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>
63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
"""Sentiment router — sentiment data endpoints."""
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import json
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from fastapi import APIRouter, Depends, Query
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.dependencies import get_db, require_access
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from app.schemas.common import APIEnvelope
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from app.schemas.sentiment import CitationItem, SentimentResponse, SentimentScoreResult
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from app.services.sentiment_service import (
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compute_sentiment_dimension_score,
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get_sentiment_scores,
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)
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router = APIRouter(tags=["sentiment"])
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def _parse_citations(citations_json: str) -> list[CitationItem]:
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"""Deserialize citations_json, defaulting to [] on invalid JSON."""
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try:
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raw = json.loads(citations_json)
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except (json.JSONDecodeError, TypeError):
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return []
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if not isinstance(raw, list):
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return []
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return [CitationItem(**item) for item in raw if isinstance(item, dict)]
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@router.get("/sentiment/{symbol}", response_model=APIEnvelope)
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async def read_sentiment(
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symbol: str,
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lookback_hours: float = Query(168, gt=0, description="Lookback window in hours"),
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_user=Depends(require_access),
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db: AsyncSession = Depends(get_db),
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) -> APIEnvelope:
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"""Get recent sentiment scores and computed dimension score for a symbol."""
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scores = await get_sentiment_scores(db, symbol, lookback_hours)
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dimension_score = await compute_sentiment_dimension_score(
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db, symbol, lookback_hours
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)
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data = SentimentResponse(
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symbol=symbol.strip().upper(),
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scores=[
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SentimentScoreResult(
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id=s.id,
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classification=s.classification,
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confidence=s.confidence,
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source=s.source,
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timestamp=s.timestamp,
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reasoning=s.reasoning,
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citations=_parse_citations(s.citations_json),
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recommendation=s.recommendation,
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)
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for s in scores
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],
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count=len(scores),
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dimension_score=round(dimension_score, 2) if dimension_score is not None else None,
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lookback_hours=lookback_hours,
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)
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return APIEnvelope(status="success", data=data.model_dump())
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