eea4469095
- Complete MVP for tracking Fidelity brokerage account performance - Transaction import from CSV with deduplication - Automatic FIFO position tracking with options support - Real-time P&L calculations with market data caching - Dashboard with timeframe filtering (30/90/180 days, 1 year, YTD, all time) - Docker-based deployment with PostgreSQL backend - React/TypeScript frontend with TailwindCSS - FastAPI backend with SQLAlchemy ORM Features: - Multi-account support - Import via CSV upload or filesystem - Open and closed position tracking - Balance history charting - Performance analytics and metrics - Top trades analysis - Responsive UI design Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
30 lines
1012 B
Python
30 lines
1012 B
Python
"""Market price cache model for storing Yahoo Finance data."""
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from sqlalchemy import Column, Integer, String, Numeric, DateTime, Index
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from datetime import datetime
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from app.database import Base
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class MarketPrice(Base):
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"""
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Cache table for market prices from Yahoo Finance.
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Stores the last fetched price for each symbol to reduce API calls.
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"""
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__tablename__ = "market_prices"
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id = Column(Integer, primary_key=True, index=True)
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symbol = Column(String(20), unique=True, nullable=False, index=True)
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price = Column(Numeric(precision=20, scale=6), nullable=False)
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fetched_at = Column(DateTime, nullable=False, default=datetime.utcnow)
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source = Column(String(50), default="yahoo_finance")
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# Index for quick lookups by symbol and freshness checks
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__table_args__ = (
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Index('idx_symbol_fetched', 'symbol', 'fetched_at'),
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)
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def __repr__(self):
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return f"<MarketPrice(symbol={self.symbol}, price={self.price}, fetched_at={self.fetched_at})>"
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