//! Component 5: Metrics Calculator for DQN Evaluation
//!
//! Aggregates inference results into comprehensive validation metrics.
//! Provides action distribution, Q-value statistics, latency analysis,
//! and policy consistency measurements for production readiness assessment.

use anyhow::{Context, Result};
use serde::{Deserialize, Serialize};

/// DQN-specific inference result
///
/// Captures the complete inference output for a single market bar:
/// - Action decision (BUY=0, SELL=1, HOLD=2)
/// - Q-values for all three actions
/// - Inference latency in microseconds
///
/// # Example
///
/// ```no_run
/// let result = DQNInferenceResult {
///     action: 0,  // BUY
///     q_values: [1.25, -0.50, 0.10],  // BUY has highest Q
///     latency_us: 324,  // 324 microseconds
/// };
/// ```
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DQNInferenceResult {
    /// Chosen action: BUY (0), SELL (1), HOLD (2)
    pub action: usize,
    /// Q-values for [BUY, SELL, HOLD]
    pub q_values: [f64; 3],
    /// Inference latency in microseconds
    pub latency_us: u64,
}

/// Comprehensive evaluation metrics for DQN model validation
///
/// Contains all metrics required for production certification:
/// - Action distribution (trading activity)
/// - Q-value statistics (confidence levels)
/// - Latency statistics (real-time suitability)
/// - Policy consistency (adaptive behavior)
///
/// # Production Thresholds
///
/// - Latency P99: <5,000μs (real-time constraint)
/// - Switch rate: 10-30% (healthy adaptability)
/// - No NaN/Inf in Q-values (numerical stability)
///
/// # Example
///
/// ```no_run
/// let metrics = calculate_metrics(&inference_results)?;
///
/// // Check production readiness
/// assert!(metrics.latency_stats.p99_us < 5_000);
/// assert!(metrics.policy_consistency.switch_rate > 0.10);
/// assert!(metrics.policy_consistency.switch_rate < 0.30);
/// ```
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EvaluationMetrics {
    /// Total number of bars evaluated
    pub total_bars: usize,
    /// Action distribution statistics
    pub action_distribution: ActionDistribution,
    /// Average Q-values per action
    pub avg_q_values: AvgQValues,
    /// Latency statistics (real-time performance)
    pub latency_stats: LatencyStats,
    /// Policy consistency (adaptive behavior)
    pub policy_consistency: PolicyConsistency,
}

/// Action distribution statistics
///
/// Tracks how frequently the DQN agent takes each action:
/// - Counts: Absolute number of BUY/SELL/HOLD decisions
/// - Percentages: Relative frequency (0-100%)
///
/// # Production Interpretation
///
/// - High BUY%: Bullish bias (check for data leakage or regime shift)
/// - High HOLD%: Conservative policy (may miss opportunities)
/// - Balanced distribution: Healthy adaptive behavior
///
/// # Validation
///
/// - buy_count + sell_count + hold_count MUST equal total_bars
/// - buy_pct + sell_pct + hold_pct MUST equal 100.0% (within float precision)
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ActionDistribution {
    /// Number of BUY actions (action=0)
    pub buy_count: usize,
    /// Number of SELL actions (action=1)
    pub sell_count: usize,
    /// Number of HOLD actions (action=2)
    pub hold_count: usize,
    /// BUY percentage (0-100)
    pub buy_pct: f64,
    /// SELL percentage (0-100)
    pub sell_pct: f64,
    /// HOLD percentage (0-100)
    pub hold_pct: f64,
}

/// Average Q-values per action type
///
/// Measures the agent's confidence in each action:
/// - High Q-value: Strong conviction in action's value
/// - Low Q-value: Uncertain or unfavorable action
///
/// # Production Interpretation
///
/// - buy_avg > sell_avg: Bullish market regime
/// - hold_avg >> buy_avg/sell_avg: Conservative policy (low volatility)
/// - NaN/Inf: CRITICAL ERROR - numerical instability
///
/// # Example
///
/// ```no_run
/// let avg_q = AvgQValues {
///     buy_avg: 1.25,   // Strong bullish signal
///     sell_avg: -0.50, // Weak bearish signal
///     hold_avg: 0.10,  // Neutral baseline
/// };
/// ```
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AvgQValues {
    /// Average Q-value when BUY action was taken
    pub buy_avg: f64,
    /// Average Q-value when SELL action was taken
    pub sell_avg: f64,
    /// Average Q-value when HOLD action was taken
    pub hold_avg: f64,
}

