Initial commit: Gold Trading Simulator with AI-powered analysis

backend/app/api/news.py

@@ -0,0 +1,147 @@
+from fastapi import APIRouter, HTTPException, Query
+from typing import List
+from app.services.news_service import news_service
+from app.services.alert_service import alert_service
+from app.schemas.schemas import (
+    NewsFeedResponse,
+    EconomicCalendarResponse,
+    AlertsResponse,
+    CorrelationAnalysisResponse,
+)
+
+router = APIRouter(prefix="/news", tags=["News & Sentiment"])
+
+
+@router.get("/feed", response_model=NewsFeedResponse)
+async def get_news_feed(
+    limit: int = Query(50, description="Maximum number of articles to return"),
+):
+    """
+    Get aggregated news feed from multiple sources with sentiment analysis
+
+    Features:
+    - Fetches from Alpha Vantage News Sentiment API
+    - Fetches from Finnhub (if API key provided)
+    - Filters for gold-relevant news
+    - Performs sentiment analysis
+    - Categorizes by impact type
+    - Calculates relevance scores
+    - Provides overall market sentiment
+    """
+    try:
+        news_feed = await news_service.get_aggregated_news_feed()
+
+        # Limit articles
+        news_feed.articles = news_feed.articles[:limit]
+
+        # Generate alerts for high-impact news
+        for article in news_feed.articles:
+            if article.impact_on_gold == "HIGH":
+                alert_service.add_news_alert(
+                    news_title=article.title,
+                    impact=article.impact_on_gold,
+                    sentiment=article.sentiment.value,
+                )
+
+        return news_feed
+
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to fetch news feed: {str(e)}"
+        )
+
+
+@router.get("/economic-calendar", response_model=EconomicCalendarResponse)
+async def get_economic_calendar():
+    """
+    Get upcoming economic events that may impact gold prices
+
+    Includes:
+    - Federal Reserve meetings
+    - Employment reports
+    - Inflation data (CPI, PPI)
+    - GDP releases
+    - Central bank decisions
+    """
+    try:
+        calendar = await news_service.get_economic_calendar()
+        return calendar
+
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to fetch economic calendar: {str(e)}"
+        )
+
+
+@router.get("/alerts", response_model=AlertsResponse)
+async def get_alerts(
+    limit: int = Query(50, description="Maximum number of alerts to return"),
+):
+    """
+    Get recent alerts for price movements and news events
+
+    Alert Types:
+    - PRICE_SPIKE: Significant upward price movement
+    - PRICE_DROP: Significant downward price movement
+    - NEWS_BREAKING: High-impact breaking news
+    - SUPPORT_BREACH: Price broke below support level
+    - RESISTANCE_BREACH: Price broke above resistance level
+    - HIGH_VOLATILITY: Unusual price volatility detected
+    - ECONOMIC_EVENT: Upcoming important economic release
+    """
+    try:
+        alerts = alert_service.get_alerts(limit=limit)
+        return alerts
+
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to fetch alerts: {str(e)}"
+        )
+
+
+@router.get("/correlation", response_model=CorrelationAnalysisResponse)
+async def get_news_price_correlation():
+    """
+    Analyze correlation between news events and price movements
+
+    Shows:
+    - How price reacted to specific news
+    - Time delay between news and price change
+    - Correlation strength (STRONG/MODERATE/WEAK)
+    - Average price impact from news
+    """
+    try:
+        # Get recent news and price data
+        news_feed = await news_service.get_aggregated_news_feed()
+
+        # Would need price data here - for MVP return empty
+        # In full implementation, fetch from market service
+        correlation = alert_service.analyze_news_price_correlation(
+            news_articles=news_feed.articles[:20],
+            price_data=[],  # Would pass actual price data
+        )
+
+        return correlation
+
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to analyze correlation: {str(e)}"
+        )
+
+
+@router.post("/alerts/clear")
+async def clear_old_alerts():
+    """Clear alerts older than 24 hours"""
+    try:
+        alert_service.clear_old_alerts(hours=24)
+        return {"message": "Old alerts cleared successfully"}
+
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to clear alerts: {str(e)}"
+        )