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/watchlist.py

@@ -0,0 +1,30 @@
+"""Watchlist 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 Watchlist(Base):
+    """Watchlist table model."""
+    
+    __tablename__ = "watchlist"
+    
+    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, unique=True)
+    
+    # Alert thresholds
+    panic_alert_threshold = Column(Numeric(5, 2), default=-50)
+    price_alert_low = Column(Numeric(15, 4))
+    price_alert_high = Column(Numeric(15, 4))
+    
+    # Preferences
+    priority = Column(Integer, default=1, index=True)  # 1 = high, 2 = medium, 3 = low
+    notes = Column(Text)
+    
+    is_active = Column(Boolean, default=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())