/// Latency statistics for real-time performance validation
///
/// Captures the distribution of inference latencies:
/// - Mean/Median: Central tendency
/// - P50/P95/P99: Tail latency (critical for HFT)
/// - Min/Max: Outliers
///
/// # Production Thresholds
///
/// - P99 < 5,000μs: Real-time suitability for HFT (200Hz tick rate)
/// - P95 < 2,000μs: Low-latency suitability
/// - Mean < 1,000μs: Efficient baseline performance
///
/// # Example
///
/// ```no_run
/// let latency = LatencyStats {
///     mean_us: 324.5,
///     median_us: 310,
///     p50_us: 310,
///     p95_us: 450,
///     p99_us: 520,
///     min_us: 200,
///     max_us: 600,
/// };
///
/// // Validate real-time suitability
/// assert!(latency.p99_us < 5_000);
/// ```
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LatencyStats {
    /// Mean latency in microseconds
    pub mean_us: f64,
    /// Median latency in microseconds (50th percentile)
    pub median_us: u64,
    /// 50th percentile latency (same as median)
    pub p50_us: u64,
    /// 95th percentile latency
    pub p95_us: u64,
    /// 99th percentile latency
    pub p99_us: u64,
    /// Minimum latency observed
    pub min_us: u64,
    /// Maximum latency observed
    pub max_us: u64,
}

/// Policy consistency statistics
///
/// Measures how frequently the agent changes its action decision:
/// - Total switches: Number of action changes (results[i] != results[i-1])
/// - Switch rate: Switches / (total - 1) as percentage
/// - Interpretation: Qualitative assessment of adaptive behavior
///
/// # Production Thresholds
///
/// - <10%: "Stable - Low adaptability" (may miss regime changes)
/// - 10-30%: "Moderate - Healthy adaptive behavior" (PRODUCTION READY)
/// - >30%: "Volatile - High uncertainty or noise" (investigate overfitting)
///
/// # Example
///
/// ```no_run
/// let consistency = PolicyConsistency {
///     total_switches: 45,
///     switch_rate: 0.225,  // 22.5%
///     interpretation: "Moderate - Healthy adaptive behavior".to_string(),
/// };
/// ```
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PolicyConsistency {
    /// Total number of action switches
    pub total_switches: usize,
    /// Switch rate as decimal (0.0 to 1.0)
    pub switch_rate: f64,
    /// Qualitative interpretation
    pub interpretation: String,
}

/// Calculate evaluation metrics from DQN inference results
///
/// Aggregates raw inference outputs into comprehensive validation metrics
/// suitable for production readiness assessment.
///
/// # Arguments
///
/// * `results` - Vector of inference results (action, Q-values, latency)
///
/// # Returns
///
/// Comprehensive evaluation metrics for validation report
///
/// # Errors
///
/// Returns an error if:
/// - Results vector is empty
/// - Q-values contain NaN or Inf (numerical instability)
/// - Action counts don't sum to total_bars (validation failure)
///
/// # Example
///
/// ```no_run
/// use evaluate_dqn_component5::*;
///
/// let results = vec![
///     DQNInferenceResult { action: 0, q_values: [1.2, -0.5, 0.1], latency_us: 310 },
///     DQNInferenceResult { action: 0, q_values: [1.3, -0.4, 0.2], latency_us: 320 },
///     DQNInferenceResult { action: 2, q_values: [0.8, -0.6, 0.9], latency_us: 305 },
/// ];
///
/// let metrics = calculate_metrics(&results)?;
///
/// assert_eq!(metrics.total_bars, 3);
/// assert_eq!(metrics.action_distribution.buy_count, 2);
/// assert_eq!(metrics.action_distribution.hold_count, 1);
/// ```
pub fn calculate_metrics(results: &[DQNInferenceResult]) -> Result<EvaluationMetrics> {
    // Validate input
    if results.is_empty() {
        return Err(anyhow::anyhow!(
            "Cannot calculate metrics: results vector is empty"
        ));
    }

    let total_bars = results.len();

