074787f067
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
47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
"""Buy signal model."""
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from sqlalchemy import Column, String, DateTime, Numeric, Integer, ForeignKey
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from sqlalchemy.dialects.postgresql import UUID
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from sqlalchemy.sql import func
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import uuid
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from app.core.database import Base
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class BuySignal(Base):
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"""Buy signal table model."""
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__tablename__ = "buy_signals"
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id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
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stock_id = Column(UUID(as_uuid=True), ForeignKey("stocks.id", ondelete="CASCADE"), nullable=False, index=True)
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panic_event_id = Column(UUID(as_uuid=True), ForeignKey("panic_events.id", ondelete="SET NULL"))
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# Signal details
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signal_time = Column(DateTime(timezone=True), nullable=False, server_default=func.now(), index=True)
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signal_price = Column(Numeric(15, 4), nullable=False)
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# Confidence scoring
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confidence_score = Column(Numeric(5, 4), nullable=False, index=True) # 0 to 1
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# Based on pattern
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pattern_id = Column(UUID(as_uuid=True), ForeignKey("historical_patterns.id", ondelete="SET NULL"))
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expected_recovery_percent = Column(Numeric(8, 4))
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expected_recovery_days = Column(Integer)
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# Current metrics
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current_drawdown_percent = Column(Numeric(8, 4))
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current_sentiment_score = Column(Numeric(5, 2))
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# Signal status
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status = Column(String(20), default="active", index=True) # active, triggered, expired, cancelled
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triggered_at = Column(DateTime(timezone=True))
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# Outcome tracking
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outcome_price = Column(Numeric(15, 4))
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outcome_percent = Column(Numeric(8, 4))
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outcome_days = Column(Integer)
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created_at = Column(DateTime(timezone=True), server_default=func.now())
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updated_at = Column(DateTime(timezone=True), server_default=func.now(), onupdate=func.now())
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