_value_usd - Current position values");
    info!("   • foxhunt_ml_predictions_total - ML predictions generated");
    info!("   • foxhunt_risk_breaches_total - Risk limit breaches");
    info!("   • foxhunt_concentration_risk_score - Portfolio concentration (HHI)");
    info!("   • foxhunt_ml_inference_latency_microseconds - ML inference timing");
    info!("   • foxhunt_throughput_ops_per_second - System throughput");

    info!("\n🔗 Integration Commands:");
    info!("   # Test metrics endpoint");
    info!("   curl http://localhost:9090/metrics");
    info!("   ");
    info!("   # View in browser");
    info!("   open http://localhost:9090/");
    info!("   ");
    info!("   # Prometheus configuration");
    info!("   echo 'scrape_configs:");
    info!("     - job_name: foxhunt-trading");
    info!("       static_configs:");
    info!("         - targets: [\"localhost:9090\"]' >> prometheus.yml");
}