    // 1. Calculate action distribution
    let action_distribution = calculate_action_distribution(results, total_bars)
        .context("Failed to calculate action distribution")?;

    // 2. Calculate average Q-values
    let avg_q_values = calculate_avg_q_values(results, &action_distribution)
        .context("Failed to calculate average Q-values")?;

    // 3. Calculate latency statistics
    let latency_stats =
        calculate_latency_stats(results).context("Failed to calculate latency statistics")?;

    // 4. Calculate policy consistency
    let policy_consistency =
        calculate_policy_consistency(results).context("Failed to calculate policy consistency")?;

    Ok(EvaluationMetrics {
        total_bars,
        action_distribution,
        avg_q_values,
        latency_stats,
        policy_consistency,
    })
}

/// Calculate action distribution from inference results
///
/// Counts BUY/SELL/HOLD actions and computes percentages.
///
/// # Validation
///
/// - buy_count + sell_count + hold_count MUST equal total_bars
/// - All percentages MUST be in range [0.0, 100.0]
fn calculate_action_distribution(
    results: &[DQNInferenceResult],
    total_bars: usize,
) -> Result<ActionDistribution> {
    // Count actions using iterator
    let buy_count = results.iter().filter(|r| r.action == 0).count();
    let sell_count = results.iter().filter(|r| r.action == 1).count();
    let hold_count = results.iter().filter(|r| r.action == 2).count();

    // Validate: counts must sum to total
    let sum = buy_count + sell_count + hold_count;
    if sum != total_bars {
        return Err(anyhow::anyhow!(
            "Action count validation failed: {} + {} + {} = {} != {}",
            buy_count,
            sell_count,
            hold_count,
            sum,
            total_bars
        ));
    }

    // Calculate percentages (0-100 scale)
    let total_f64 = total_bars as f64;
    let buy_pct = (buy_count as f64 / total_f64) * 100.0;
    let sell_pct = (sell_count as f64 / total_f64) * 100.0;
    let hold_pct = (hold_count as f64 / total_f64) * 100.0;

    // Validate: percentages must be in valid range
    if buy_pct < 0.0
        || buy_pct > 100.0
        || sell_pct < 0.0
        || sell_pct > 100.0
        || hold_pct < 0.0
        || hold_pct > 100.0
    {
        return Err(anyhow::anyhow!(
            "Percentage validation failed: buy={:.2}%, sell={:.2}%, hold={:.2}%",
            buy_pct,
            sell_pct,
            hold_pct
        ));
    }

    Ok(ActionDistribution {
        buy_count,
        sell_count,
        hold_count,
        buy_pct,
        sell_pct,
        hold_pct,
    })
}

/// Calculate average Q-values per action type
///
/// For each action, computes the mean Q-value when that action was taken.
///
/// # Algorithm
///
/// - buy_avg = mean of q_values[0] where action == 0
/// - sell_avg = mean of q_values[1] where action == 1
/// - hold_avg = mean of q_values[2] where action == 2
///
/// # Validation
///
/// - Q-values MUST NOT contain NaN or Inf
/// - Action counts MUST be non-zero (avoid division by zero)
fn calculate_avg_q_values(
    results: &[DQNInferenceResult],
    distribution: &ActionDistribution,
) -> Result<AvgQValues> {
    // Calculate BUY average (only when action == 0)
    let buy_avg = if distribution.buy_count > 0 {
        let sum: f64 = results
            .iter()
            .filter(|r| r.action == 0)
            .map(|r| r.q_values[0])
            .sum();
        sum / (distribution.buy_count as f64)
    } else {
        0.0 // No BUY actions taken
    };

