foxhunt/ml/tests/barrier_backtest_test.rs

// ml/tests/barrier_backtest_test.rs
// Comprehensive tests for barrier parameter optimization backtesting

use ml::backtesting::barrier_backtest::{BarrierBacktester, BarrierParams};

#[test]
fn test_barrier_backtester_initialization() {
    let backtester = BarrierBacktester::new(10, 0.7);
    assert_eq!(backtester.walk_forward_windows(), 10);
    assert_eq!(backtester.train_test_split(), 0.7);
}

#[test]
fn test_walk_forward_validation_single_window() {
    // Single window walk-forward validation
    let backtester = BarrierBacktester::new(1, 0.7);

    // Create synthetic price series (100 bars)
    let prices: Vec<f64> = (0..100)
        .map(|i| 100.0 + (i as f64) * 0.1 + ((i % 5) as f64) * 0.5)
        .collect();

    let params = BarrierParams {
        profit_target: 0.02,
        stop_loss: 0.01,
        max_holding_periods: 10,
    };

    let results = backtester.run(&prices, params).expect("Backtest should succeed");

    // Basic validation
    assert!(results.sharpe_ratio.is_finite());
    assert!(results.win_rate >= 0.0 && results.win_rate <= 1.0);
    assert!(results.max_drawdown <= 0.0); // Drawdown is negative
    assert_eq!(
        results.label_distribution.0 + results.label_distribution.1 + results.label_distribution.2,
        prices.len()
    );
}

#[test]
fn test_walk_forward_validation_multiple_windows() {
    // Multiple windows walk-forward validation
    let backtester = BarrierBacktester::new(5, 0.7);

    // Create synthetic price series (500 bars for multiple windows)
    let prices: Vec<f64> = (0..500)
        .map(|i| 100.0 + (i as f64) * 0.02 + ((i as f64 / 10.0).sin() * 5.0))
        .collect();

    let params = BarrierParams {
        profit_target: 0.015,
        stop_loss: 0.01,
        max_holding_periods: 15,
    };

    let results = backtester.run(&prices, params).expect("Backtest should succeed");

    // Validate multi-window results
    assert!(results.sharpe_ratio.is_finite());
    assert!(results.stability_score >= 0.0); // Variance should be non-negative
    assert!(results.win_rate >= 0.0 && results.win_rate <= 1.0);
}

#[test]
fn test_sharpe_ratio_calculation() {
    let backtester = BarrierBacktester::new(1, 0.7);

    // Uptrending prices with volatility
    let prices: Vec<f64> = (0..200)
        .map(|i| 100.0 + (i as f64) * 0.1 + ((i as f64 / 5.0).sin() * 2.0))
        .collect();

    let params = BarrierParams {
        profit_target: 0.02,
        stop_loss: 0.01,
        max_holding_periods: 10,
    };

    let results = backtester.run(&prices, params).expect("Backtest should succeed");

    // Sharpe ratio should be finite for trending market
    // Note: Annualized Sharpe can be extreme for small samples with low volatility
    assert!(results.sharpe_ratio.is_finite());
}

#[test]
fn test_parameter_stability_across_regimes() {
    // Test stability score across different market regimes
    let backtester = BarrierBacktester::new(3, 0.7);

    // Create price series with regime changes
    let mut prices = Vec::new();

    // Regime 1: Uptrend (bars 0-150)
    for i in 0..150 {
        prices.push(100.0 + (i as f64) * 0.15);
    }

    // Regime 2: Downtrend (bars 150-300)
    for i in 0..150 {
        prices.push(122.5 - (i as f64) * 0.1);
    }

    // Regime 3: Sideways (bars 300-450)
    for i in 0..150 {
        prices.push(107.5 + ((i as f64 / 10.0).sin() * 3.0));
    }

    let params = BarrierParams {
        profit_target: 0.02,
        stop_loss: 0.01,
        max_holding_periods: 10,
    };

    let results = backtester.run(&prices, params).expect("Backtest should succeed");

    // Stability score should reflect regime changes
    assert!(results.stability_score >= 0.0);
    // Higher stability score means more variance across windows
    assert!(results.stability_score.is_finite());
}

#[test]
fn test_overfitting_detection_tight_barriers() {
    // Test for overfitting with very tight barriers
    let backtester = BarrierBacktester::new(5, 0.7);

    let prices: Vec<f64> = (0..500)
        .map(|i| 100.0 + (i as f64) * 0.01 + ((i as f64 / 20.0).sin() * 2.0))
        .collect();

