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/signal.py

@@ -0,0 +1,46 @@
+"""Buy signal model."""
+
+from sqlalchemy import Column, String, DateTime, Numeric, Integer, ForeignKey
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.sql import func
+import uuid
+
+from app.core.database import Base
+
+
+class BuySignal(Base):
+    """Buy signal table model."""
+    
+    __tablename__ = "buy_signals"
+    
+    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)
+    panic_event_id = Column(UUID(as_uuid=True), ForeignKey("panic_events.id", ondelete="SET NULL"))
+    
+    # Signal details
+    signal_time = Column(DateTime(timezone=True), nullable=False, server_default=func.now(), index=True)
+    signal_price = Column(Numeric(15, 4), nullable=False)
+    
+    # Confidence scoring
+    confidence_score = Column(Numeric(5, 4), nullable=False, index=True)  # 0 to 1
+    
+    # Based on pattern
+    pattern_id = Column(UUID(as_uuid=True), ForeignKey("historical_patterns.id", ondelete="SET NULL"))
+    expected_recovery_percent = Column(Numeric(8, 4))
+    expected_recovery_days = Column(Integer)
+    
+    # Current metrics
+    current_drawdown_percent = Column(Numeric(8, 4))
+    current_sentiment_score = Column(Numeric(5, 2))
+    
+    # Signal status
+    status = Column(String(20), default="active", index=True)  # active, triggered, expired, cancelled
+    triggered_at = Column(DateTime(timezone=True))
+    
+    # Outcome tracking
+    outcome_price = Column(Numeric(15, 4))
+    outcome_percent = Column(Numeric(8, 4))
+    outcome_days = Column(Integer)
+    
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())