foxhunt/crates/common/tests/shared_ml_strategy_integration_test.rs

//! Integration tests for SharedMLStrategy
//!
//! Validates that ONE SINGLE SYSTEM works for both trading and backtesting services.
//! NO duplication - both services use the same SharedMLStrategy instance.

use chrono::Utc;
use common::ml_strategy::{MLPrediction, SharedMLStrategy};
use ml::features::ProductionFeatureExtractorAdapter;
use std::sync::Arc;

#[tokio::test]
async fn test_single_strategy_both_services() {
    // Create ONE SINGLE SYSTEM with production feature extractor (225 features)
    let extractor = Box::new(ProductionFeatureExtractorAdapter::new());
    let strategy = Arc::new(SharedMLStrategy::new_with_production_extractor(extractor, 0.3));

    // Simulate trading service using the strategy
    let trading_strategy = Arc::clone(&strategy);
    let trading_handle = tokio::spawn(async move {
        let predictions = trading_strategy
            .get_ensemble_prediction(100.0, 1000.0, Utc::now())
            .await
            .expect("Trading service should get predictions");

        // Calculate vote if predictions are available
        if !predictions.is_empty() {
            trading_strategy.calculate_ensemble_vote(&predictions);
        }

        predictions.len()
    });

    // Simulate backtesting service using the SAME strategy
    let backtesting_strategy = Arc::clone(&strategy);
    let backtesting_handle = tokio::spawn(async move {
        let predictions = backtesting_strategy
            .get_ensemble_prediction(102.0, 1100.0, Utc::now())
            .await
            .expect("Backtesting service should get predictions");

        // Calculate vote if predictions are available
        if !predictions.is_empty() {
            backtesting_strategy.calculate_ensemble_vote(&predictions);
        }

        predictions.len()
    });

    // Both services should succeed
    let trading_count = trading_handle.await.expect("Trading task should complete");
    let backtesting_count = backtesting_handle
        .await
        .expect("Backtesting task should complete");

    assert!(trading_count > 0, "Trading should generate predictions");
    assert!(
        backtesting_count > 0,
        "Backtesting should generate predictions"
    );

    // Performance tracking would be populated after validate_predictions is called
    // For now, just verify the strategy is functioning
}

#[tokio::test]
async fn test_concurrent_access_from_multiple_services() {
    let extractor = Box::new(ProductionFeatureExtractorAdapter::new());
    let strategy = Arc::new(SharedMLStrategy::new_with_production_extractor(extractor, 0.5));

    let mut handles = Vec::new();

    // Spawn 10 concurrent tasks (simulating trading + backtesting + monitoring services)
    for i in 0..10 {
        let strategy_clone = Arc::clone(&strategy);
        let handle = tokio::spawn(async move {
            let price = 100.0 + (i as f64);
            let volume = 1000.0 + (i as f64 * 10.0);

            strategy_clone
                .get_ensemble_prediction(price, volume, Utc::now())
                .await
                .expect("Should get predictions")
        });
        handles.push(handle);
    }

    // Wait for all tasks
    for handle in handles {
        let predictions = handle.await.expect("Task should complete");
        assert!(!predictions.is_empty(), "Should have predictions");
    }
}

#[tokio::test]
async fn test_ensemble_vote_aggregation() {
    let extractor = Box::new(ProductionFeatureExtractorAdapter::new());
    let strategy = SharedMLStrategy::new_with_production_extractor(extractor, 0.0);

    let predictions = vec![
        MLPrediction {
            model_id: "dqn_v1".to_string(),
            prediction_value: 0.8,
            confidence: 0.9,
            features: vec![],
            timestamp: Utc::now(),
            inference_latency_us: 50,
        },
        MLPrediction {
            model_id: "dqn_v2".to_string(),
            prediction_value: 0.6,
            confidence: 0.7,
            features: vec![],
            timestamp: Utc::now(),
            inference_latency_us: 60,
        },
        MLPrediction {
            model_id: "dqn_v3".to_string(),
            prediction_value: 0.7,
            confidence: 0.8,
            features: vec![],
            timestamp: Utc::now(),
            inference_latency_us: 55,
        },
    ];

    let result = strategy.calculate_ensemble_vote(&predictions);
    assert!(result.is_some(), "Should calculate ensemble vote");

    let (vote, confidence) = result.unwrap_or_default();

    // Weighted average should be between 0.6 and 0.8
    assert!(
        (0.6..=0.8).contains(&vote),
        "Vote should be in expected range"
    );
    assert!(
        (0.7..=0.9).contains(&confidence),
        "Confidence should be in expected range"
    );
}

