Initial project structure: MarketScanner - Fear-to-Fortune Trading Intelligence

Features:
- FastAPI backend with stocks, news, signals, watchlist, analytics endpoints
- React frontend with TailwindCSS dark mode trading dashboard
- Celery workers for news fetching, sentiment analysis, pattern detection
- TimescaleDB schema for time-series stock data
- Docker Compose setup for all services
- OpenAI integration for sentiment analysis

backend/app/models/panic.py

@@ -0,0 +1,48 @@
+"""Panic event model."""
+
+from sqlalchemy import Column, String, Text, Boolean, DateTime, Numeric, Integer, ForeignKey
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.sql import func
+import uuid
+
+from app.core.database import Base
+
+
+class PanicEvent(Base):
+    """Panic event table model."""
+    
+    __tablename__ = "panic_events"
+    
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    stock_id = Column(UUID(as_uuid=True), ForeignKey("stocks.id", ondelete="CASCADE"), nullable=False, index=True)
+    
+    # Event timing
+    start_time = Column(DateTime(timezone=True), nullable=False, index=True)
+    peak_time = Column(DateTime(timezone=True))
+    end_time = Column(DateTime(timezone=True))
+    
+    # Price impact
+    price_at_start = Column(Numeric(15, 4), nullable=False)
+    price_at_peak_panic = Column(Numeric(15, 4))
+    price_at_end = Column(Numeric(15, 4))
+    max_drawdown_percent = Column(Numeric(8, 4), index=True)
+    
+    # Sentiment
+    avg_sentiment_score = Column(Numeric(5, 2))
+    min_sentiment_score = Column(Numeric(5, 2))
+    news_volume = Column(Integer)
+    
+    # Recovery metrics
+    recovery_time_days = Column(Integer)
+    recovery_percent = Column(Numeric(8, 4))
+    
+    # Classification
+    event_type = Column(String(100), index=True)  # earnings_miss, scandal, lawsuit, macro, etc.
+    event_category = Column(String(50))  # company_specific, sector_wide, market_wide
+    
+    # Analysis
+    is_complete = Column(Boolean, default=False)
+    notes = Column(Text)
+    
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())