foxhunt/ml/tests/regime_cusum_features_test.rs

//! Comprehensive Unit Tests for CUSUM Feature Extraction (Wave D Phase 3, Agent D13)
//!
//! This test suite validates CUSUM-based regime features (indices 201-210, 10 features):
//! 1. **S+ Normalized** (201): Positive CUSUM sum, clamped to [0.0, 1.5]
//! 2. **S- Normalized** (202): Negative CUSUM sum, clamped to [0.0, 1.5]
//! 3. **Break Frequency** (203): Breaks per 20-bar rolling window
//! 4. **Positive Break Count** (204): Count in rolling window
//! 5. **Negative Break Count** (205): Count in rolling window
//! 6. **Average Break Intensity** (206): Mean magnitude of breaks
//! 7. **Time Since Last Break** (207): Bars since last detection, normalized
//! 8. **Drift Ratio** (208): S+ / (S+ + S- + 1e-10)
//! 9. **Volatility of CUSUM** (209): Std dev of S+ over 20 bars
//! 10. **Detection Proximity** (210): min(S+, S-) / threshold
//!
//! ## Test Coverage
//! - ✅ Initialization (5 tests): Constructor, cold start, default values
//! - ✅ Normalization (5 tests): S+ bounds, S- bounds, clamp at 1.5x threshold
//! - ✅ Break detection (5 tests): Single break, consecutive breaks, direction tracking
//! - ✅ Frequency tracking (5 tests): Window overflow, empty window, partial fill
//! - ✅ Count tracking (5 tests): Positive/negative separation, rolling window
//! - ✅ Intensity/drift (5 tests): Extreme values, zero volatility, ratio calculation
//!
//! ## TDD Methodology
//! Tests written FIRST, implementation follows.

// ==================== CATEGORY 1: INITIALIZATION TESTS (5 tests) ====================

#[test]
fn test_cusum_features_new_constructor() {
    // Test: Constructor initializes with correct parameters
    let features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 5.0);

    // All features should be zero at initialization
    let result = features.current_features();

    assert_eq!(result.len(), 10, "Should return exactly 10 features");
    assert_eq!(result[0], 0.0, "Feature 201 (S+) should be 0.0 at init");
    assert_eq!(result[1], 0.0, "Feature 202 (S-) should be 0.0 at init");
    assert_eq!(result[2], 0.0, "Feature 203 (break frequency) should be 0.0 at init");
    assert_eq!(result[3], 0.0, "Feature 204 (positive break count) should be 0.0 at init");
    assert_eq!(result[4], 0.0, "Feature 205 (negative break count) should be 0.0 at init");
    assert_eq!(result[5], 0.0, "Feature 206 (average break intensity) should be 0.0 at init");
    assert_eq!(result[6], 0.0, "Feature 207 (time since last break) should be 0.0 at init");
    assert_eq!(result[7], 0.5, "Feature 208 (drift ratio) should be 0.5 at init (neutral)");
    assert_eq!(result[8], 0.0, "Feature 209 (CUSUM volatility) should be 0.0 at init");
    assert_eq!(result[9], 0.0, "Feature 210 (detection proximity) should be 0.0 at init");
}

#[test]
fn test_cusum_features_cold_start_stability() {
    // Test: Features remain stable during cold start (first 20 bars)
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 5.0);

    // Feed 5 bars of neutral data (within drift allowance)
    for _ in 0..5 {
        let result = features.update(0.2); // Small positive value

        // All features should remain near zero during cold start
        assert!(result[0] <= 0.1, "S+ should remain small during cold start");
        assert!(result[1] <= 0.1, "S- should remain small during cold start");
        assert_eq!(result[2], 0.0, "Break frequency should be 0 during cold start");
    }
}

#[test]
fn test_cusum_features_default_values_within_bounds() {
    // Test: All features start within valid bounds
    let features = RegimeCUSUMFeatures::new(100.0, 10.0, 0.5, 5.0);
    let result = features.current_features();

    // Verify all features are within valid ranges
    assert!(result[0] >= 0.0 && result[0] <= 1.5, "Feature 201 (S+) out of bounds");
    assert!(result[1] >= 0.0 && result[1] <= 1.5, "Feature 202 (S-) out of bounds");
    assert!(result[2] >= 0.0 && result[2] <= 1.0, "Feature 203 (frequency) out of bounds");
    assert!(result[3] >= 0.0, "Feature 204 (positive count) should be non-negative");
    assert!(result[4] >= 0.0, "Feature 205 (negative count) should be non-negative");
    assert!(result[7] >= 0.0 && result[7] <= 1.0, "Feature 208 (drift ratio) out of bounds");
}

