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/news.py

@@ -0,0 +1,34 @@
+"""News article model."""
+
+from sqlalchemy import Column, String, Text, Boolean, DateTime, Numeric
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.sql import func
+import uuid
+
+from app.core.database import Base
+
+
+class NewsArticle(Base):
+    """News article table model."""
+    
+    __tablename__ = "news_articles"
+    
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    title = Column(Text, nullable=False)
+    content = Column(Text)
+    summary = Column(Text)
+    url = Column(Text, unique=True, nullable=False)
+    source = Column(String(100), nullable=False, index=True)
+    author = Column(String(255))
+    published_at = Column(DateTime(timezone=True), nullable=False, index=True)
+    fetched_at = Column(DateTime(timezone=True), server_default=func.now())
+    image_url = Column(Text)
+    
+    # Sentiment analysis results
+    sentiment_score = Column(Numeric(5, 2), index=True)  # -100 to +100
+    sentiment_label = Column(String(20))  # negative, neutral, positive
+    sentiment_confidence = Column(Numeric(5, 4))
+    
+    # Processing status
+    is_processed = Column(Boolean, default=False)
+    processing_error = Column(Text)