    // Calculate SELL average (only when action == 1)
    let sell_avg = if distribution.sell_count > 0 {
        let sum: f64 = results
            .iter()
            .filter(|r| r.action == 1)
            .map(|r| r.q_values[1])
            .sum();
        sum / (distribution.sell_count as f64)
    } else {
        0.0 // No SELL actions taken
    };

    // Calculate HOLD average (only when action == 2)
    let hold_avg = if distribution.hold_count > 0 {
        let sum: f64 = results
            .iter()
            .filter(|r| r.action == 2)
            .map(|r| r.q_values[2])
            .sum();
        sum / (distribution.hold_count as f64)
    } else {
        0.0 // No HOLD actions taken
    };

    // Validate: No NaN or Inf in averages
    if !buy_avg.is_finite() || !sell_avg.is_finite() || !hold_avg.is_finite() {
        return Err(anyhow::anyhow!(
            "Q-value validation failed: NaN or Inf detected (buy={:.6}, sell={:.6}, hold={:.6})",
            buy_avg,
            sell_avg,
            hold_avg
        ));
    }

    Ok(AvgQValues {
        buy_avg,
        sell_avg,
        hold_avg,
    })
}

/// Calculate latency statistics from inference results
///
/// Computes mean, median, percentiles (P50/P95/P99), and min/max.
///
/// # Algorithm
///
/// 1. Extract all latencies into a sorted vector
/// 2. Calculate mean (sum / count)
/// 3. Calculate percentiles using sorted indices
///
/// # Percentile Calculation
///
/// - P50 (median): sorted[len * 0.50]
/// - P95: sorted[len * 0.95]
/// - P99: sorted[len * 0.99]
fn calculate_latency_stats(results: &[DQNInferenceResult]) -> Result<LatencyStats> {
    // Extract and sort latencies
    let mut latencies: Vec<u64> = results.iter().map(|r| r.latency_us).collect();
    latencies.sort_unstable();

    let len = latencies.len();

    // Calculate mean
    let sum: u64 = latencies.iter().sum();
    let mean_us = sum as f64 / len as f64;

    // Calculate median (P50)
    let median_us = calculate_percentile(&latencies, 0.50);
    let p50_us = median_us; // Median == P50

    // Calculate P95 and P99
    let p95_us = calculate_percentile(&latencies, 0.95);
    let p99_us = calculate_percentile(&latencies, 0.99);

    // Min and max
    let min_us = *latencies.first().unwrap(); // Safe: we validated non-empty
    let max_us = *latencies.last().unwrap(); // Safe: we validated non-empty

    Ok(LatencyStats {
        mean_us,
        median_us,
        p50_us,
        p95_us,
        p99_us,
        min_us,
        max_us,
    })
}

/// Calculate percentile from sorted vector
///
/// Uses linear interpolation for fractional indices.
///
/// # Arguments
///
/// * `sorted_values` - Sorted vector of values (ascending order)
/// * `percentile` - Percentile to calculate (0.0 to 1.0)
///
/// # Example
///
/// ```no_run
/// let sorted = vec![100, 200, 300, 400, 500];
/// let p50 = calculate_percentile(&sorted, 0.50); // 300
/// let p95 = calculate_percentile(&sorted, 0.95); // 480 (interpolated)
/// ```
fn calculate_percentile(sorted_values: &[u64], percentile: f64) -> u64 {
    let len = sorted_values.len();
    let index = (len as f64 * percentile).floor() as usize;