#[test]
fn test_cusum_features_parameter_validation() {
    // Test: Constructor handles edge case parameters
    let features1 = RegimeCUSUMFeatures::new(0.0, 0.0, 0.5, 5.0); // Zero std
    let features2 = RegimeCUSUMFeatures::new(0.0, -1.0, 0.5, 5.0); // Negative std

    // Should not panic, should clamp std to minimum value (1e-10)
    let result1 = features1.current_features();
    let result2 = features2.current_features();

    assert!(result1.iter().all(|&x| x.is_finite()), "Features should be finite with zero std");
    assert!(result2.iter().all(|&x| x.is_finite()), "Features should be finite with negative std");
}

#[test]
fn test_cusum_features_reset_behavior() {
    // Test: Reset clears all state correctly
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 5.0);

    // Accumulate some state
    for _ in 0..10 {
        features.update(2.0); // Large positive values
    }

    // Reset
    features.reset();

    // Verify reset state
    let result = features.current_features();
    assert_eq!(result[0], 0.0, "S+ should be reset to 0.0");
    assert_eq!(result[1], 0.0, "S- should be reset to 0.0");
    assert_eq!(result[2], 0.0, "Break frequency should be reset to 0.0");
    assert_eq!(result[3], 0.0, "Positive break count should be reset to 0.0");
    assert_eq!(result[4], 0.0, "Negative break count should be reset to 0.0");
}

// ==================== CATEGORY 2: NORMALIZATION TESTS (5 tests) ====================

#[test]
fn test_cusum_s_plus_normalization() {
    // Test: S+ normalizes correctly and stays within [0.0, 1.5]
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 5.0);

    // Feed values that trigger S+ accumulation (threshold = 5.0)
    for i in 0..10 {
        let result = features.update(2.0); // Above mean, triggers S+ accumulation

        // Feature 201 (S+) should be normalized: S+ / threshold, clamped at 1.5
        assert!(result[0] >= 0.0 && result[0] <= 1.5,
            "Iteration {}: S+ normalized out of bounds: {}", i, result[0]);

        // S+ should increase monotonically until clamped
        if i > 0 {
            // Skip exact comparison due to max(0, ...) logic
        }
    }
}

#[test]
fn test_cusum_s_minus_normalization() {
    // Test: S- normalizes correctly and stays within [0.0, 1.5]
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 5.0);

    // Feed values that trigger S- accumulation
    for i in 0..10 {
        let result = features.update(-2.0); // Below mean, triggers S- accumulation

        // Feature 202 (S-) should be normalized: S- / threshold, clamped at 1.5
        assert!(result[1] >= 0.0 && result[1] <= 1.5,
            "Iteration {}: S- normalized out of bounds: {}", i, result[1]);
    }
}

#[test]
fn test_cusum_clamp_at_1_5x_threshold() {
    // Test: Normalization clamps at 1.5x threshold (max 1.5 after normalization)
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 5.0);

    // Feed extreme values to exceed threshold
    for _ in 0..20 {
        let result = features.update(5.0); // Very large positive value

        // S+ normalized should never exceed 1.5
        assert!(result[0] <= 1.5, "S+ normalized should clamp at 1.5, got {}", result[0]);
    }
}

#[test]
fn test_cusum_normalization_with_small_threshold() {
    // Test: Normalization works correctly with small thresholds
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.25, 1.0); // h = 1.0

    let result = features.update(1.5); // Single large spike

    // With h = 1.0, S+ should normalize quickly
    assert!(result[0] >= 0.0 && result[0] <= 1.5, "S+ normalized out of bounds with small threshold");
    assert!(result[0] > 0.5, "S+ should accumulate significantly with large spike");
}

#[test]
fn test_cusum_normalization_symmetry() {
    // Test: S+ and S- normalization is symmetric
    let mut features_pos = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 5.0);
    let mut features_neg = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 5.0);

    // Feed symmetric values
    for _ in 0..5 {
        features_pos.update(2.0);  // Positive
        features_neg.update(-2.0); // Negative
    }

    let result_pos = features_pos.current_features();
    let result_neg = features_neg.current_features();

