074787f067
Features: - FastAPI backend with stocks, news, signals, watchlist, analytics endpoints - React frontend with TailwindCSS dark mode trading dashboard - Celery workers for news fetching, sentiment analysis, pattern detection - TimescaleDB schema for time-series stock data - Docker Compose setup for all services - OpenAI integration for sentiment analysis
123 lines
3.9 KiB
Python
123 lines
3.9 KiB
Python
"""
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News API Endpoints
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"""
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from typing import List, Optional
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from datetime import datetime, timedelta
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from uuid import UUID
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from fastapi import APIRouter, Depends, HTTPException, Query
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, desc
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from app.core.database import get_db
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from app.models.news import NewsArticle
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from app.schemas.news import NewsResponse, NewsWithSentiment
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router = APIRouter()
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@router.get("/", response_model=List[NewsResponse])
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async def list_news(
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db: AsyncSession = Depends(get_db),
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source: Optional[str] = Query(None, description="Filter by source"),
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sentiment: Optional[str] = Query(None, description="Filter by sentiment: positive, negative, neutral"),
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hours: int = Query(24, ge=1, le=168, description="News from last N hours"),
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skip: int = Query(0, ge=0),
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limit: int = Query(50, ge=1, le=100),
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):
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"""List recent news articles."""
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since = datetime.utcnow() - timedelta(hours=hours)
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query = select(NewsArticle).where(NewsArticle.published_at >= since)
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if source:
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query = query.where(NewsArticle.source == source)
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if sentiment:
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query = query.where(NewsArticle.sentiment_label == sentiment)
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query = query.order_by(desc(NewsArticle.published_at)).offset(skip).limit(limit)
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result = await db.execute(query)
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return result.scalars().all()
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@router.get("/sources")
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async def list_sources(db: AsyncSession = Depends(get_db)):
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"""Get list of all news sources."""
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query = select(NewsArticle.source).distinct()
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result = await db.execute(query)
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sources = [row[0] for row in result.fetchall() if row[0]]
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return {"sources": sorted(sources)}
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@router.get("/stock/{symbol}", response_model=List[NewsWithSentiment])
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async def get_news_for_stock(
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symbol: str,
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db: AsyncSession = Depends(get_db),
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hours: int = Query(72, ge=1, le=720, description="News from last N hours"),
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skip: int = Query(0, ge=0),
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limit: int = Query(50, ge=1, le=100),
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):
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"""Get news articles mentioning a specific stock."""
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# This would use the news_stock_mentions join table
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# For now, we search in title/content
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since = datetime.utcnow() - timedelta(hours=hours)
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search_term = f"%{symbol.upper()}%"
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query = (
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select(NewsArticle)
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.where(NewsArticle.published_at >= since)
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.where(
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(NewsArticle.title.ilike(search_term)) |
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(NewsArticle.content.ilike(search_term))
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)
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.order_by(desc(NewsArticle.published_at))
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.offset(skip)
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.limit(limit)
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)
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result = await db.execute(query)
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return result.scalars().all()
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@router.get("/panic")
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async def get_panic_news(
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db: AsyncSession = Depends(get_db),
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threshold: float = Query(-50.0, ge=-100, le=0, description="Sentiment threshold"),
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hours: int = Query(24, ge=1, le=168, description="News from last N hours"),
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limit: int = Query(20, ge=1, le=50),
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):
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"""Get the most panic-inducing news articles."""
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since = datetime.utcnow() - timedelta(hours=hours)
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query = (
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select(NewsArticle)
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.where(NewsArticle.published_at >= since)
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.where(NewsArticle.sentiment_score <= threshold)
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.order_by(NewsArticle.sentiment_score.asc())
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.limit(limit)
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)
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result = await db.execute(query)
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articles = result.scalars().all()
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return {
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"threshold": threshold,
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"hours": hours,
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"count": len(articles),
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"articles": articles,
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}
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@router.get("/{article_id}", response_model=NewsWithSentiment)
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async def get_article(
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article_id: UUID,
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db: AsyncSession = Depends(get_db),
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):
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"""Get a specific news article with full details."""
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query = select(NewsArticle).where(NewsArticle.id == article_id)
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result = await db.execute(query)
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article = result.scalar_one_or_none()
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if not article:
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raise HTTPException(status_code=404, detail="Article not found")
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return article
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