// Bug #20: Portfolio value normalization test
// Tests that portfolio value is normalized by initial_capital
// to prevent 100,000x feature imbalance

use anyhow::Result;
use ml::dqn::portfolio_tracker::PortfolioTracker;

#[test]
fn test_portfolio_value_normalized_to_baseline() -> Result<()> {
    // Bug #20: Portfolio value should be normalized to ~1.0 baseline
    let initial_capital = 100_000.0;
    let avg_spread = 0.0001;
    let cash_reserve_percent = 0.0;

    let tracker = PortfolioTracker::new(initial_capital, avg_spread, cash_reserve_percent);

    // At start, portfolio value = initial_capital
    let features = tracker.get_portfolio_features(4000.0);

    // Bug #20: Feature should be 1.0 (normalized), NOT 100,000
    assert_eq!(features.len(), 3, "Should have 3 portfolio features");

    let portfolio_feature = features[0];

    // After fix, this should be ~1.0 (normalized by initial_capital)
    // Before fix, this would be 100,000.0 (raw value)
    assert!(
        (portfolio_feature - 1.0).abs() < 0.01,
        "Portfolio value should be normalized to 1.0, got: {}",
        portfolio_feature
    );

    println!("Portfolio value normalized correctly: {}", portfolio_feature);

    Ok(())
}

#[test]
fn test_all_portfolio_features_similar_scale() -> Result<()> {
    // Bug #20: All portfolio features should be in similar scale
    // No 100,000x imbalance
    let initial_capital = 100_000.0;
    let avg_spread = 0.0001;
    let cash_reserve_percent = 0.0;

    let tracker = PortfolioTracker::new(initial_capital, avg_spread, cash_reserve_percent);

    let features = tracker.get_portfolio_features(4000.0);

    // Check all features are in reasonable scale (NOT 100,000x difference)
    for (i, &feature) in features.iter().enumerate() {
        assert!(
            feature.abs() < 10.0,
            "Feature {} should be in reasonable scale, got: {}",
            i,
            feature
        );
    }

    println!("All portfolio features in similar scale: {:?}", features);

    Ok(())
}

#[test]
fn test_feature_scale_consistency() -> Result<()> {
    // Bug #20: Verify portfolio features don't dominate other features
    // The key test is that portfolio value is NOT 100,000 (raw value)
    let initial_capital = 100_000.0;
    let avg_spread = 0.0001;
    let cash_reserve_percent = 0.0;

    let tracker = PortfolioTracker::new(initial_capital, avg_spread, cash_reserve_percent);

    let features = tracker.get_portfolio_features(4000.0);

    // The key fix: Portfolio value should be ~1.0 (normalized), NOT 100,000
    let portfolio_value = features[0];

    // Before Bug #20 fix: Would be 100,000.0 (raw value)
    // After Bug #20 fix: Should be ~1.0 (normalized)
    assert!(
        portfolio_value.abs() < 10.0,
        "Portfolio value should be normalized, got: {}",
        portfolio_value
    );

    // Check that portfolio value doesn't dwarf other features by 100,000x
    // (spread is intentionally small, but that's OK - key is portfolio isn't massive)
    let max_feature = features.iter().copied().fold(f32::NEG_INFINITY, f32::max);

    // Before fix: max_feature would be 100,000 (raw portfolio value)
    // After fix: max_feature should be ~1.0 (normalized portfolio value)
    assert!(
        max_feature < 10.0,
        "Max feature should be in normalized scale, got: {}",
        max_feature
    );

    println!("Feature scale check passed: max={:.2}, portfolio={:.2}", max_feature, portfolio_value);

    Ok(())
}

#[test]
fn test_portfolio_feature_format() -> Result<()> {
    // Test that get_portfolio_features returns 3 features:
    // [portfolio_value, position_normalized, spread]
    let tracker = PortfolioTracker::new(100_000.0, 0.0001, 0.0);
    let features = tracker.get_portfolio_features(4000.0);

    assert_eq!(features.len(), 3, "Should return exactly 3 features");

    // Feature 0: Portfolio value (should be normalized to ~1.0)
    // Feature 1: Position (normalized by max_position)
    // Feature 2: Spread

    println!("Portfolio features: {:?}", features);
    println!("Feature 0 (portfolio value): {}", features[0]);
    println!("Feature 1 (position): {}", features[1]);
    println!("Feature 2 (spread): {}", features[2]);

    Ok(())
}