dennisthiessen
e5166ed668
Richer LLM output (same grounded call, ~no extra cost): - All providers now also return a recommendation (buy/hold/avoid) and a thorough reasoning paragraph; Gemini now actually captures reasoning + grounding citations (it was dropping them). Stored on sentiment_scores (migration 008), exposed in the API; display-only — NOT fed into the composite/EV. - Ticker Sentiment panel shows an "LLM view" badge and a "Full analysis & sources" expander with the complete reasoning + citations. Search-budget scoping (Gemini grounding free tier = 5000/mo): - collect_sentiment now targets only watchlist + open paper trades + top-N by composite, skips tickers refreshed within sentiment_fresh_hours (72h), and caps per run (sentiment_max_per_run). Once the relevant set is fresh, runs spend 0 searches until it ages out — bounding monthly usage well under the free tier. - Widened sentiment lookback to 7d (scoring + display) so sparser collection still feeds the dimension score. Deploy: alembic upgrade (sentiment_scores.recommendation). Switch provider to Gemini Flash in Admin for the cost win (grounded, cheapest). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
28 lines
1.0 KiB
Python
28 lines
1.0 KiB
Python
from datetime import datetime
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from sqlalchemy import DateTime, ForeignKey, Integer, String, Text
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from app.database import Base
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class SentimentScore(Base):
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__tablename__ = "sentiment_scores"
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id: Mapped[int] = mapped_column(primary_key=True)
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ticker_id: Mapped[int] = mapped_column(
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ForeignKey("tickers.id", ondelete="CASCADE"), nullable=False
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)
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classification: Mapped[str] = mapped_column(String(20), nullable=False)
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confidence: Mapped[int] = mapped_column(Integer, nullable=False)
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source: Mapped[str] = mapped_column(String(100), nullable=False)
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timestamp: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False
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)
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reasoning: Mapped[str] = mapped_column(Text, nullable=False, default="")
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citations_json: Mapped[str] = mapped_column(Text, nullable=False, default="[]")
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recommendation: Mapped[str | None] = mapped_column(String(10), nullable=True)
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ticker = relationship("Ticker", back_populates="sentiment_scores")
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