#![allow(
    unused_variables,
    clippy::assertions_on_constants,
    clippy::len_zero,
    clippy::useless_vec
)]
//! Unit Tests for Ensemble Metrics Module
//!
//! This test suite validates Prometheus metrics for ensemble ML monitoring
//! including aggregation latency, confidence, disagreement, weights, and A/B testing.

use prometheus::core::Collector;
use trading_service::ensemble_metrics::*;

#[test]
fn test_ensemble_aggregation_latency_metric() {
    let metric = &*ENSEMBLE_AGGREGATION_LATENCY_US;
    let desc = metric.desc();

    assert!(
        desc.len() > 0,
        "Ensemble aggregation latency histogram should exist"
    );

    // Test different aggregation methods
    metric
        .with_label_values(&["weighted_average"])
        .observe(10.0);
    metric.with_label_values(&["majority_vote"]).observe(5.0);
    metric
        .with_label_values(&["confidence_weighted"])
        .observe(15.0);

    // Verify buckets: [1.0, 5.0, 10.0, 25.0, 50.0, 100.0]
    let collected = metric.collect();
    assert!(!collected.is_empty(), "Should have collected metrics");
}

#[test]
fn test_ensemble_confidence_metric() {
    let metric = &*ENSEMBLE_CONFIDENCE;
    let desc = metric.desc();

    assert!(desc.len() > 0, "Ensemble confidence gauge should exist");

    // Test confidence scores for different symbols (0.0-1.0)
    metric.with_label_values(&["ES.FUT"]).set(0.85);
    metric.with_label_values(&["NQ.FUT"]).set(0.92);
    metric.with_label_values(&["ZN.FUT"]).set(0.78);
    metric.with_label_values(&["6E.FUT"]).set(0.68);

    let collected = metric.collect();
    assert!(!collected.is_empty(), "Should have collected metrics");
}

#[test]
fn test_ensemble_disagreement_rate_metric() {
    let metric = &*ENSEMBLE_DISAGREEMENT_RATE;
    let desc = metric.desc();

    assert!(
        desc.len() > 0,
        "Ensemble disagreement rate gauge should exist"
    );

    // Test disagreement rates (0.0 = all agree, 1.0 = all disagree)
    metric.with_label_values(&["ES.FUT"]).set(0.15); // Low disagreement
    metric.with_label_values(&["NQ.FUT"]).set(0.55); // High disagreement (alert threshold)
    metric.with_label_values(&["ZN.FUT"]).set(0.05); // Very low disagreement
    metric.with_label_values(&["6E.FUT"]).set(0.75); // Very high disagreement

    let collected = metric.collect();
    assert!(!collected.is_empty(), "Should have collected metrics");
}

#[test]
fn test_ensemble_predictions_counter() {
    let metric = &*ENSEMBLE_PREDICTIONS_TOTAL;
    let desc = metric.desc();

    assert!(desc.len() > 0, "Ensemble predictions counter should exist");

    // Test predictions by action type and symbol
    metric.with_label_values(&["buy", "ES.FUT"]).inc();
    metric.with_label_values(&["sell", "ES.FUT"]).inc();
    metric.with_label_values(&["hold", "ES.FUT"]).inc();

    metric.with_label_values(&["buy", "NQ.FUT"]).inc_by(5.0);
    metric.with_label_values(&["sell", "ZN.FUT"]).inc_by(3.0);
    metric.with_label_values(&["hold", "6E.FUT"]).inc_by(10.0);

    let collected = metric.collect();
    assert!(!collected.is_empty(), "Should have collected metrics");
}

#[test]
fn test_ensemble_model_weight_metric() {
    let metric = &*ENSEMBLE_MODEL_WEIGHT;
    let desc = metric.desc();

    assert!(desc.len() > 0, "Ensemble model weight gauge should exist");

    // Test model weights (should sum to ~1.0 for each symbol)
    // ES.FUT weights
    metric.with_label_values(&["DQN", "ES.FUT"]).set(0.25);
    metric.with_label_values(&["PPO", "ES.FUT"]).set(0.30);
    metric.with_label_values(&["MAMBA-2", "ES.FUT"]).set(0.20);
    metric.with_label_values(&["TFT", "ES.FUT"]).set(0.25);
    // Sum = 1.00 ✓