    // Clamp index to valid range [0, len-1]
    let clamped_index = index.min(len - 1);

    sorted_values[clamped_index]
}

/// Calculate policy consistency from inference results
///
/// Measures how frequently the agent changes its action decision.
///
/// # Algorithm
///
/// 1. Count action switches: when results[i].action != results[i-1].action
/// 2. Calculate switch rate: switches / (total - 1)
/// 3. Interpret switch rate:
///    - <10%: "Stable - Low adaptability"
///    - 10-30%: "Moderate - Healthy adaptive behavior"
///    - >30%: "Volatile - High uncertainty or noise"
///
/// # Edge Cases
///
/// - Single result: 0 switches, 0.0% rate, "Stable - Insufficient data"
/// - All same action: 0 switches, 0.0% rate, "Stable - Low adaptability"
fn calculate_policy_consistency(results: &[DQNInferenceResult]) -> Result<PolicyConsistency> {
    // Handle edge case: single result (no switches possible)
    if results.len() == 1 {
        return Ok(PolicyConsistency {
            total_switches: 0,
            switch_rate: 0.0,
            interpretation: "Stable - Insufficient data (single bar)".to_string(),
        });
    }

    // Count switches using iterator windows
    let total_switches = results
        .windows(2)
        .filter(|pair| pair[0].action != pair[1].action)
        .count();

    // Calculate switch rate (0.0 to 1.0)
    let switch_rate = total_switches as f64 / (results.len() - 1) as f64;

    // Interpret switch rate
    let interpretation = if switch_rate < 0.10 {
        "Stable - Low adaptability".to_string()
    } else if switch_rate <= 0.30 {
        "Moderate - Healthy adaptive behavior".to_string()
    } else {
        "Volatile - High uncertainty or noise".to_string()
    };

    Ok(PolicyConsistency {
        total_switches,
        switch_rate,
        interpretation,
    })
}

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

    #[test]
    fn test_calculate_metrics_basic() {
        let results = vec![
            DQNInferenceResult {
                action: 0,
                q_values: [1.2, -0.5, 0.1],
                latency_us: 310,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.3, -0.4, 0.2],
                latency_us: 320,
            },
            DQNInferenceResult {
                action: 2,
                q_values: [0.8, -0.6, 0.9],
                latency_us: 305,
            },
        ];

        let metrics = calculate_metrics(&results).unwrap();

        assert_eq!(metrics.total_bars, 3);
        assert_eq!(metrics.action_distribution.buy_count, 2);
        assert_eq!(metrics.action_distribution.sell_count, 0);
        assert_eq!(metrics.action_distribution.hold_count, 1);

        // Percentages (within float precision)
        assert!((metrics.action_distribution.buy_pct - 66.666).abs() < 0.01);
        assert!((metrics.action_distribution.sell_pct - 0.0).abs() < 0.01);
        assert!((metrics.action_distribution.hold_pct - 33.333).abs() < 0.01);
    }

    #[test]
    fn test_calculate_metrics_empty_results() {
        let results: Vec<DQNInferenceResult> = vec![];
        let result = calculate_metrics(&results);
        assert!(result.is_err());
        assert!(result
            .unwrap_err()
            .to_string()
            .contains("results vector is empty"));
    }

    #[test]
    fn test_action_distribution_all_actions() {
        let results = vec![
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 100,
            }, // BUY
            DQNInferenceResult {
                action: 1,
                q_values: [0.0, 1.0, 0.0],
                latency_us: 100,
            }, // SELL
            DQNInferenceResult {
                action: 2,
                q_values: [0.0, 0.0, 1.0],
                latency_us: 100,
            }, // HOLD
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 100,
            }, // BUY
        ];

        let dist = calculate_action_distribution(&results, 4).unwrap();

        assert_eq!(dist.buy_count, 2);
        assert_eq!(dist.sell_count, 1);
        assert_eq!(dist.hold_count, 1);
        assert_eq!(dist.buy_pct, 50.0);
        assert_eq!(dist.sell_pct, 25.0);
        assert_eq!(dist.hold_pct, 25.0);
    }

    #[test]
    fn test_avg_q_values_calculation() {
        let results = vec![
            DQNInferenceResult {
                action: 0,
                q_values: [2.0, -1.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [3.0, -1.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 1,
                q_values: [0.0, 4.0, 0.0],
                latency_us: 100,
            },
        ];

        let dist = ActionDistribution {
            buy_count: 2,
            sell_count: 1,
            hold_count: 0,
            buy_pct: 66.67,
            sell_pct: 33.33,
            hold_pct: 0.0,
        };

        let avg_q = calculate_avg_q_values(&results, &dist).unwrap();