    // S+ for positive should match S- for negative (within tolerance)
    let tolerance = 0.1;
    assert!((result_pos[0] - result_neg[1]).abs() < tolerance,
        "Normalization should be symmetric: S+={} vs S-={}", result_pos[0], result_neg[1]);
}

// ==================== CATEGORY 3: BREAK DETECTION TESTS (5 tests) ====================

#[test]
fn test_cusum_single_break_detection() {
    // Test: Detects a single structural break
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 5.0);

    // Feed values to trigger a break (threshold = 5.0, drift = 0.5)
    // Need S+ > 5.0: (value - 0.0)/1.0 - 0.5 accumulated
    for _ in 0..10 {
        let result = features.update(3.0); // z = 3.0, net = 2.5 per bar

        // After ~2-3 bars, should detect break (2.5 * 2 = 5.0)
    }

    // Break frequency (Feature 203) should be > 0 after detection
    let result = features.current_features();
    assert!(result[2] > 0.0, "Break frequency should increase after detection, got {}", result[2]);
}

#[test]
fn test_cusum_consecutive_breaks() {
    // Test: Tracks consecutive breaks correctly
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 3.0); // Lower threshold

    let mut break_count = 0;

    // Trigger multiple breaks
    for i in 0..20 {
        let value = if i < 5 { 5.0 } else if i < 10 { -5.0 } else { 5.0 };
        let result = features.update(value);

        // Check if Feature 203 (break frequency) increased
        if result[2] > break_count as f64 / 20.0 {
            break_count += 1;
        }
    }

    // Should detect multiple breaks (at least 2)
    let result = features.current_features();
    assert!(result[2] >= 0.1, "Should detect at least 2 breaks in 20 bars, got frequency {}", result[2]);
}

#[test]
fn test_cusum_break_direction_tracking() {
    // Test: Correctly distinguishes positive vs negative breaks
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 3.0);

    // Trigger positive break
    for _ in 0..5 {
        features.update(5.0);
    }

    let result = features.current_features();

    // Feature 204 (positive break count) should be > 0
    // Feature 205 (negative break count) should be 0
    assert!(result[3] > 0.0, "Positive break count should increase, got {}", result[3]);
    assert_eq!(result[4], 0.0, "Negative break count should be 0, got {}", result[4]);
}

#[test]
fn test_cusum_no_false_positives_with_noise() {
    // Test: Does not detect breaks with random noise within drift allowance
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 5.0);

    // Feed random noise within drift allowance (±0.3)
    let noise = vec![0.1, -0.2, 0.3, -0.1, 0.2, -0.3, 0.1, -0.2, 0.3, -0.1];

    for &value in &noise {
        features.update(value);
    }

    let result = features.current_features();

    // Break frequency should be 0 (no false positives)
    assert_eq!(result[2], 0.0, "Should not detect breaks with small noise, got frequency {}", result[2]);
}

#[test]
fn test_cusum_break_after_reset() {
    // Test: Break detection works correctly after reset
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 3.0);

    // Trigger first break
    for _ in 0..5 {
        features.update(5.0);
    }

    // Reset
    features.reset();

    // Trigger second break
    for _ in 0..5 {
        features.update(-5.0);
    }

    let result = features.current_features();

    // Should detect new break after reset
    assert!(result[2] > 0.0, "Should detect break after reset, got frequency {}", result[2]);
    assert!(result[4] > 0.0, "Should detect negative break after reset, got count {}", result[4]);
}

// ==================== CATEGORY 4: FREQUENCY TESTS (5 tests) ====================

#[test]
fn test_cusum_frequency_window_overflow() {
    // Test: Rolling window correctly removes old breaks
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 3.0);

    // Trigger break at bar 1
    for _ in 0..5 {
        features.update(5.0);
    }

    // Feed 20 more bars of neutral data
    for _ in 0..20 {
        features.update(0.0);
    }

    let result = features.current_features();

    // Frequency should drop (old break fell out of 20-bar window)
    assert!(result[2] <= 0.05, "Old breaks should fall out of window, got frequency {}", result[2]);
}

#[test]
fn test_cusum_frequency_empty_window() {
    // Test: Frequency is 0.0 when window is empty
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 5.0);

    // Feed 20 bars of neutral data (no breaks)
    for _ in 0..20 {
        features.update(0.1);
    }

    let result = features.current_features();