    // NQ.FUT weights (different distribution)
    metric.with_label_values(&["DQN", "NQ.FUT"]).set(0.20);
    metric.with_label_values(&["PPO", "NQ.FUT"]).set(0.35);
    metric.with_label_values(&["TFT", "NQ.FUT"]).set(0.45);
    // Sum = 1.00 ✓

    let collected = metric.collect();
    assert!(!collected.is_empty(), "Should have collected metrics");
}

#[test]
fn test_ensemble_high_disagreement_counter() {
    let metric = &*ENSEMBLE_HIGH_DISAGREEMENT_TOTAL;
    let desc = metric.desc();

    assert!(desc.len() > 0, "High disagreement counter should exist");

    // Test high disagreement events (symbol, threshold)
    metric.with_label_values(&["ES.FUT", "0.5"]).inc();
    metric.with_label_values(&["NQ.FUT", "0.7"]).inc_by(3.0);
    metric.with_label_values(&["ZN.FUT", "0.9"]).inc_by(2.0);

    let collected = metric.collect();
    assert!(!collected.is_empty(), "Should have collected metrics");
}

#[test]
fn test_ensemble_model_pnl_attribution_histogram() {
    let metric = &*ENSEMBLE_MODEL_PNL_CONTRIBUTION;
    let desc = metric.desc();

    assert!(desc.len() > 0, "PnL attribution histogram should exist");

    // Test PnL contributions from different models
    metric.with_label_values(&["DQN", "ES.FUT"]).observe(125.50);
    metric.with_label_values(&["PPO", "ES.FUT"]).observe(-45.25);
    metric.with_label_values(&["TFT", "NQ.FUT"]).observe(350.75);
    metric
        .with_label_values(&["MAMBA-2", "ZN.FUT"])
        .observe(-120.00);

    // Buckets: [-1000, -500, -100, 0, 100, 500, 1000]
    let collected = metric.collect();
    assert!(!collected.is_empty(), "Should have collected metrics");
}

#[test]
fn test_checkpoint_swaps_counter() {
    let metric = &*CHECKPOINT_SWAPS_TOTAL;
    let desc = metric.desc();

    assert!(desc.len() > 0, "Checkpoint swaps counter should exist");

    // Test checkpoint hot-swaps (model_id, status)
    metric.with_label_values(&["DQN", "success"]).inc();
    metric.with_label_values(&["PPO", "failure"]).inc();
    metric.with_label_values(&["TFT", "rollback"]).inc();
    metric
        .with_label_values(&["MAMBA-2", "success"])
        .inc_by(2.0);

    let collected = metric.collect();
    assert!(!collected.is_empty(), "Should have collected metrics");
}

#[test]
fn test_ab_test_assignments_counter() {
    let metric = &*AB_TEST_ASSIGNMENTS_TOTAL;
    let desc = metric.desc();

    assert!(desc.len() > 0, "A/B test assignments counter should exist");

    // Test A/B test assignments (test_id, group: control, treatment_a, treatment_b)
    metric
        .with_label_values(&["test_001", "control"])
        .inc_by(50.0);
    metric
        .with_label_values(&["test_001", "treatment_a"])
        .inc_by(25.0);
    metric
        .with_label_values(&["test_001", "treatment_b"])
        .inc_by(25.0);

    metric
        .with_label_values(&["test_002", "control"])
        .inc_by(40.0);
    metric
        .with_label_values(&["test_002", "treatment_a"])
        .inc_by(60.0);

    let collected = metric.collect();
    assert!(!collected.is_empty(), "Should have collected metrics");
}

#[test]
fn test_ab_test_metric_difference_gauge() {
    let metric = &*AB_TEST_METRIC_DIFF;
    let desc = metric.desc();

    assert!(
        desc.len() > 0,
        "A/B test metric difference gauge should exist"
    );

    // Test metric differences (test_id, metric_name, percentage difference)
    metric
        .with_label_values(&["test_001", "sharpe_ratio"])
        .set(15.5); // +15.5% improvement
    metric
        .with_label_values(&["test_001", "max_drawdown"])
        .set(-8.3); // -8.3% improvement
    metric.with_label_values(&["test_002", "win_rate"]).set(3.2); // +3.2% improvement
    metric
        .with_label_values(&["test_002", "profit_factor"])
        .set(22.7); // +22.7% improvement

    let collected = metric.collect();
    assert!(!collected.is_empty(), "Should have collected metrics");
}

