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>
41 lines
986 B
Python
41 lines
986 B
Python
"""Pydantic schemas for sentiment endpoints."""
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from __future__ import annotations
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from datetime import datetime
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from typing import Literal
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from pydantic import BaseModel, Field
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class CitationItem(BaseModel):
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"""A single citation from the sentiment analysis."""
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url: str
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title: str
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class SentimentScoreResult(BaseModel):
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"""A single sentiment score record."""
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id: int
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classification: Literal["bullish", "bearish", "neutral"]
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confidence: int = Field(ge=0, le=100)
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source: str
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timestamp: datetime
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reasoning: str = ""
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citations: list[CitationItem] = []
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recommendation: Literal["buy", "hold", "avoid"] | None = None
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class SentimentResponse(BaseModel):
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"""Envelope-ready sentiment response."""
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symbol: str
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scores: list[SentimentScoreResult]
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count: int
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dimension_score: float | None = Field(
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None, ge=0, le=100, description="Time-decay weighted sentiment dimension score"
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)
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lookback_hours: float
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