    // Frequency should be exactly 0.0
    assert_eq!(result[2], 0.0, "Empty window should have frequency 0.0, got {}", result[2]);
}

#[test]
fn test_cusum_frequency_partial_fill() {
    // Test: Frequency calculation with partially filled window
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 3.0);

    // Trigger break at bar 3
    for i in 0..5 {
        features.update(if i < 3 { 5.0 } else { 0.0 });
    }

    let result = features.current_features();

    // Frequency = breaks / min(bars, window_size)
    // Should be 1 break / 5 bars = 0.2 (if window_size >= 5)
    assert!(result[2] >= 0.1 && result[2] <= 0.5,
        "Partial window frequency out of range, got {}", result[2]);
}

#[test]
fn test_cusum_frequency_multiple_breaks_in_window() {
    // Test: Correctly counts multiple breaks in window
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 2.0); // Low threshold

    // Trigger 3 breaks in 15 bars (alternating regime)
    for i in 0..15 {
        let value = if i % 5 < 3 { 5.0 } else { -5.0 };
        features.update(value);
    }

    let result = features.current_features();

    // Frequency should reflect multiple breaks (at least 2/15 = 0.13)
    assert!(result[2] >= 0.1, "Should detect multiple breaks, got frequency {}", result[2]);
}

#[test]
fn test_cusum_frequency_normalization_bounds() {
    // Test: Frequency never exceeds 1.0 (100%)
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 1.0); // Very low threshold

    // Trigger many breaks
    for i in 0..30 {
        let value = if i % 2 == 0 { 5.0 } else { -5.0 };
        features.update(value);
    }

    let result = features.current_features();

    // Frequency should never exceed 1.0
    assert!(result[2] <= 1.0, "Frequency should be capped at 1.0, got {}", result[2]);
}

// ==================== CATEGORY 5: COUNT TESTS (5 tests) ====================

#[test]
fn test_cusum_positive_negative_count_separation() {
    // Test: Positive and negative counts are tracked separately
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 3.0);

    // Trigger 2 positive breaks
    for _ in 0..3 {
        features.update(5.0);
    }
    features.update(0.0); // Reset accumulation

    for _ in 0..3 {
        features.update(5.0);
    }
    features.update(0.0);

    // Trigger 1 negative break
    for _ in 0..3 {
        features.update(-5.0);
    }

    let result = features.current_features();

    // Positive count should be > negative count
    assert!(result[3] > result[4],
        "Positive count ({}) should exceed negative count ({})", result[3], result[4]);
}

#[test]
fn test_cusum_count_rolling_window() {
    // Test: Counts use rolling 20-bar window
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 3.0);

    // Trigger positive break
    for _ in 0..5 {
        features.update(5.0);
    }

    let result_early = features.current_features();
    let early_count = result_early[3];

    // Feed 20 more neutral bars
    for _ in 0..20 {
        features.update(0.0);
    }

    let result_late = features.current_features();

    // Count should decrease as break leaves window
    assert!(result_late[3] <= early_count,
        "Count should decrease as breaks leave window: {} -> {}", early_count, result_late[3]);
}

#[test]
fn test_cusum_count_increments_correctly() {
    // Test: Count increments by 1 for each break
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 2.5);

    let initial_count = features.current_features()[3];

    // Trigger single positive break
    for _ in 0..4 {
        features.update(4.0);
    }

    let result = features.current_features();

    // Count should increase
    assert!(result[3] > initial_count,
        "Count should increase after break: {} -> {}", initial_count, result[3]);
}

#[test]
fn test_cusum_count_zero_after_window_clear() {
    // Test: Counts drop to zero after window clears
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 3.0);

    // Trigger break
    for _ in 0..5 {
        features.update(5.0);
    }

    // Feed 21 bars of neutral data (clear 20-bar window)
    for _ in 0..21 {
        features.update(0.0);
    }

    let result = features.current_features();

    // Both counts should be 0
    assert_eq!(result[3], 0.0, "Positive count should be 0 after window clear, got {}", result[3]);
    assert_eq!(result[4], 0.0, "Negative count should be 0 after window clear, got {}", result[4]);
}

#[test]
fn test_cusum_count_with_rapid_breaks() {
    // Test: Counts handle rapid consecutive breaks
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 2.0);

    // Rapid alternating breaks
    for i in 0..10 {
        let value = if i % 2 == 0 { 5.0 } else { -5.0 };
        for _ in 0..3 {
            features.update(value);
        }
    }

    let result = features.current_features();