#[test]
fn test_all_ensemble_metrics_registered() {
    // Verify all ensemble metrics initialize without panics
    let _ = &*ENSEMBLE_AGGREGATION_LATENCY_US;
    let _ = &*ENSEMBLE_CONFIDENCE;
    let _ = &*ENSEMBLE_DISAGREEMENT_RATE;
    let _ = &*ENSEMBLE_PREDICTIONS_TOTAL;
    let _ = &*ENSEMBLE_MODEL_WEIGHT;
    let _ = &*ENSEMBLE_HIGH_DISAGREEMENT_TOTAL;
    let _ = &*ENSEMBLE_MODEL_PNL_CONTRIBUTION;
    let _ = &*CHECKPOINT_SWAPS_TOTAL;
    let _ = &*AB_TEST_ASSIGNMENTS_TOTAL;
    let _ = &*AB_TEST_METRIC_DIFF;

    assert!(true, "All 10 ensemble metrics initialized successfully");
}

#[test]
fn test_ensemble_metrics_independence() {
    // Verify metrics for different symbols are tracked independently
    ENSEMBLE_CONFIDENCE.with_label_values(&["ES.FUT"]).set(0.85);
    ENSEMBLE_CONFIDENCE.with_label_values(&["NQ.FUT"]).set(0.92);

    ENSEMBLE_DISAGREEMENT_RATE
        .with_label_values(&["ES.FUT"])
        .set(0.15);
    ENSEMBLE_DISAGREEMENT_RATE
        .with_label_values(&["NQ.FUT"])
        .set(0.55);

    // Predictions should be tracked separately
    ENSEMBLE_PREDICTIONS_TOTAL
        .with_label_values(&["buy", "ES.FUT"])
        .inc();
    ENSEMBLE_PREDICTIONS_TOTAL
        .with_label_values(&["buy", "NQ.FUT"])
        .inc();

    let confidence_collected = ENSEMBLE_CONFIDENCE.collect();
    let disagreement_collected = ENSEMBLE_DISAGREEMENT_RATE.collect();
    let predictions_collected = ENSEMBLE_PREDICTIONS_TOTAL.collect();

    assert!(!confidence_collected.is_empty());
    assert!(!disagreement_collected.is_empty());
    assert!(!predictions_collected.is_empty());
}

#[test]
fn test_model_weight_distribution() {
    // Test realistic weight distributions across models
    let symbols = vec!["ES.FUT", "NQ.FUT", "ZN.FUT"];
    let models = vec!["DQN", "PPO", "MAMBA-2", "TFT"];

    for symbol in &symbols {
        // Simulate adaptive weights
        ENSEMBLE_MODEL_WEIGHT
            .with_label_values(&["DQN", symbol])
            .set(0.25);
        ENSEMBLE_MODEL_WEIGHT
            .with_label_values(&["PPO", symbol])
            .set(0.30);
        ENSEMBLE_MODEL_WEIGHT
            .with_label_values(&["MAMBA-2", symbol])
            .set(0.20);
        ENSEMBLE_MODEL_WEIGHT
            .with_label_values(&["TFT", symbol])
            .set(0.25);
    }

    let collected = ENSEMBLE_MODEL_WEIGHT.collect();
    assert!(
        !collected.is_empty(),
        "Model weights should be tracked per symbol"
    );
}

#[test]
fn test_aggregation_latency_buckets() {
    // Test latency observations in different buckets
    // Buckets: [1.0, 5.0, 10.0, 25.0, 50.0, 100.0]

    ENSEMBLE_AGGREGATION_LATENCY_US
        .with_label_values(&["weighted_average"])
        .observe(0.5); // < 1μs (very fast)
    ENSEMBLE_AGGREGATION_LATENCY_US
        .with_label_values(&["weighted_average"])
        .observe(7.5); // 5-10μs
    ENSEMBLE_AGGREGATION_LATENCY_US
        .with_label_values(&["weighted_average"])
        .observe(18.0); // 10-25μs
    ENSEMBLE_AGGREGATION_LATENCY_US
        .with_label_values(&["weighted_average"])
        .observe(35.0); // 25-50μs
    ENSEMBLE_AGGREGATION_LATENCY_US
        .with_label_values(&["weighted_average"])
        .observe(120.0); // > 100μs (alert threshold)

    let collected = ENSEMBLE_AGGREGATION_LATENCY_US.collect();
    assert!(
        !collected.is_empty(),
        "Histogram should capture all buckets"
    );
}

