foxhunt/crates/ml/tests/gpu_backtest_validation.rs

//! Validates GPU backtest evaluator produces reasonable metrics
//! using synthetic data and deterministic action models.
//!
//! These tests require a CUDA GPU and are skipped gracefully when none is available.
//! Run with: `cargo test -p ml --test gpu_backtest_validation -- --ignored`

/// Generate deterministic synthetic price data (random walk with drift) using LCG.
fn generate_prices(n_bars: usize, seed: u64, drift: f32) -> Vec<[f32; 4]> {
    let mut rng_state = seed;
    let mut prices = Vec::with_capacity(n_bars);
    let mut price = 100.0_f32;

    for _ in 0..n_bars {
        // Simple LCG for determinism
        rng_state = rng_state
            .wrapping_mul(6_364_136_223_846_793_005)
            .wrapping_add(1_442_695_040_888_963_407);
        let rand_f = ((rng_state >> 33) as f32) / (u32::MAX as f32) - 0.5;
        let ret = drift + rand_f * 0.02;
        price *= 1.0 + ret;
        let ohlc = [price * 0.999, price * 1.001, price * 0.998, price];
        prices.push(ohlc);
    }
    prices
}

/// Generate minimal synthetic features (just enough for the evaluator).
fn generate_features(n_bars: usize, feature_dim: usize) -> Vec<Vec<f32>> {
    (0..n_bars)
        .map(|i| {
            let mut fv = vec![0.0_f32; feature_dim];
            // Put some variation in features so they are not all zero
            if let Some(f) = fv.get_mut(0) {
                *f = (i as f32) * 0.001;
            }
            fv
        })
        .collect()
}

#[cfg(feature = "cuda")]
mod gpu_tests {
    use super::*;
    use candle_core::{Device, Tensor};
    use ml::cuda_pipeline::gpu_backtest_evaluator::{
        GpuBacktestConfig, GpuBacktestEvaluator,
    };
    use ml::MLError;

    /// Skip test gracefully if no CUDA device is available.
    fn try_cuda_device() -> Option<Device> {
        match Device::cuda_if_available(0) {
            Ok(dev) if dev.is_cuda() => Some(dev),
            _ => {
                eprintln!("CUDA not available, skipping GPU backtest test");
                None
            }
        }
    }

    /// Build a closure that always returns Q-values favouring `action`.
    ///
    /// Returns `[batch_size, num_actions]` with 1.0 at `action` and 0.0 elsewhere.
    fn constant_action_model(
        action: usize,
        num_actions: usize,
    ) -> impl Fn(&Tensor) -> Result<Tensor, MLError> {
        move |states: &Tensor| {
            let batch_size = states
                .dim(0)
                .map_err(|e| MLError::ModelError(format!("{e}")))?;
            let mut q_data = vec![0.0_f32; batch_size * num_actions];
            for b in 0..batch_size {
                let base = b * num_actions;
                if let Some(q) = q_data.get_mut(base + action) {
                    *q = 1.0;
                }
            }
            Tensor::from_vec(q_data, (batch_size, num_actions), states.device())
                .map_err(|e| MLError::ModelError(format!("{e}")))
        }
    }

    // ── Individual test cases ─────────────────────────────────────────────────

    /// Always-long model on upward-trending data should produce positive PnL.
    #[test]
    #[ignore]
    fn test_always_long_on_uptrend() {
        let device = match try_cuda_device() {
            Some(d) => d,
            None => return,
        };

        const FEATURE_DIM: usize = 10;
        const N_BARS: usize = 500;

        let prices = generate_prices(N_BARS, 42, 0.001); // positive drift
        let features = generate_features(N_BARS, FEATURE_DIM);

        let config = GpuBacktestConfig {
            max_position: 1.0,
            tx_cost_bps: 0.0, // zero costs for a clean signal
            spread_cost: 0.0,
            initial_capital: 100_000.0,
            ..Default::default()
        };

        let mut evaluator = GpuBacktestEvaluator::new(
            &[prices],
            &[features],
            FEATURE_DIM,
            config,
            &device,
        )
        .expect("evaluator creation should succeed");