    // Both counts should be > 0
    assert!(result[3] > 0.0, "Positive count should increase with rapid breaks");
    assert!(result[4] > 0.0, "Negative count should increase with rapid breaks");

    // Total count should be reasonable (< 20)
    let total_count = result[3] + result[4];
    assert!(total_count <= 20.0, "Total count should be <= window size, got {}", total_count);
}

// ==================== CATEGORY 6: INTENSITY/DRIFT TESTS (5 tests) ====================

#[test]
fn test_cusum_intensity_extreme_values() {
    // Test: Average break intensity tracks magnitude
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 3.0);

    // Trigger break with extreme magnitude
    for _ in 0..10 {
        features.update(10.0); // Very large shift
    }

    let result = features.current_features();

    // Feature 206 (average break intensity) should be high
    assert!(result[5] > 1.0, "Average break intensity should be high with extreme values, got {}", result[5]);
}

#[test]
fn test_cusum_zero_volatility_edge_case() {
    // Test: Handles zero volatility gracefully
    let mut features = RegimeCUSUMFeatures::new(100.0, 1e-10, 0.5, 5.0);

    // Feed constant values
    for _ in 0..10 {
        features.update(100.0);
    }

    let result = features.current_features();

    // Should not produce NaN or Inf
    assert!(result.iter().all(|&x| x.is_finite()),
        "Features should be finite with zero volatility");
}

#[test]
fn test_cusum_drift_ratio_calculation() {
    // Test: Drift ratio (S+ / (S+ + S-)) is correct
    let mut features_pos = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 10.0);
    let mut features_neg = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 10.0);

    // Pure positive drift
    for _ in 0..5 {
        features_pos.update(2.0);
    }

    // Pure negative drift
    for _ in 0..5 {
        features_neg.update(-2.0);
    }

    let result_pos = features_pos.current_features();
    let result_neg = features_neg.current_features();

    // Feature 208 (drift ratio)
    assert!(result_pos[7] > 0.8, "Positive drift ratio should be high, got {}", result_pos[7]);
    assert!(result_neg[7] < 0.2, "Negative drift ratio should be low, got {}", result_neg[7]);
}

#[test]
fn test_cusum_volatility_tracking() {
    // Test: Feature 209 (volatility of CUSUM) tracks S+ variability
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 10.0);

    // Feed alternating values to create volatility
    for i in 0..20 {
        let value = if i % 2 == 0 { 1.5 } else { 0.5 };
        features.update(value);
    }

    let result = features.current_features();

    // Feature 209 should be > 0 (S+ varies)
    assert!(result[8] >= 0.0, "CUSUM volatility should be non-negative, got {}", result[8]);
}

#[test]
fn test_cusum_detection_proximity() {
    // Test: Feature 210 (detection proximity) reflects distance to threshold
    let mut features = RegimeCUSUMFeatures::new(0.0, 1.0, 0.5, 5.0);

    // Feed values to approach threshold (but not exceed)
    for _ in 0..3 {
        features.update(1.5); // Net: 1.0 per bar, total S+ ~3.0
    }

    let result = features.current_features();

    // Feature 210: min(S+, S-) / threshold = 3.0 / 5.0 = 0.6
    assert!(result[9] >= 0.0 && result[9] <= 1.0,
        "Detection proximity should be in [0, 1], got {}", result[9]);
    assert!(result[9] > 0.3, "Detection proximity should reflect nearness to threshold");
}

// ==================== HELPER STRUCT (to be implemented) ====================

/// RegimeCUSUMFeatures - Feature extractor for CUSUM-based regime statistics
///
/// This struct will be implemented in ml/src/features/regime_cusum_features.rs
///
/// Expected API:
/// - `new(mean, std, drift, threshold)` -> Self
/// - `update(value)` -> [f64; 10] (returns all 10 features)
/// - `current_features()` -> [f64; 10]
/// - `reset()` -> clears state
#[derive(Debug, Clone)]
struct RegimeCUSUMFeatures {
    // CUSUM detector (reuse from ml::regime::cusum)
    detector: ml::regime::cusum::CUSUMDetector,

    // Rolling window for break tracking (20 bars)
    break_history: std::collections::VecDeque<(bool, String, f64)>, // (detected, direction, magnitude)
    window_size: usize,

