foxhunt/ml/tests/regime_temperature_test.rs

//! Unit tests for regime-aware temperature adaptation in DQN
//!
//! This module tests the integration between RegimeOrchestrator and DQN temperature control.
//! Tests cover:
//! 1. Temperature adjustment for each regime type (Trending, Ranging, Volatile)
//! 2. Integration with RegimeOrchestrator
//! 3. Fallback behavior when regime detection unavailable
//! 4. Configuration of regime-specific multipliers

use chrono::Utc;
use ml::regime::orchestrator::{Bar, RegimeOrchestrator, RegimeState};
use sqlx::PgPool;
use std::collections::HashMap;

/// Helper function to create test bars with trending pattern
fn create_trending_bars(count: usize) -> Vec<Bar> {
    let base_time = Utc::now();
    let base_price = 100.0;

    (0..count)
        .map(|i| {
            let price = base_price + (i as f64 * 0.5); // Strong uptrend
            Bar {
                timestamp: base_time + chrono::Duration::seconds(i as i64 * 60),
                open: price,
                high: price + 0.3,
                low: price - 0.2,
                close: price + 0.25,
                volume: 10000.0,
            }
        })
        .collect()
}

/// Helper function to create test bars with ranging pattern
fn create_ranging_bars(count: usize) -> Vec<Bar> {
    let base_time = Utc::now();
    let base_price = 100.0;

    (0..count)
        .map(|i| {
            // Oscillate between 99-101
            let offset = ((i as f64 * 0.5).sin() * 1.0);
            let price = base_price + offset;
            Bar {
                timestamp: base_time + chrono::Duration::seconds(i as i64 * 60),
                open: price,
                high: price + 0.2,
                low: price - 0.2,
                close: price,
                volume: 8000.0,
            }
        })
        .collect()
}

/// Helper function to create test bars with volatile pattern
fn create_volatile_bars(count: usize) -> Vec<Bar> {
    let base_time = Utc::now();
    let base_price = 100.0;

    (0..count)
        .map(|i| {
            // Large random swings
            let offset = ((i as f64).sin() * 5.0); // ±5 point swings
            let price = base_price + offset;
            Bar {
                timestamp: base_time + chrono::Duration::seconds(i as i64 * 60),
                open: price,
                high: price + 2.0, // Wide range
                low: price - 2.0,
                close: price + 0.5,
                volume: 50000.0, // High volume
            }
        })
        .collect()
}

#[tokio::test]
async fn test_regime_temperature_multipliers_default() {
    // Test that default regime temperature multipliers are reasonable
    let multipliers = get_default_regime_multipliers();

    // Verify trending has lower multiplier (exploit trend)
    assert!(multipliers.get("Trending").unwrap() < &1.0);

    // Verify ranging has higher multiplier (explore breakouts)
    assert!(multipliers.get("Ranging").unwrap() >= &1.0);

    // Verify volatile has high multiplier (high exploration)
    assert!(multipliers.get("Volatile").unwrap() > &1.0);

    // Verify normal/fallback regime exists
    assert!(multipliers.contains_key("Normal"));
}

#[tokio::test]
async fn test_apply_regime_temperature_trending() {
    // Test temperature adjustment for trending regime
    let base_temp = 1.0;
    let multipliers = get_default_regime_multipliers();

    let regime = "Trending";
    let adjusted_temp = apply_regime_temperature(base_temp, regime, &multipliers);

    // Trending should reduce temperature (0.8x)
    assert!(adjusted_temp < base_temp);
    assert!((adjusted_temp - 0.8).abs() < 0.01);
}

#[tokio::test]
async fn test_apply_regime_temperature_ranging() {
    // Test temperature adjustment for ranging regime
    let base_temp = 1.0;
    let multipliers = get_default_regime_multipliers();

    let regime = "Ranging";
    let adjusted_temp = apply_regime_temperature(base_temp, regime, &multipliers);

    // Ranging should increase temperature (1.2x)
    assert!(adjusted_temp > base_temp);
    assert!((adjusted_temp - 1.2).abs() < 0.01);
}