#[tokio::test]
async fn test_performance_tracking_across_services() {
    let extractor = Box::new(ProductionFeatureExtractorAdapter::new());
    let strategy = Arc::new(SharedMLStrategy::new_with_production_extractor(extractor, 0.5));

    // Trading service generates signals
    for _ in 0..5 {
        let predictions = strategy
            .get_ensemble_prediction(100.0, 1000.0, Utc::now())
            .await
            .expect("Should get predictions");

        // Validate positive outcome
        strategy.validate_predictions(&predictions, 0.05).await;
    }

    // Backtesting service generates signals
    for _ in 0..5 {
        let predictions = strategy
            .get_ensemble_prediction(102.0, 1100.0, Utc::now())
            .await
            .expect("Should get predictions");

        // Validate negative outcome
        strategy.validate_predictions(&predictions, -0.02).await;
    }

    // Check performance summary
    let performance = strategy.get_performance_summary().await;

    for (model_id, perf) in performance.iter() {
        assert!(
            perf.total_predictions > 0,
            "Model {} should have predictions",
            model_id
        );
        assert!(
            perf.accuracy_percentage >= 0.0 && perf.accuracy_percentage <= 100.0,
            "Accuracy should be valid percentage"
        );
    }
}

#[tokio::test]
async fn test_confidence_threshold_filtering() {
    let high_extractor = Box::new(ProductionFeatureExtractorAdapter::new());
    let high_threshold_strategy = SharedMLStrategy::new_with_production_extractor(high_extractor, 0.95);

    let low_extractor = Box::new(ProductionFeatureExtractorAdapter::new());
    let low_threshold_strategy = SharedMLStrategy::new_with_production_extractor(low_extractor, 0.1);

    // High threshold should filter out most predictions
    let high_predictions = high_threshold_strategy
        .get_ensemble_prediction(100.0, 1000.0, Utc::now())
        .await
        .expect("Should get predictions");

    // Low threshold should keep most predictions
    let low_predictions = low_threshold_strategy
        .get_ensemble_prediction(100.0, 1000.0, Utc::now())
        .await
        .expect("Should get predictions");

    assert!(
        low_predictions.len() >= high_predictions.len(),
        "Lower threshold should have more predictions"
    );
}

#[tokio::test]
async fn test_feature_extraction_consistency() {
    let extractor = Box::new(ProductionFeatureExtractorAdapter::new());
    let strategy = Arc::new(SharedMLStrategy::new_with_production_extractor(extractor, 0.5));

    // Generate predictions at two different times with same price/volume
    let predictions1 = strategy
        .get_ensemble_prediction(100.0, 1000.0, Utc::now())
        .await
        .expect("Should get predictions");

    tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;

    let predictions2 = strategy
        .get_ensemble_prediction(100.0, 1000.0, Utc::now())
        .await
        .expect("Should get predictions");

    // Should have same number of models responding
    assert_eq!(
        predictions1.len(),
        predictions2.len(),
        "Should have consistent number of predictions"
    );
}

#[tokio::test]
async fn test_empty_prediction_handling() {
    let extractor = Box::new(ProductionFeatureExtractorAdapter::new());
    let strategy = SharedMLStrategy::new_with_production_extractor(extractor, 0.99); // Very high threshold

    let predictions = vec![];

    let result = strategy.calculate_ensemble_vote(&predictions);
    assert!(result.is_none(), "Should return None for empty predictions");
}

#[tokio::test]
async fn test_model_performance_accuracy_tracking() {
    let extractor = Box::new(ProductionFeatureExtractorAdapter::new());
    let strategy = SharedMLStrategy::new_with_production_extractor(extractor, 0.0);

    let prediction = MLPrediction {
        model_id: "test_model".to_string(),
        prediction_value: 0.7, // Predicts positive
        confidence: 0.8,
        features: vec![],
        timestamp: Utc::now(),
        inference_latency_us: 50,
    };

    // Test with positive outcome (correct prediction)
    strategy
        .validate_predictions(std::slice::from_ref(&prediction), 0.05)
        .await;

    let performance = strategy.get_performance_summary().await;
    let model_perf = performance
        .get("test_model")
        .expect("Should have test_model performance");

    assert_eq!(model_perf.total_predictions, 1);
    assert_eq!(model_perf.correct_predictions, 1);
    assert_eq!(model_perf.accuracy_percentage, 100.0);

    // Test with negative outcome (incorrect prediction)
    strategy
        .validate_predictions(std::slice::from_ref(&prediction), -0.05)
        .await;

    let performance = strategy.get_performance_summary().await;
    let model_perf = performance
        .get("test_model")
        .expect("Should have test_model performance");

    assert_eq!(model_perf.total_predictions, 2);
    assert_eq!(model_perf.correct_predictions, 1);
    assert_eq!(model_perf.accuracy_percentage, 50.0);
}