d36ae40df1
- Point Ollama to local instance via host.docker.internal, use gemma3 model - Remove Docker Ollama service (using host's Ollama instead) - Add company-name-to-ticker mapping (Apple→AAPL, Tesla→TSLA, etc.) for RSS articles - Lower signal thresholds for faster feedback with paper trading: - FinBERT confidence: 0.6→0.4, signal strength: 0.3→0.15 - News strategy: article_count 2→1, confidence 0.5→0.3, score ±0.3→±0.15 - Fix market data BarSet access bug (BarSet.__contains__ returns False incorrectly) - Fix market data SIP feed error by switching to IEX feed for free Alpaca accounts - Fix nginx proxy routing for /api/auth/* to api-gateway /auth/* - Add seed_sample_data script - Update tests for new thresholds and alpaca mock modules
91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
"""News endpoints — recent scored articles with filtering."""
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from __future__ import annotations
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from fastapi import APIRouter, Depends, Query, Request
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from services.api_gateway.auth.middleware import get_current_user
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from sqlalchemy import select, desc, func
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router = APIRouter(prefix="/api/news", tags=["news"])
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@router.get("")
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async def list_news(
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request: Request,
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_user: dict = Depends(get_current_user),
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ticker: str | None = Query(default=None),
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source: str | None = Query(default=None),
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min_score: float | None = Query(default=None, ge=-1.0, le=1.0),
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max_score: float | None = Query(default=None, ge=-1.0, le=1.0),
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page: int = Query(default=1, ge=1),
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per_page: int = Query(default=20, ge=1, le=100),
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page_size: int | None = Query(default=None, ge=1, le=100),
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) -> dict:
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"""Recent scored articles with optional filters."""
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from shared.models.news import Article, ArticleSentiment
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effective_per_page = page_size if page_size is not None else per_page
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db = request.app.state.db_session_factory
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async with db() as session:
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# Base query joining articles with sentiments
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query = (
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select(Article, ArticleSentiment)
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.join(ArticleSentiment, Article.id == ArticleSentiment.article_id)
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.order_by(desc(Article.fetched_at))
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)
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count_query = (
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select(func.count())
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.select_from(Article)
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.join(ArticleSentiment, Article.id == ArticleSentiment.article_id)
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)
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if ticker:
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query = query.where(ArticleSentiment.ticker == ticker.upper())
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count_query = count_query.where(
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ArticleSentiment.ticker == ticker.upper()
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)
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if source:
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query = query.where(Article.source == source)
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count_query = count_query.where(Article.source == source)
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if min_score is not None:
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query = query.where(ArticleSentiment.score >= min_score)
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count_query = count_query.where(ArticleSentiment.score >= min_score)
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if max_score is not None:
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query = query.where(ArticleSentiment.score <= max_score)
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count_query = count_query.where(ArticleSentiment.score <= max_score)
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total = (await session.execute(count_query)).scalar() or 0
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offset = (page - 1) * effective_per_page
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query = query.offset(offset).limit(effective_per_page)
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result = await session.execute(query)
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rows = result.all()
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return {
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"articles": [
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{
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"id": str(article.id),
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"source": article.source,
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"url": article.url,
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"title": article.title,
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"published_at": (
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article.published_at.isoformat()
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if article.published_at
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else None
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),
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"fetched_at": article.fetched_at.isoformat(),
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"ticker": sentiment.ticker,
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"sentiment_score": sentiment.score,
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"confidence": sentiment.confidence,
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"model_used": sentiment.model_used,
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}
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for article, sentiment in rows
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],
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"total": total,
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"page": page,
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"page_size": effective_per_page,
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"per_page": effective_per_page,
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"pages": (total + effective_per_page - 1) // effective_per_page if effective_per_page else 0,
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}
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