#[tokio::test]
async fn test_apply_regime_temperature_volatile() {
    // Test temperature adjustment for volatile regime
    let base_temp = 1.0;
    let multipliers = get_default_regime_multipliers();

    let regime = "Volatile";
    let adjusted_temp = apply_regime_temperature(base_temp, regime, &multipliers);

    // Volatile should significantly increase temperature (1.5x)
    assert!(adjusted_temp > base_temp);
    assert!((adjusted_temp - 1.5).abs() < 0.01);
}

#[tokio::test]
async fn test_apply_regime_temperature_unknown_fallback() {
    // Test fallback behavior for unknown regime
    let base_temp = 1.0;
    let multipliers = get_default_regime_multipliers();

    let regime = "UnknownRegime";
    let adjusted_temp = apply_regime_temperature(base_temp, regime, &multipliers);

    // Should fallback to "Normal" multiplier (1.0x)
    assert!((adjusted_temp - base_temp).abs() < 0.01);
}

#[tokio::test]
#[ignore] // Requires database connection
async fn test_regime_orchestrator_integration_trending() {
    // Test integration with RegimeOrchestrator for trending pattern
    let database_url = std::env::var("DATABASE_URL").unwrap_or_else(|_| {
        "postgresql://foxhunt:foxhunt_dev_password@localhost:5432/foxhunt".to_string()
    });

    let pool = PgPool::connect(&database_url)
        .await
        .expect("Failed to connect to database");

    let mut orchestrator = RegimeOrchestrator::new(pool.clone())
        .await
        .expect("Failed to create orchestrator");

    // Create trending bars
    let bars = create_trending_bars(50);

    // Detect regime
    let regime_state = orchestrator
        .detect_and_persist("TEST_SYMBOL_TREND", &bars)
        .await
        .expect("Failed to detect regime");

    // Verify regime classification (may be "Trending" or "Normal" depending on ADX threshold)
    assert!(
        regime_state.regime == "Trending" || regime_state.regime == "Normal",
        "Unexpected regime: {}",
        regime_state.regime
    );

    // Apply temperature adjustment
    let base_temp = 1.0;
    let multipliers = get_default_regime_multipliers();
    let adjusted_temp = apply_regime_temperature(base_temp, &regime_state.regime, &multipliers);

    // Verify temperature is adjusted based on regime
    if regime_state.regime == "Trending" {
        assert!(adjusted_temp < base_temp);
    }
}

#[tokio::test]
#[ignore] // Requires database connection
async fn test_regime_orchestrator_integration_ranging() {
    // Test integration with RegimeOrchestrator for ranging pattern
    let database_url = std::env::var("DATABASE_URL").unwrap_or_else(|_| {
        "postgresql://foxhunt:foxhunt_dev_password@localhost:5432/foxhunt".to_string()
    });

    let pool = PgPool::connect(&database_url)
        .await
        .expect("Failed to connect to database");

    let mut orchestrator = RegimeOrchestrator::new(pool.clone())
        .await
        .expect("Failed to create orchestrator");

    // Create ranging bars
    let bars = create_ranging_bars(50);

    // Detect regime
    let regime_state = orchestrator
        .detect_and_persist("TEST_SYMBOL_RANGE", &bars)
        .await
        .expect("Failed to detect regime");

    // Verify regime classification
    assert!(
        regime_state.regime == "Ranging" || regime_state.regime == "Normal",
        "Unexpected regime: {}",
        regime_state.regime
    );

    // Apply temperature adjustment
    let base_temp = 1.0;
    let multipliers = get_default_regime_multipliers();
    let adjusted_temp = apply_regime_temperature(base_temp, &regime_state.regime, &multipliers);

    // Verify temperature is adjusted based on regime
    if regime_state.regime == "Ranging" {
        assert!(adjusted_temp > base_temp);
    }
}

#[tokio::test]
#[ignore] // Requires database connection
async fn test_regime_orchestrator_integration_volatile() {
    // Test integration with RegimeOrchestrator for volatile pattern
    let database_url = std::env::var("DATABASE_URL").unwrap_or_else(|_| {
        "postgresql://foxhunt:foxhunt_dev_password@localhost:5432/foxhunt".to_string()
    });

    let pool = PgPool::connect(&database_url)
        .await
        .expect("Failed to connect to database");

    let mut orchestrator = RegimeOrchestrator::new(pool.clone())
        .await
        .expect("Failed to create orchestrator");