    // Very tight barriers (likely to overfit to noise)
    let params = BarrierParams {
        profit_target: 0.001, // 0.1%
        stop_loss: 0.0005,    // 0.05%
        max_holding_periods: 5,
    };

    let results = backtester.run(&prices, params).expect("Backtest should succeed");

    // Tight barriers should result in high stability score (high variance across windows)
    assert!(results.stability_score >= 0.0);
    // Label distribution should be heavily skewed (mostly holds or stops)
    let total_labels = results.label_distribution.0
        + results.label_distribution.1
        + results.label_distribution.2;
    assert_eq!(total_labels, prices.len());
}

#[test]
fn test_overfitting_detection_wide_barriers() {
    // Test for underfitting with very wide barriers
    let backtester = BarrierBacktester::new(5, 0.7);

    let prices: Vec<f64> = (0..500)
        .map(|i| 100.0 + (i as f64) * 0.01 + ((i as f64 / 20.0).sin() * 2.0))
        .collect();

    // Very wide barriers (may underfit)
    let params = BarrierParams {
        profit_target: 0.1,  // 10%
        stop_loss: 0.05,     // 5%
        max_holding_periods: 100,
    };

    let results = backtester.run(&prices, params).expect("Backtest should succeed");

    // Wide barriers should result in low stability score (consistent behavior)
    assert!(results.stability_score >= 0.0);
    // Most labels should timeout (max_holding_periods reached)
}

#[test]
fn test_performance_full_dataset() {
    use std::time::Instant;

    let backtester = BarrierBacktester::new(10, 0.7);

    // Simulate ES.FUT-like dataset (1000 bars, typical intraday)
    let prices: Vec<f64> = (0..1000)
        .map(|i| 4500.0 + (i as f64) * 0.5 + ((i as f64 / 50.0).sin() * 20.0))
        .collect();

    let params = BarrierParams {
        profit_target: 0.02,
        stop_loss: 0.01,
        max_holding_periods: 10,
    };

    let start = Instant::now();
    let _results = backtester.run(&prices, params).expect("Backtest should succeed");
    let elapsed = start.elapsed();

    // Performance requirement: <30s for full dataset
    assert!(
        elapsed.as_secs() < 30,
        "Backtest took {:?}, expected <30s",
        elapsed
    );
}

#[test]
fn test_label_distribution_balanced() {
    let backtester = BarrierBacktester::new(1, 0.7);

    // Create price series designed to hit both profit/stop targets
    let mut prices = Vec::new();
    for i in 0..100 {
        if i % 2 == 0 {
            // Upswing (should hit profit target)
            prices.push(100.0 + (i as f64 / 10.0));
        } else {
            // Downswing (should hit stop loss)
            prices.push(100.0 - (i as f64 / 10.0));
        }
    }

    let params = BarrierParams {
        profit_target: 0.02,
        stop_loss: 0.01,
        max_holding_periods: 5,
    };

    let results = backtester.run(&prices, params).expect("Backtest should succeed");

    let (buys, sells, holds) = results.label_distribution;
    let total = buys + sells + holds;

    assert_eq!(total, prices.len());
    // With alternating up/down swings, we should have some balance
    assert!(buys > 0 || sells > 0); // At least some directional labels
}

#[test]
fn test_win_rate_calculation() {
    let backtester = BarrierBacktester::new(1, 0.7);

    // Strong uptrend (should have high win rate with buy labels)
    let prices: Vec<f64> = (0..100)
        .map(|i| 100.0 + (i as f64) * 0.5) // Consistent uptrend
        .collect();

    let params = BarrierParams {
        profit_target: 0.02,
        stop_loss: 0.01,
        max_holding_periods: 10,
    };

    let results = backtester.run(&prices, params).expect("Backtest should succeed");

    // Win rate should be reasonable
    assert!(results.win_rate >= 0.0 && results.win_rate <= 1.0);
    assert!(results.win_rate.is_finite());
}

#[test]
fn test_max_drawdown_calculation() {
    let backtester = BarrierBacktester::new(1, 0.7);

    // Create price series with a known drawdown
    let mut prices = Vec::new();

    // Initial rise
    for i in 0..30 {
        prices.push(100.0 + (i as f64) * 0.5);
    }

    // Sharp drop (creates drawdown)
    for i in 0..20 {
        prices.push(115.0 - (i as f64) * 0.3);
    }

    // Recovery
    for i in 0..30 {
        prices.push(109.0 + (i as f64) * 0.2);
    }

    let params = BarrierParams {
        profit_target: 0.02,
        stop_loss: 0.01,
        max_holding_periods: 10,
    };

    let results = backtester.run(&prices, params).expect("Backtest should succeed");

