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
49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
"""Panic event model."""
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from sqlalchemy import Column, String, Text, Boolean, 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 PanicEvent(Base):
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"""Panic event table model."""
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__tablename__ = "panic_events"
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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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# Event timing
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start_time = Column(DateTime(timezone=True), nullable=False, index=True)
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peak_time = Column(DateTime(timezone=True))
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end_time = Column(DateTime(timezone=True))
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# Price impact
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price_at_start = Column(Numeric(15, 4), nullable=False)
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price_at_peak_panic = Column(Numeric(15, 4))
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price_at_end = Column(Numeric(15, 4))
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max_drawdown_percent = Column(Numeric(8, 4), index=True)
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# Sentiment
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avg_sentiment_score = Column(Numeric(5, 2))
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min_sentiment_score = Column(Numeric(5, 2))
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news_volume = Column(Integer)
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# Recovery metrics
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recovery_time_days = Column(Integer)
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recovery_percent = Column(Numeric(8, 4))
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# Classification
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event_type = Column(String(100), index=True) # earnings_miss, scandal, lawsuit, macro, etc.
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event_category = Column(String(50)) # company_specific, sector_wide, market_wide
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# Analysis
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is_complete = Column(Boolean, default=False)
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notes = Column(Text)
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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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