    // Create volatile bars
    let bars = create_volatile_bars(50);

    // Detect regime
    let regime_state = orchestrator
        .detect_and_persist("TEST_SYMBOL_VOLATILE", &bars)
        .await
        .expect("Failed to detect regime");

    // Verify regime classification
    assert!(
        regime_state.regime == "Volatile" || regime_state.regime == "Normal",
        "Unexpected regime: {}",
        regime_state.regime
    );

    // Apply temperature adjustment
    let base_temp = 1.0;
    let multipliers = get_default_regime_multipliers();
    let adjusted_temp = apply_regime_temperature(base_temp, &regime_state.regime, &multipliers);

    // Verify temperature is adjusted based on regime
    if regime_state.regime == "Volatile" {
        assert!(adjusted_temp > base_temp);
    }
}

#[test]
fn test_custom_regime_multipliers() {
    // Test custom regime multipliers configuration
    let mut custom_multipliers = HashMap::new();
    custom_multipliers.insert("Trending".to_string(), 0.5); // Very low temp
    custom_multipliers.insert("Ranging".to_string(), 2.0); // Very high temp
    custom_multipliers.insert("Volatile".to_string(), 1.8); // High temp
    custom_multipliers.insert("Normal".to_string(), 1.0); // Baseline

    let base_temp = 1.0;

    // Test trending
    let trending_temp = apply_regime_temperature(base_temp, "Trending", &custom_multipliers);
    assert!((trending_temp - 0.5).abs() < 0.01);

    // Test ranging
    let ranging_temp = apply_regime_temperature(base_temp, "Ranging", &custom_multipliers);
    assert!((ranging_temp - 2.0).abs() < 0.01);

    // Test volatile
    let volatile_temp = apply_regime_temperature(base_temp, "Volatile", &custom_multipliers);
    assert!((volatile_temp - 1.8).abs() < 0.01);
}

#[test]
fn test_temperature_bounds() {
    // Test that temperature adjustment respects min/max bounds
    let base_temp = 0.1; // At minimum
    let multipliers = get_default_regime_multipliers();

    // Even with high multiplier, should not go below reasonable bounds
    let adjusted_temp = apply_regime_temperature(base_temp, "Volatile", &multipliers);

    // Verify temperature is positive
    assert!(adjusted_temp > 0.0);

    // Verify temperature doesn't explode
    assert!(adjusted_temp < 10.0);
}

// ============================================================================
// Helper Functions (to be implemented in ml/src/dqn/regime_temperature.rs)
// ============================================================================

/// Get default regime temperature multipliers
///
/// Returns recommended multipliers based on adaptive temperature research:
/// - Trending: 0.8x (exploit trend)
/// - Ranging: 1.2x (explore breakouts)
/// - Volatile: 1.5x (high exploration)
/// - Normal: 1.0x (baseline)
fn get_default_regime_multipliers() -> HashMap<String, f64> {
    let mut multipliers = HashMap::new();
    multipliers.insert("Trending".to_string(), 0.8);
    multipliers.insert("Ranging".to_string(), 1.2);
    multipliers.insert("Volatile".to_string(), 1.5);
    multipliers.insert("Normal".to_string(), 1.0);
    multipliers
}

/// Apply regime-specific temperature multiplier
///
/// # Arguments
///
/// * `base_temp` - Base temperature from exponential decay
/// * `regime` - Current market regime (Trending, Ranging, Volatile, Normal)
/// * `multipliers` - Regime-specific multipliers
///
/// # Returns
///
/// Adjusted temperature scaled by regime multiplier
fn apply_regime_temperature(
    base_temp: f64,
    regime: &str,
    multipliers: &HashMap<String, f64>,
) -> f64 {
    let multiplier = multipliers.get(regime).unwrap_or(&1.0);
    base_temp * multiplier
}