"""
Pattern detection and buy signal generation tasks
"""

from datetime import datetime, timedelta
import structlog

from app.workers.celery_app import celery_app
from app.core.config import settings

logger = structlog.get_logger()


@celery_app.task(name="app.workers.tasks.pattern_tasks.detect_panic_events")
def detect_panic_events():
    """Detect new panic events based on sentiment and price drops."""
    logger.info("Starting panic event detection")
    
    # Detection criteria:
    # 1. Sentiment score drops below threshold
    # 2. Price drops significantly (>5% in 24h)
    # 3. News volume spikes
    
    # Placeholder - actual implementation would:
    # - Query recent news sentiment by stock
    # - Check price movements
    # - Create panic_events records
    
    detected_count = 0
    
    logger.info("Panic detection complete", detected=detected_count)
    return {"detected": detected_count}


@celery_app.task(name="app.workers.tasks.pattern_tasks.generate_buy_signals")
def generate_buy_signals():
    """Generate buy signals based on historical patterns."""
    logger.info("Starting buy signal generation")
    
    # Signal generation criteria:
    # 1. Active panic event exists
    # 2. Similar historical events had good recovery
    # 3. Price is near or past typical bottom
    # 4. Volume indicates capitulation
    
    # Placeholder - actual implementation would:
    # - Find stocks with active panic events
    # - Match against historical patterns
    # - Calculate confidence scores
    # - Create buy_signals records
    
    signals_count = 0
    
    logger.info("Signal generation complete", signals=signals_count)
    return {"generated": signals_count}


@celery_app.task(name="app.workers.tasks.pattern_tasks.analyze_historical_pattern")
def analyze_historical_pattern(stock_id: str, event_type: str):
    """Analyze historical patterns for a specific stock and event type."""
    logger.info("Analyzing historical pattern", stock_id=stock_id, event_type=event_type)
    
    # Would query past panic events for this stock
    # Calculate statistics:
    # - Average/median drawdown
    # - Average/median recovery time
    # - Average/median recovery percentage
    # - Success rate (how often did it recover)
    
    return {
        "stock_id": stock_id,
        "event_type": event_type,
        "pattern": None,  # Would contain pattern data
    }


@celery_app.task(name="app.workers.tasks.pattern_tasks.calculate_confidence_score")
def calculate_confidence_score(
    stock_id: str,
    current_drawdown: float,
    current_sentiment: float,
    historical_pattern: dict,
) -> float:
    """Calculate confidence score for a potential buy signal."""
    
    # Factors:
    # 1. How close is current drawdown to historical average
    # 2. How negative is sentiment (capitulation indicator)
    # 3. Pattern reliability (sample size, consistency)
    # 4. Market conditions (sector performance, overall market)
    
    score = 0.5  # Base score
    
    # Adjust based on drawdown match
    if historical_pattern and historical_pattern.get("avg_drawdown"):
        avg_drawdown = historical_pattern["avg_drawdown"]
        drawdown_ratio = current_drawdown / avg_drawdown
        if 0.8 <= drawdown_ratio <= 1.2:
            score += 0.2  # Close to historical average
    
    # Adjust based on sentiment (more panic = higher score)
    if current_sentiment < settings.PANIC_THRESHOLD:
        panic_intensity = abs(current_sentiment - settings.PANIC_THRESHOLD) / 50
        score += min(panic_intensity * 0.2, 0.2)
    
    # Adjust based on pattern reliability
    if historical_pattern and historical_pattern.get("event_count", 0) >= 3:
        score += 0.1  # Multiple historical examples
    
    return min(max(score, 0), 1)  # Clamp to 0-1


@celery_app.task(name="app.workers.tasks.pattern_tasks.update_panic_event_status")
def update_panic_event_status():
    """Update panic events - check if they've ended/recovered."""
    logger.info("Updating panic event statuses")
    
    # Check active (incomplete) panic events
    # Mark as complete if:
    # - Price has recovered to pre-panic levels
    # - Sentiment has normalized
    # - Enough time has passed
    
    updated_count = 0
    
    logger.info("Panic status update complete", updated=updated_count)
    return {"updated": updated_count}


@celery_app.task(name="app.workers.tasks.pattern_tasks.rebuild_patterns")
def rebuild_patterns(stock_id: str = None):
    """Rebuild historical patterns from panic events."""
    logger.info("Rebuilding patterns", stock_id=stock_id or "all")
    
    # Aggregate all completed panic events
    # Group by stock and event type
    # Calculate pattern statistics
    
    patterns_count = 0
    
    logger.info("Pattern rebuild complete", patterns=patterns_count)
    return {"rebuilt": patterns_count}