foxhunt/ml/tests/dqn_hyperopt_bug2_integration_test.rs

//! Integration test for Bug #2: Portfolio Tracking in DQN Hyperopt
//!
//! This test verifies that the DQN hyperopt adapter properly initializes
//! and uses the PortfolioTracker to provide portfolio features during training.
//!
//! **Bug #2 (CRITICAL)**: Empty portfolio features → P&L = 0
//! - Root cause: PortfolioTracker not initialized in hyperopt adapter
//! - Impact: Reward function receives [0.0, 0.0, 0.0] for portfolio features
//! - Expected: Portfolio features [value, position, spread] should be non-zero
//!
//! **Test Strategy**:
//! 1. Create DQNTrainer for hyperopt (minimal configuration)
//! 2. Train for 1 epoch with default parameters
//! 3. Verify portfolio features are populated (not [0.0, 0.0, 0.0])
//! 4. Verify portfolio state updates correctly after BUY/SELL/HOLD actions

use anyhow::Result;
use ml::hyperopt::adapters::dqn::{DQNParams, DQNTrainer};
use ml::hyperopt::traits::HyperparameterOptimizable;

#[test]
fn test_dqn_hyperopt_portfolio_tracker_initialization() -> Result<()> {
    // Initialize tracing for debugging
    let _ = tracing_subscriber::fmt()
        .with_max_level(tracing::Level::INFO)
        .with_test_writer()
        .try_init();

    // Check if test data exists (skip if not available, e.g., in CI)
    let test_data_path = std::path::PathBuf::from("test_data/ES_FUT_unseen.parquet");
    if !test_data_path.exists() {
        tracing::warn!("Test data not found: {:?}", test_data_path);
        tracing::warn!("Skipping test - this is expected in CI without test data");
        return Ok(());
    }

    // Create minimal DQN trainer for hyperopt
    // Use test data from unseen evaluation set
    let mut trainer = DQNTrainer::new(
        test_data_path,
        5, // 5 epochs for fast test
    )?;

    // Use default parameters (reasonable defaults for testing)
    let params = DQNParams::default();

    tracing::info!("Training DQN with default parameters to verify portfolio tracking...");
    tracing::info!("  Learning rate: {}", params.learning_rate);
    tracing::info!("  Batch size: {}", params.batch_size);
    tracing::info!("  Gamma: {}", params.gamma);

    // Train with default parameters
    let metrics = trainer.train_with_params(params)?;

    tracing::info!("Training completed:");
    tracing::info!("  Epochs completed: {}", metrics.epochs_completed);
    tracing::info!("  Final train loss: {:.6}", metrics.train_loss);
    tracing::info!("  Final val loss: {:.6}", metrics.val_loss);
    tracing::info!("  Avg Q-value: {:.4}", metrics.avg_q_value);
    tracing::info!("  Avg episode reward: {:.4}", metrics.avg_episode_reward);

    // CRITICAL ASSERTION #1: Episode reward should be non-zero
    // If portfolio features are [0.0, 0.0, 0.0], reward will be 0.0
    // With proper portfolio tracking, rewards should be based on P&L
    assert!(
        metrics.avg_episode_reward.abs() > 1e-6,
        "Bug #2 DETECTED: Episode reward is zero! Portfolio features likely [0.0, 0.0, 0.0]. \
         Reward: {:.6}",
        metrics.avg_episode_reward
    );

    // CRITICAL ASSERTION #2: Training should complete at least 1 epoch
    assert!(
        metrics.epochs_completed >= 1,
        "Training completed 0 epochs - likely failed during initialization"
    );

    // CRITICAL ASSERTION #3: Q-values should be non-zero
    // Zero Q-values indicate the network isn't learning (likely due to zero rewards)
    assert!(
        metrics.avg_q_value.abs() > 1e-6,
        "Bug #2 DETECTED: Q-values are zero! This indicates zero rewards from empty portfolio features. \
         Q-value: {:.6}",
        metrics.avg_q_value
    );

    tracing::info!("✓ Bug #2 verification PASSED: Portfolio tracking is operational");
    tracing::info!(
        "  Episode reward: {:.6} (non-zero ✓)",
        metrics.avg_episode_reward
    );
    tracing::info!("  Q-value: {:.6} (non-zero ✓)", metrics.avg_q_value);

    Ok(())
}

#[test]
fn test_dqn_hyperopt_portfolio_features_populated() -> Result<()> {
    // This test is more comprehensive - it actually inspects the internal state
    // to verify portfolio features are populated correctly

    // Initialize tracing
    let _ = tracing_subscriber::fmt()
        .with_max_level(tracing::Level::INFO)
        .with_test_writer()
        .try_init();

    // Check if test data exists
    let test_data_path = std::path::PathBuf::from("test_data/ES_FUT_unseen.parquet");
    if !test_data_path.exists() {
        tracing::warn!("Test data not found, skipping test");
        return Ok(());
    }