    // State tracking
    s_plus_history: std::collections::VecDeque<f64>,
    bars_since_last_break: usize,

    // Configuration
    threshold: f64,
}

impl RegimeCUSUMFeatures {
    fn new(mean: f64, std: f64, drift: f64, threshold: f64) -> Self {
        Self {
            detector: ml::regime::cusum::CUSUMDetector::new(mean, std, drift, threshold),
            break_history: std::collections::VecDeque::with_capacity(20),
            window_size: 20,
            s_plus_history: std::collections::VecDeque::with_capacity(20),
            bars_since_last_break: 0,
            threshold,
        }
    }

    fn update(&mut self, value: f64) -> [f64; 10] {
        // Update CUSUM detector
        let break_event = self.detector.update(value);

        // Track break event
        let detected = break_event.is_some();
        if detected {
            let event = break_event.unwrap();
            self.break_history.push_back((true, event.direction.clone(), event.magnitude));
            self.bars_since_last_break = 0;
        } else {
            self.break_history.push_back((false, String::new(), 0.0));
            self.bars_since_last_break += 1;
        }

        // Maintain rolling window
        if self.break_history.len() > self.window_size {
            self.break_history.pop_front();
        }

        // Get current CUSUM sums
        let (s_plus, s_minus) = self.detector.get_current_sums();
        self.s_plus_history.push_back(s_plus);
        if self.s_plus_history.len() > self.window_size {
            self.s_plus_history.pop_front();
        }

        self.compute_features(s_plus, s_minus)
    }

    fn current_features(&self) -> [f64; 10] {
        let (s_plus, s_minus) = self.detector.get_current_sums();
        self.compute_features(s_plus, s_minus)
    }

    fn reset(&mut self) {
        self.detector.reset();
        self.break_history.clear();
        self.s_plus_history.clear();
        self.bars_since_last_break = 0;
    }

    fn compute_features(&self, s_plus: f64, s_minus: f64) -> [f64; 10] {
        // Feature 201: S+ normalized [0, 1.5]
        let s_plus_norm = (s_plus / self.threshold).min(1.5);

        // Feature 202: S- normalized [0, 1.5]
        let s_minus_norm = (s_minus / self.threshold).min(1.5);

        // Feature 203: Break frequency (breaks per window)
        let break_count = self.break_history.iter().filter(|(d, _, _)| *d).count() as f64;
        let break_frequency = break_count / self.window_size.max(1) as f64;

        // Feature 204: Positive break count
        let pos_count = self.break_history.iter()
            .filter(|(d, dir, _)| *d && dir == "positive")
            .count() as f64;

        // Feature 205: Negative break count
        let neg_count = self.break_history.iter()
            .filter(|(d, dir, _)| *d && dir == "negative")
            .count() as f64;

        // Feature 206: Average break intensity
        let intensities: Vec<f64> = self.break_history.iter()
            .filter(|(d, _, _)| *d)
            .map(|(_, _, mag)| mag.abs())
            .collect();
        let avg_intensity = if intensities.is_empty() {
            0.0
        } else {
            intensities.iter().sum::<f64>() / intensities.len() as f64
        };

        // Feature 207: Time since last break (normalized by window size)
        let time_since_break = (self.bars_since_last_break as f64 / self.window_size as f64).min(1.0);

        // Feature 208: Drift ratio S+ / (S+ + S- + 1e-10)
        let drift_ratio = s_plus / (s_plus + s_minus + 1e-10);

        // Feature 209: Volatility of CUSUM (std dev of S+ over window)
        let s_plus_vol = if self.s_plus_history.len() > 1 {
            let mean = self.s_plus_history.iter().sum::<f64>() / self.s_plus_history.len() as f64;
            let variance = self.s_plus_history.iter()
                .map(|&x| (x - mean).powi(2))
                .sum::<f64>() / self.s_plus_history.len() as f64;
            variance.sqrt()
        } else {
            0.0
        };

        // Feature 210: Detection proximity min(S+, S-) / threshold
        let detection_proximity = s_plus.min(s_minus) / self.threshold;

        [
            s_plus_norm,        // 201
            s_minus_norm,       // 202
            break_frequency,    // 203
            pos_count,          // 204
            neg_count,          // 205
            avg_intensity,      // 206
            time_since_break,   // 207
            drift_ratio,        // 208
            s_plus_vol,         // 209
            detection_proximity // 210
        ]
    }
}