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/stock.py

@@ -0,0 +1,26 @@
+"""Stock model."""
+
+from sqlalchemy import Column, String, BigInteger, Boolean, DateTime
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.sql import func
+import uuid
+
+from app.core.database import Base
+
+
+class Stock(Base):
+    """Stock table model."""
+    
+    __tablename__ = "stocks"
+    
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    symbol = Column(String(20), unique=True, nullable=False, index=True)
+    name = Column(String(255), nullable=False)
+    sector = Column(String(100), index=True)
+    industry = Column(String(100), index=True)
+    market_cap = Column(BigInteger)
+    exchange = Column(String(50))
+    country = Column(String(100), default="USA")
+    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())