//! Test: DQN Hyperopt Movement Threshold Parameter
//!
//! Verifies that `movement_threshold` is properly exposed to the hyperopt search space
//! and sampled correctly during optimization.
//!
//! **Context**: The production DQN training script uses `--movement-threshold 0.02` but
//! this parameter was HARDCODED in the hyperopt adapter. It should be part of the search
//! space so hyperopt can optimize it.
//!
//! **Search Range**: [0.01, 0.05] (1% to 5% price movement)
//! **Default Value**: 0.02 (2% price movement)
//!
//! This test ensures:
//! 1. movement_threshold is sampled from [0.01, 0.05] range
//! 2. Different trials get different threshold values
//! 3. The value is correctly passed to the reward function

use ml::hyperopt::adapters::dqn::DQNParams;
use ml::hyperopt::traits::ParameterSpace;

#[test]
fn test_movement_threshold_in_search_space() {
    // Test that DQNParams includes movement_threshold in continuous bounds

    let bounds = DQNParams::continuous_bounds();

    // Expected: 6 parameters (learning_rate, batch_size, gamma, epsilon_decay, buffer_size, movement_threshold)
    assert_eq!(
        bounds.len(),
        6,
        "Expected 6 parameters including movement_threshold, got {}",
        bounds.len()
    );

    // movement_threshold should be at index 5 with bounds [0.01, 0.05]
    let (min_threshold, max_threshold) = bounds[5];

    assert_eq!(
        min_threshold, 0.01,
        "Minimum movement_threshold should be 0.01 (1%), got {}",
        min_threshold
    );

    assert_eq!(
        max_threshold, 0.05,
        "Maximum movement_threshold should be 0.05 (5%), got {}",
        max_threshold
    );
}

#[test]
fn test_movement_threshold_param_names() {
    // Verify parameter names include movement_threshold

    let names = DQNParams::param_names();

    assert_eq!(
        names.len(),
        6,
        "Expected 6 parameter names, got {}",
        names.len()
    );

    assert_eq!(
        names[5], "movement_threshold",
        "Parameter at index 5 should be 'movement_threshold', got '{}'",
        names[5]
    );
}

#[test]
fn test_movement_threshold_roundtrip() {
    // Test that movement_threshold survives to_continuous/from_continuous conversion

    let params = DQNParams {
        learning_rate: 0.0001,
        batch_size: 128,
        gamma: 0.99,
        epsilon_decay: 0.995,
        buffer_size: 100_000,
        movement_threshold: 0.03, // 3% threshold
    };

    let continuous = params.to_continuous();
    let recovered = DQNParams::from_continuous(&continuous).unwrap();

    assert_eq!(
        continuous.len(),
        6,
        "Continuous representation should have 6 values, got {}",
        continuous.len()
    );

    // movement_threshold should be at index 5 (linear scale, no transformation)
    assert_eq!(
        continuous[5], 0.03,
        "Continuous movement_threshold should be 0.03, got {}",
        continuous[5]
    );

    assert!(
        (recovered.movement_threshold - params.movement_threshold).abs() < 1e-10,
        "Recovered movement_threshold should match original: expected {}, got {}",
        params.movement_threshold,
        recovered.movement_threshold
    );
}

#[test]
fn test_movement_threshold_default_value() {
    // Test that default movement_threshold is 0.02 (2%)

    let params = DQNParams::default();

    assert_eq!(
        params.movement_threshold, 0.02,
        "Default movement_threshold should be 0.02 (2%), got {}",
        params.movement_threshold
    );
}

#[test]
fn test_movement_threshold_clamping() {
    // Test that movement_threshold is clamped to [0.01, 0.05] range

    // Test lower bound clamping
    let continuous_low = vec![
        0.0001_f64.ln(),  // learning_rate (log scale)
        128.0,            // batch_size
        0.99,             // gamma
        0.995_f64.ln(),   // epsilon_decay (log scale)
        100_000_f64.ln(), // buffer_size (log scale)
        0.005,            // movement_threshold (below min)
    ];

    let params_low = DQNParams::from_continuous(&continuous_low).unwrap();

    assert_eq!(
        params_low.movement_threshold, 0.01,
        "movement_threshold below 0.01 should be clamped to 0.01, got {}",
        params_low.movement_threshold
    );