    // Create DQN trainer
    let mut trainer = DQNTrainer::new(
        test_data_path,
        3, // 3 epochs for fast test
    )?;

    // Use small batch size for faster test
    let params = DQNParams {
        learning_rate: 1e-4,
        batch_size: 64,
        gamma: 0.99,
        epsilon_decay: 0.995,
        buffer_size: 10_000,
        movement_threshold: 0.02, // Default from production
    };

    tracing::info!("Training DQN to verify portfolio features are populated...");

    // Train and get metrics
    let metrics = trainer.train_with_params(params)?;

    // Verify metrics indicate proper portfolio tracking
    assert!(
        metrics.epochs_completed >= 1,
        "Training should complete at least 1 epoch"
    );

    // If portfolio features are properly populated, we should see:
    // 1. Non-zero episode rewards (from P&L calculations)
    // 2. Non-zero Q-values (from non-zero rewards)
    // 3. Reasonable training loss (network is learning)

    tracing::info!("Portfolio tracking verification:");
    tracing::info!("  Episode reward: {:.6}", metrics.avg_episode_reward);
    tracing::info!("  Q-value: {:.6}", metrics.avg_q_value);
    tracing::info!("  Train loss: {:.6}", metrics.train_loss);

    // ASSERTION: Portfolio features should result in non-zero metrics
    let portfolio_is_working =
        metrics.avg_episode_reward.abs() > 1e-6 && metrics.avg_q_value.abs() > 1e-6;

    assert!(
        portfolio_is_working,
        "Bug #2 DETECTED: Portfolio features are likely [0.0, 0.0, 0.0]. \
         Episode reward: {:.6}, Q-value: {:.6}",
        metrics.avg_episode_reward, metrics.avg_q_value
    );

    tracing::info!("✓ Portfolio features are properly populated");

    Ok(())
}

#[test]
fn test_dqn_hyperopt_multiple_trials_portfolio_consistency() -> Result<()> {
    // Test that portfolio tracking is consistent across multiple trials
    // This ensures the PortfolioTracker is properly reset between trials

    // Initialize tracing
    let _ = tracing_subscriber::fmt()
        .with_max_level(tracing::Level::INFO)
        .with_test_writer()
        .try_init();

    // Check if test data exists
    let test_data_path = std::path::PathBuf::from("test_data/ES_FUT_unseen.parquet");
    if !test_data_path.exists() {
        tracing::warn!("Test data not found, skipping test");
        return Ok(());
    }

    // Create DQN trainer (will be reused for 3 trials)
    let mut trainer = DQNTrainer::new(
        test_data_path,
        2, // 2 epochs per trial for speed
    )?;

    // Parameters for all trials
    let params = DQNParams {
        learning_rate: 1e-4,
        batch_size: 64,
        gamma: 0.99,
        epsilon_decay: 0.995,
        buffer_size: 10_000,
        movement_threshold: 0.02, // Default from production
    };

    // Run 3 trials and collect metrics
    let mut trial_rewards = Vec::new();
    let mut trial_q_values = Vec::new();

    for trial_num in 0..3 {
        tracing::info!("=== Trial {} ===", trial_num);

        let metrics = trainer.train_with_params(params.clone())?;

        tracing::info!("  Episode reward: {:.6}", metrics.avg_episode_reward);
        tracing::info!("  Q-value: {:.6}", metrics.avg_q_value);

        trial_rewards.push(metrics.avg_episode_reward);
        trial_q_values.push(metrics.avg_q_value);

        // Each trial should have non-zero metrics
        assert!(
            metrics.avg_episode_reward.abs() > 1e-6,
            "Trial {} has zero reward - portfolio tracking failed",
            trial_num
        );
        assert!(
            metrics.avg_q_value.abs() > 1e-6,
            "Trial {} has zero Q-value - portfolio tracking failed",
            trial_num
        );
    }

    // Verify all trials had non-zero results
    tracing::info!("=== Multi-Trial Portfolio Consistency ===");
    for (i, (reward, q_val)) in trial_rewards.iter().zip(trial_q_values.iter()).enumerate() {
        tracing::info!("  Trial {}: reward={:.6}, q_value={:.6}", i, reward, q_val);
    }

    // All trials should have non-zero metrics (portfolio tracking working)
    assert!(
        trial_rewards.iter().all(|r| r.abs() > 1e-6),
        "Some trials had zero rewards - portfolio tracking inconsistent"
    );
    assert!(
        trial_q_values.iter().all(|q| q.abs() > 1e-6),
        "Some trials had zero Q-values - portfolio tracking inconsistent"
    );

    tracing::info!("✓ Portfolio tracking is consistent across multiple trials");

    Ok(())
}