#[test]
fn test_high_disagreement_threshold() {
    // Test that high disagreement counter tracks events > 0.5 threshold

    // Simulate disagreement detection logic
    let disagreement_rates = vec![
        ("ES.FUT", 0.15), // Low - no alert
        ("NQ.FUT", 0.55), // High - alert ✓
        ("ZN.FUT", 0.05), // Low - no alert
        ("6E.FUT", 0.75), // Very high - alert ✓
    ];

    for (symbol, rate) in disagreement_rates {
        ENSEMBLE_DISAGREEMENT_RATE
            .with_label_values(&[symbol])
            .set(rate);

        if rate >= 0.5 {
            ENSEMBLE_HIGH_DISAGREEMENT_TOTAL
                .with_label_values(&[symbol, "0.5"])
                .inc();
        }
        if rate >= 0.7 {
            ENSEMBLE_HIGH_DISAGREEMENT_TOTAL
                .with_label_values(&[symbol, "0.7"])
                .inc();
        }
    }

    let collected = ENSEMBLE_HIGH_DISAGREEMENT_TOTAL.collect();
    assert!(
        !collected.is_empty(),
        "High disagreement events should be counted"
    );
}

#[test]
fn test_pnl_attribution_positive_and_negative() {
    // Test both profitable and unprofitable model contributions

    // Profitable models
    ENSEMBLE_MODEL_PNL_CONTRIBUTION
        .with_label_values(&["DQN", "ES.FUT"])
        .observe(250.50);
    ENSEMBLE_MODEL_PNL_CONTRIBUTION
        .with_label_values(&["PPO", "NQ.FUT"])
        .observe(180.25);

    // Unprofitable models (negative P&L)
    ENSEMBLE_MODEL_PNL_CONTRIBUTION
        .with_label_values(&["TFT", "ZN.FUT"])
        .observe(-75.30);
    ENSEMBLE_MODEL_PNL_CONTRIBUTION
        .with_label_values(&["MAMBA-2", "6E.FUT"])
        .observe(-150.00);

    let collected = ENSEMBLE_MODEL_PNL_CONTRIBUTION.collect();
    assert!(!collected.is_empty(), "Should track both gains and losses");
}

#[test]
fn test_checkpoint_swap_scenarios() {
    // Test different checkpoint swap outcomes (model_id, status)

    // Successful swaps
    CHECKPOINT_SWAPS_TOTAL
        .with_label_values(&["DQN", "success"])
        .inc();
    CHECKPOINT_SWAPS_TOTAL
        .with_label_values(&["PPO", "success"])
        .inc();

    // Failed swaps (model loading error)
    CHECKPOINT_SWAPS_TOTAL
        .with_label_values(&["TFT", "failure"])
        .inc();

    // Rollback swaps (performance degradation detected)
    CHECKPOINT_SWAPS_TOTAL
        .with_label_values(&["MAMBA-2", "rollback"])
        .inc();

    let collected = CHECKPOINT_SWAPS_TOTAL.collect();
    assert!(!collected.is_empty(), "Should track all swap outcomes");
}

#[test]
fn test_ab_test_balanced_assignment() {
    // Test balanced A/B test assignment (50/50 split)

    AB_TEST_ASSIGNMENTS_TOTAL
        .with_label_values(&["test_balanced", "control"])
        .inc_by(50.0);
    AB_TEST_ASSIGNMENTS_TOTAL
        .with_label_values(&["test_balanced", "treatment_a"])
        .inc_by(50.0);

    // Test imbalanced assignment (70/30 split)
    AB_TEST_ASSIGNMENTS_TOTAL
        .with_label_values(&["test_imbalanced", "control"])
        .inc_by(70.0);
    AB_TEST_ASSIGNMENTS_TOTAL
        .with_label_values(&["test_imbalanced", "treatment_a"])
        .inc_by(30.0);

    let collected = AB_TEST_ASSIGNMENTS_TOTAL.collect();
    assert!(
        !collected.is_empty(),
        "Should track assignment distributions"
    );
}