    // Test upper bound clamping
    let continuous_high = vec![
        0.0001_f64.ln(),  // learning_rate (log scale)
        128.0,            // batch_size
        0.99,             // gamma
        0.995_f64.ln(),   // epsilon_decay (log scale)
        100_000_f64.ln(), // buffer_size (log scale)
        0.10,             // movement_threshold (above max)
    ];

    let params_high = DQNParams::from_continuous(&continuous_high).unwrap();

    assert_eq!(
        params_high.movement_threshold, 0.05,
        "movement_threshold above 0.05 should be clamped to 0.05, got {}",
        params_high.movement_threshold
    );
}

#[test]
fn test_movement_threshold_sampling_range() {
    // Test that different continuous values produce different movement_threshold values

    let test_cases = vec![
        (0.01, 0.01), // Min
        (0.02, 0.02), // Default
        (0.03, 0.03), // Mid-range
        (0.04, 0.04), // Upper-mid
        (0.05, 0.05), // Max
    ];

    for (continuous_value, expected_threshold) in test_cases {
        let continuous = vec![
            0.0001_f64.ln(),  // learning_rate
            128.0,            // batch_size
            0.99,             // gamma
            0.995_f64.ln(),   // epsilon_decay
            100_000_f64.ln(), // buffer_size
            continuous_value, // movement_threshold
        ];

        let params = DQNParams::from_continuous(&continuous).unwrap();

        assert_eq!(
            params.movement_threshold, expected_threshold,
            "Continuous value {} should produce movement_threshold {}, got {}",
            continuous_value, expected_threshold, params.movement_threshold
        );
    }
}

#[test]
fn test_movement_threshold_affects_different_trials() {
    // Simulate 5 different hyperopt trials with different movement_threshold values

    let trial_thresholds = vec![0.01, 0.02, 0.03, 0.04, 0.05];

    for (trial_num, expected_threshold) in trial_thresholds.iter().enumerate() {
        let continuous = vec![
            (-4.0 + trial_num as f64 * 0.1).ln().max(1e-5_f64.ln()), // learning_rate (varying)
            (128.0 + trial_num as f64 * 10.0),                       // batch_size (varying)
            0.99,                                                    // gamma (fixed)
            0.995_f64.ln(),                                          // epsilon_decay (fixed)
            100_000_f64.ln(),                                        // buffer_size (fixed)
            *expected_threshold,                                     // movement_threshold (varying)
        ];

        let params = DQNParams::from_continuous(&continuous).unwrap();

        assert_eq!(
            params.movement_threshold, *expected_threshold,
            "Trial {} should have movement_threshold {}, got {}",
            trial_num, expected_threshold, params.movement_threshold
        );
    }
}

#[test]
fn test_movement_threshold_integration() {
    // Test end-to-end: Create DQNParams, verify movement_threshold is accessible

    let params = DQNParams {
        learning_rate: 0.00001,
        batch_size: 207,
        gamma: 0.950,
        epsilon_decay: 0.99900,
        buffer_size: 162_739,
        movement_threshold: 0.025, // 2.5% (mid-range)
    };

    // Verify all fields are set correctly
    assert_eq!(params.learning_rate, 0.00001);
    assert_eq!(params.batch_size, 207);
    assert_eq!(params.gamma, 0.950);
    assert_eq!(params.epsilon_decay, 0.99900);
    assert_eq!(params.buffer_size, 162_739);
    assert_eq!(params.movement_threshold, 0.025);

    // Verify continuous conversion preserves value
    let continuous = params.to_continuous();
    let recovered = DQNParams::from_continuous(&continuous).unwrap();

    assert!(
        (recovered.movement_threshold - 0.025).abs() < 1e-10,
        "movement_threshold should survive roundtrip: expected 0.025, got {}",
        recovered.movement_threshold
    );
}