trading/services/api_gateway/routes/news.py

87 lines
3.1 KiB
Python

"""News endpoints — recent scored articles with filtering."""
from __future__ import annotations
from fastapi import APIRouter, Depends, Query, Request
from services.api_gateway.auth.middleware import get_current_user
from sqlalchemy import select, desc, func
router = APIRouter(prefix="/api/news", tags=["news"])
@router.get("")
async def list_news(
request: Request,
_user: dict = Depends(get_current_user),
ticker: str | None = Query(default=None),
source: str | None = Query(default=None),
min_score: float | None = Query(default=None, ge=-1.0, le=1.0),
max_score: float | None = Query(default=None, ge=-1.0, le=1.0),
page: int = Query(default=1, ge=1),
per_page: int = Query(default=20, ge=1, le=100),
) -> dict:
"""Recent scored articles with optional filters."""
from shared.models.news import Article, ArticleSentiment
db = request.app.state.db_session_factory
async with db() as session:
# Base query joining articles with sentiments
query = (
select(Article, ArticleSentiment)
.join(ArticleSentiment, Article.id == ArticleSentiment.article_id)
.order_by(desc(Article.fetched_at))
)
count_query = (
select(func.count())
.select_from(Article)
.join(ArticleSentiment, Article.id == ArticleSentiment.article_id)
)
if ticker:
query = query.where(ArticleSentiment.ticker == ticker.upper())
count_query = count_query.where(
ArticleSentiment.ticker == ticker.upper()
)
if source:
query = query.where(Article.source == source)
count_query = count_query.where(Article.source == source)
if min_score is not None:
query = query.where(ArticleSentiment.score >= min_score)
count_query = count_query.where(ArticleSentiment.score >= min_score)
if max_score is not None:
query = query.where(ArticleSentiment.score <= max_score)
count_query = count_query.where(ArticleSentiment.score <= max_score)
total = (await session.execute(count_query)).scalar() or 0
offset = (page - 1) * per_page
query = query.offset(offset).limit(per_page)
result = await session.execute(query)
rows = result.all()
return {
"articles": [
{
"id": str(article.id),
"source": article.source,
"url": article.url,
"title": article.title,
"published_at": (
article.published_at.isoformat()
if article.published_at
else None
),
"fetched_at": article.fetched_at.isoformat(),
"ticker": sentiment.ticker,
"sentiment_score": sentiment.score,
"confidence": sentiment.confidence,
"model_used": sentiment.model_used,
}
for article, sentiment in rows
],
"total": total,
"page": page,
"per_page": per_page,
"pages": (total + per_page - 1) // per_page if per_page else 0,
}