    // Max drawdown should be negative and finite
    assert!(results.max_drawdown <= 0.0);
    assert!(results.max_drawdown.is_finite());
}

#[test]
fn test_empty_price_series() {
    let backtester = BarrierBacktester::new(1, 0.7);

    let prices: Vec<f64> = vec![];
    let params = BarrierParams {
        profit_target: 0.02,
        stop_loss: 0.01,
        max_holding_periods: 10,
    };

    let result = backtester.run(&prices, params);
    assert!(result.is_err(), "Should fail with empty prices");
}

#[test]
fn test_insufficient_data_for_windows() {
    let backtester = BarrierBacktester::new(10, 0.7);

    // Only 50 bars, not enough for 10 windows
    let prices: Vec<f64> = (0..50)
        .map(|i| 100.0 + (i as f64) * 0.1)
        .collect();

    let params = BarrierParams {
        profit_target: 0.02,
        stop_loss: 0.01,
        max_holding_periods: 10,
    };

    let result = backtester.run(&prices, params);
    assert!(result.is_err(), "Should fail with insufficient data");
}

#[test]
fn test_invalid_parameters() {
    let backtester = BarrierBacktester::new(1, 0.7);

    let prices: Vec<f64> = (0..100)
        .map(|i| 100.0 + (i as f64) * 0.1)
        .collect();

    // Negative profit target
    let invalid_params = BarrierParams {
        profit_target: -0.02,
        stop_loss: 0.01,
        max_holding_periods: 10,
    };

    let result = backtester.run(&prices, invalid_params);
    assert!(result.is_err(), "Should fail with negative profit target");

    // Negative stop loss
    let invalid_params = BarrierParams {
        profit_target: 0.02,
        stop_loss: -0.01,
        max_holding_periods: 10,
    };

    let result = backtester.run(&prices, invalid_params);
    assert!(result.is_err(), "Should fail with negative stop loss");

    // Zero max holding periods
    let invalid_params = BarrierParams {
        profit_target: 0.02,
        stop_loss: 0.01,
        max_holding_periods: 0,
    };

    let result = backtester.run(&prices, invalid_params);
    assert!(result.is_err(), "Should fail with zero max holding periods");
}

#[test]
fn test_stability_score_perfect_consistency() {
    let backtester = BarrierBacktester::new(5, 0.7);

    // Perfectly consistent price series (no regime changes)
    let prices: Vec<f64> = (0..500)
        .map(|i| 100.0 + (i as f64) * 0.1) // Linear trend
        .collect();

    let params = BarrierParams {
        profit_target: 0.02,
        stop_loss: 0.01,
        max_holding_periods: 10,
    };

    let results = backtester.run(&prices, params).expect("Backtest should succeed");

    // Low stability score (low variance) for consistent market
    assert!(results.stability_score >= 0.0);
    assert!(results.stability_score.is_finite());
}

#[test]
fn test_real_world_scenario_es_fut() {
    // Simulate realistic ES.FUT price action
    let backtester = BarrierBacktester::new(10, 0.7);

    let mut prices = Vec::new();
    let mut current_price = 4500.0;

    // Simulate 1000 bars with realistic volatility
    for i in 0..1000 {
        // Add trend component
        let trend = (i as f64 / 1000.0) * 50.0;

        // Add cyclical component
        let cycle = (i as f64 / 20.0).sin() * 15.0;

        // Add noise
        let noise = ((i * 7) % 13) as f64 - 6.0;

        let price = 4500.0 + trend + cycle + noise;
        prices.push(price);
    }

    let params = BarrierParams {
        profit_target: 0.015, // 1.5% (realistic for ES.FUT)
        stop_loss: 0.01,      // 1% (risk management)
        max_holding_periods: 20, // ~20 minutes for 1min bars
    };

    let results = backtester.run(&prices, params).expect("Backtest should succeed");

    // All metrics should be reasonable for real-world data
    assert!(results.sharpe_ratio.is_finite());
    assert!(results.sharpe_ratio >= -3.0 && results.sharpe_ratio <= 3.0);
    assert!(results.win_rate >= 0.0 && results.win_rate <= 1.0);
    assert!(results.max_drawdown <= 0.0 && results.max_drawdown >= -0.5);
    assert!(results.stability_score >= 0.0 && results.stability_score.is_finite());

    let total_labels = results.label_distribution.0
        + results.label_distribution.1
        + results.label_distribution.2;
    assert_eq!(total_labels, prices.len());
}