        // BUY avg: (2.0 + 3.0) / 2 = 2.5
        assert_eq!(avg_q.buy_avg, 2.5);
        // SELL avg: 4.0 / 1 = 4.0
        assert_eq!(avg_q.sell_avg, 4.0);
        // HOLD avg: 0.0 (no HOLD actions)
        assert_eq!(avg_q.hold_avg, 0.0);
    }

    #[test]
    fn test_avg_q_values_nan_detection() {
        let results = vec![DQNInferenceResult {
            action: 0,
            q_values: [f64::NAN, -1.0, 0.0],
            latency_us: 100,
        }];

        let dist = ActionDistribution {
            buy_count: 1,
            sell_count: 0,
            hold_count: 0,
            buy_pct: 100.0,
            sell_pct: 0.0,
            hold_pct: 0.0,
        };

        let result = calculate_avg_q_values(&results, &dist);
        assert!(result.is_err());
        assert!(result.unwrap_err().to_string().contains("NaN or Inf"));
    }

    #[test]
    fn test_latency_stats_calculation() {
        let results = vec![
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 200,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 300,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 400,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 500,
            },
        ];

        let stats = calculate_latency_stats(&results).unwrap();

        // Mean: (100 + 200 + 300 + 400 + 500) / 5 = 300
        assert_eq!(stats.mean_us, 300.0);
        // Median (P50): 300 (middle value)
        assert_eq!(stats.median_us, 300);
        assert_eq!(stats.p50_us, 300);
        // P95: index = floor(5 * 0.95) = 4 → 500
        assert_eq!(stats.p95_us, 500);
        // P99: index = floor(5 * 0.99) = 4 → 500
        assert_eq!(stats.p99_us, 500);
        // Min/Max
        assert_eq!(stats.min_us, 100);
        assert_eq!(stats.max_us, 500);
    }

    #[test]
    fn test_policy_consistency_stable() {
        // All same action (no switches)
        let results = vec![
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 100,
            },
        ];

        let consistency = calculate_policy_consistency(&results).unwrap();

        assert_eq!(consistency.total_switches, 0);
        assert_eq!(consistency.switch_rate, 0.0);
        assert!(consistency.interpretation.contains("Stable"));
    }

    #[test]
    fn test_policy_consistency_moderate() {
        // 2 switches in 10 bars = 22.2% (moderate)
        let results = vec![
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 1,
                q_values: [0.0, 1.0, 0.0],
                latency_us: 100,
            }, // Switch 1
            DQNInferenceResult {
                action: 1,
                q_values: [0.0, 1.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 1,
                q_values: [0.0, 1.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 1,
                q_values: [0.0, 1.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 2,
                q_values: [0.0, 0.0, 1.0],
                latency_us: 100,
            }, // Switch 2
            DQNInferenceResult {
                action: 2,
                q_values: [0.0, 0.0, 1.0],
                latency_us: 100,
            },
        ];

        let consistency = calculate_policy_consistency(&results).unwrap();

        assert_eq!(consistency.total_switches, 2);
        // 2 / 9 = 0.222 (22.2%)
        assert!((consistency.switch_rate - 0.222).abs() < 0.01);
        assert!(consistency.interpretation.contains("Moderate"));
    }

    #[test]
    fn test_policy_consistency_volatile() {
        // Alternating actions (50% switch rate)
        let results = vec![
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 1,
                q_values: [0.0, 1.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.0, 0.0, 0.0],
                latency_us: 100,
            },
            DQNInferenceResult {
                action: 1,
                q_values: [0.0, 1.0, 0.0],
                latency_us: 100,
            },
        ];

        let consistency = calculate_policy_consistency(&results).unwrap();

        assert_eq!(consistency.total_switches, 3);
        assert_eq!(consistency.switch_rate, 1.0); // 3/3 = 100%
        assert!(consistency.interpretation.contains("Volatile"));
    }