        // Action 4 = Long100
        let model = constant_action_model(4, 5);
        let metrics = evaluator
            .evaluate(&model, 3, &device)
            .expect("evaluation should succeed");

        assert_eq!(metrics.len(), 1, "expected exactly one window result");
        let m = metrics
            .first()
            .expect("metrics vec must have at least one element");

        // With positive drift and always-long, should be profitable
        assert!(
            m.total_pnl > 0.0,
            "expected positive PnL for long on uptrend, got {}",
            m.total_pnl
        );
        assert!(
            m.max_drawdown >= 0.0,
            "drawdown should be non-negative, got {}",
            m.max_drawdown
        );
        assert!(
            (0.0..=1.0).contains(&m.win_rate),
            "win_rate {} is out of [0, 1] range",
            m.win_rate
        );
    }

    /// Always-long model on downward-trending data should produce negative PnL.
    #[test]
    #[ignore]
    fn test_always_long_on_downtrend() {
        let device = match try_cuda_device() {
            Some(d) => d,
            None => return,
        };

        const FEATURE_DIM: usize = 10;
        const N_BARS: usize = 500;

        let prices = generate_prices(N_BARS, 77, -0.001); // negative drift
        let features = generate_features(N_BARS, FEATURE_DIM);

        let config = GpuBacktestConfig {
            max_position: 1.0,
            tx_cost_bps: 0.0,
            spread_cost: 0.0,
            initial_capital: 100_000.0,
            ..Default::default()
        };

        let mut evaluator = GpuBacktestEvaluator::new(
            &[prices],
            &[features],
            FEATURE_DIM,
            config,
            &device,
        )
        .expect("evaluator creation should succeed");

        let model = constant_action_model(4, 5); // Always Long100
        let metrics = evaluator
            .evaluate(&model, 3, &device)
            .expect("evaluation should succeed");

        assert_eq!(metrics.len(), 1);
        let m = metrics
            .first()
            .expect("metrics vec must have at least one element");

        assert!(
            m.total_pnl < 0.0,
            "expected negative PnL for long on downtrend, got {}",
            m.total_pnl
        );
        assert!(
            m.max_drawdown >= 0.0,
            "drawdown should be non-negative, got {}",
            m.max_drawdown
        );
    }

    /// Always-flat model should produce ~zero PnL and minimal trades.
    #[test]
    #[ignore]
    fn test_always_flat_produces_no_pnl() {
        let device = match try_cuda_device() {
            Some(d) => d,
            None => return,
        };

        const FEATURE_DIM: usize = 10;
        const N_BARS: usize = 200;

        let prices = generate_prices(N_BARS, 99, 0.0);
        let features = generate_features(N_BARS, FEATURE_DIM);

        let config = GpuBacktestConfig::default();

        let mut evaluator = GpuBacktestEvaluator::new(
            &[prices],
            &[features],
            FEATURE_DIM,
            config,
            &device,
        )
        .expect("evaluator creation should succeed");

        // Action 2 = Flat — never enters a position
        let model = constant_action_model(2, 5);
        let metrics = evaluator
            .evaluate(&model, 3, &device)
            .expect("evaluation should succeed");

        assert_eq!(metrics.len(), 1);
        let m = metrics
            .first()
            .expect("metrics vec must have at least one element");

        // Flat action means no position changes, so PnL should be approximately zero
        assert!(
            m.total_pnl.abs() < 0.01,
            "expected ~zero PnL for flat model, got {}",
            m.total_pnl
        );
    }

    /// Multiple windows must produce one result per window with sensible ordering.
    #[test]
    #[ignore]
    fn test_multiple_windows_produce_results() {
        let device = match try_cuda_device() {
            Some(d) => d,
            None => return,
        };

        const FEATURE_DIM: usize = 10;
        const N_BARS: usize = 300;

        let prices_up = generate_prices(N_BARS, 42, 0.001);
        let prices_down = generate_prices(N_BARS, 123, -0.001);
        let features1 = generate_features(N_BARS, FEATURE_DIM);
        let features2 = generate_features(N_BARS, FEATURE_DIM);