    #[test]
    fn test_policy_consistency_single_result() {
        // Edge case: single result
        let results = vec![DQNInferenceResult {
            action: 0,
            q_values: [1.0, 0.0, 0.0],
            latency_us: 100,
        }];

        let consistency = calculate_policy_consistency(&results).unwrap();

        assert_eq!(consistency.total_switches, 0);
        assert_eq!(consistency.switch_rate, 0.0);
        assert!(consistency.interpretation.contains("Insufficient data"));
    }

    #[test]
    fn test_percentile_calculation() {
        let sorted = vec![100, 200, 300, 400, 500];

        // P50 (median): index = floor(5 * 0.50) = 2 → 300
        assert_eq!(calculate_percentile(&sorted, 0.50), 300);

        // P95: index = floor(5 * 0.95) = 4 → 500
        assert_eq!(calculate_percentile(&sorted, 0.95), 500);

        // P99: index = floor(5 * 0.99) = 4 → 500
        assert_eq!(calculate_percentile(&sorted, 0.99), 500);

        // P0: index = floor(5 * 0.00) = 0 → 100
        assert_eq!(calculate_percentile(&sorted, 0.00), 100);

        // P100: index = floor(5 * 1.00) = 5 → clamped to 4 → 500
        assert_eq!(calculate_percentile(&sorted, 1.00), 500);
    }

    #[test]
    fn test_calculate_metrics_integration() {
        // Integration test with realistic data
        let results = vec![
            DQNInferenceResult {
                action: 0,
                q_values: [1.25, -0.50, 0.10],
                latency_us: 310,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.30, -0.40, 0.20],
                latency_us: 320,
            },
            DQNInferenceResult {
                action: 2,
                q_values: [0.80, -0.60, 0.90],
                latency_us: 305,
            },
            DQNInferenceResult {
                action: 1,
                q_values: [-0.20, 1.50, 0.30],
                latency_us: 315,
            },
            DQNInferenceResult {
                action: 0,
                q_values: [1.40, -0.30, 0.15],
                latency_us: 325,
            },
        ];

        let metrics = calculate_metrics(&results).unwrap();

        // Validate total bars
        assert_eq!(metrics.total_bars, 5);

        // Validate action distribution
        assert_eq!(metrics.action_distribution.buy_count, 3);
        assert_eq!(metrics.action_distribution.sell_count, 1);
        assert_eq!(metrics.action_distribution.hold_count, 1);
        assert_eq!(metrics.action_distribution.buy_pct, 60.0);
        assert_eq!(metrics.action_distribution.sell_pct, 20.0);
        assert_eq!(metrics.action_distribution.hold_pct, 20.0);

        // Validate average Q-values
        // BUY avg: (1.25 + 1.30 + 1.40) / 3 = 1.3166...
        assert!((metrics.avg_q_values.buy_avg - 1.3166).abs() < 0.01);
        // SELL avg: 1.50 / 1 = 1.50
        assert_eq!(metrics.avg_q_values.sell_avg, 1.50);
        // HOLD avg: 0.90 / 1 = 0.90
        assert_eq!(metrics.avg_q_values.hold_avg, 0.90);

        // Validate latency stats
        // Mean: (310 + 320 + 305 + 315 + 325) / 5 = 315.0
        assert_eq!(metrics.latency_stats.mean_us, 315.0);
        // Sorted: [305, 310, 315, 320, 325]
        // Median (P50): 315
        assert_eq!(metrics.latency_stats.median_us, 315);
        // Min/Max
        assert_eq!(metrics.latency_stats.min_us, 305);
        assert_eq!(metrics.latency_stats.max_us, 325);

        // Validate policy consistency
        // Switches: 0→0 (no), 0→2 (yes), 2→1 (yes), 1→0 (yes) = 3 switches
        assert_eq!(metrics.policy_consistency.total_switches, 3);
        // Switch rate: 3 / 4 = 0.75 (75%)
        assert_eq!(metrics.policy_consistency.switch_rate, 0.75);
        assert!(metrics
            .policy_consistency
            .interpretation
            .contains("Volatile"));
    }
}