        let config = GpuBacktestConfig {
            max_position: 1.0,
            tx_cost_bps: 0.0,
            spread_cost: 0.0,
            initial_capital: 100_000.0,
            ..Default::default()
        };

        let mut evaluator = GpuBacktestEvaluator::new(
            &[prices_up, prices_down],
            &[features1, features2],
            FEATURE_DIM,
            config,
            &device,
        )
        .expect("evaluator creation should succeed");

        let model = constant_action_model(4, 5); // Always Long100
        let metrics = evaluator
            .evaluate(&model, 3, &device)
            .expect("evaluation should succeed");

        assert_eq!(metrics.len(), 2, "expected exactly 2 window results");

        let m0 = metrics.first().expect("window 0 result must exist");
        let m1 = metrics.get(1).expect("window 1 result must exist");

        // Uptrend window (0) should be more profitable than downtrend window (1)
        assert!(
            m0.total_pnl > m1.total_pnl,
            "expected uptrend window more profitable: {} vs {}",
            m0.total_pnl,
            m1.total_pnl
        );

        // Both drawdowns must be non-negative
        assert!(m0.max_drawdown >= 0.0, "window 0 drawdown negative");
        assert!(m1.max_drawdown >= 0.0, "window 1 drawdown negative");
    }

    /// Extended metrics (VaR, CVaR, Calmar, Omega) must be finite and self-consistent.
    #[test]
    #[ignore]
    fn test_extended_metrics_populated() {
        let device = match try_cuda_device() {
            Some(d) => d,
            None => return,
        };

        const FEATURE_DIM: usize = 10;
        const N_BARS: usize = 500;

        let prices = generate_prices(N_BARS, 42, 0.001);
        let features = generate_features(N_BARS, FEATURE_DIM);
        let config = GpuBacktestConfig::default();

        let mut evaluator = GpuBacktestEvaluator::new(
            &[prices],
            &[features],
            FEATURE_DIM,
            config,
            &device,
        )
        .expect("evaluator creation should succeed");

        let model = constant_action_model(4, 5);
        let metrics = evaluator
            .evaluate(&model, 3, &device)
            .expect("evaluation should succeed");

        let m = metrics
            .first()
            .expect("metrics vec must have at least one element");

        assert!(!m.var_95.is_nan(), "VaR should not be NaN");
        assert!(!m.cvar_95.is_nan(), "CVaR should not be NaN");
        assert!(!m.calmar.is_nan(), "Calmar should not be NaN");
        assert!(!m.omega_ratio.is_nan(), "Omega ratio should not be NaN");

        // CVaR (conditional VaR / expected shortfall) must be <= VaR because CVaR
        // averages the worst returns that are already worse than the VaR threshold.
        // Add a small tolerance for floating-point rounding.
        assert!(
            m.cvar_95 <= m.var_95 + 1e-3,
            "CVaR {} should be <= VaR {} (mean of tail should not exceed threshold)",
            m.cvar_95,
            m.var_95
        );
    }

    /// total_trades must be positive when the model takes an active position.
    #[test]
    #[ignore]
    fn test_active_model_records_trades() {
        let device = match try_cuda_device() {
            Some(d) => d,
            None => return,
        };

        const FEATURE_DIM: usize = 10;
        const N_BARS: usize = 300;

        let prices = generate_prices(N_BARS, 55, 0.001);
        let features = generate_features(N_BARS, FEATURE_DIM);
        let config = GpuBacktestConfig::default();

        let mut evaluator = GpuBacktestEvaluator::new(
            &[prices],
            &[features],
            FEATURE_DIM,
            config,
            &device,
        )
        .expect("evaluator creation should succeed");

        let model = constant_action_model(4, 5); // Always Long100
        let metrics = evaluator
            .evaluate(&model, 3, &device)
            .expect("evaluation should succeed");

        let m = metrics
            .first()
            .expect("metrics vec must have at least one element");

        // An always-long model must execute at least the initial entry trade
        assert!(
            m.total_trades > 0.0,
            "expected at least one trade for active model, got {}",
            m.total_trades
        );
        assert!(
            (0.0..=1.0).contains(&m.win_rate),
            "win_rate {} out of [0, 1]",
            m.win_rate
        );
    }
}