News Intelligence Agent - 뉴스 인텔리전스

Role

/news 페이지에서 배치로 여러 뉴스를 동시 분석하여 시장 전체 흐름을 파악합니다.

Core Capabilities

1. Batch News Processing

async def analyze_batch( news_articles: List[NewsArticle], batch_size: int = 50 ) -> Dict: """Process multiple articles in parallel"""

results = []

# Process in batches to avoid API rate limits
for i in range(0, len(news_articles), batch_size):
    batch = news_articles[i:i+batch_size]
    
    # Parallel processing
    batch_results = await asyncio.gather(*[
        analyze_single_article(article)
        for article in batch
    ])
    
    results.extend(batch_results)

return aggregate_batch_results(results)

2. Sentiment Analysis

Sentiment Scoring

def calculate_sentiment(text: str) -> float: """Calculate sentiment score -1 to +1"""

# Positive keywords
positive = ["surge", "beat", "record", "growth", "bullish", "upgrade"]

# Negative keywords
negative = ["plunge", "miss", "loss", "decline", "bearish", "downgrade"]

# Count occurrences
pos_count = sum(text.lower().count(word) for word in positive)
neg_count = sum(text.lower().count(word) for word in negative)

# Normalize
total = pos_count + neg_count
if total == 0:
    return 0.0

sentiment = (pos_count - neg_count) /total

# Clamp to [-1, 1]
return max(-1.0, min(1.0, sentiment))

Sentiment Categories

SENTIMENT_LEVELS = { "VERY_POSITIVE": (0.6, 1.0), "POSITIVE": (0.3, 0.6), "NEUTRAL": (-0.3, 0.3), "NEGATIVE": (-0.6, -0.3), "VERY_NEGATIVE": (-1.0, -0.6) }

3. Keyword Extraction

from sklearn.feature_extraction.text import TfidfVectorizer

def extract_keywords(texts: List[str], top_n: int = 10) -> List[str]: """Extract important keywords using TF-IDF"""

vectorizer = TfidfVectorizer(
    max_features=top_n,
    stop_words='english',
    ngram_range=(1, 2)  # Unigrams and bigrams
)

tfidf_matrix = vectorizer.fit_transform(texts)

feature_names = vectorizer.get_feature_names_out()

# Get top keywords
scores = tfidf_matrix.sum(axis=0).A1
top_indices = scores.argsort()[-top_n:][::-1]

keywords = [feature_names[i] for i in top_indices]

return keywords

4. Theme Detection

from sklearn.cluster import KMeans from sklearn.feature_extraction.text import TfidfVectorizer

def detect_themes(articles: List[str], n_themes: int = 5) -> List[Dict]: """Cluster articles into themes"""

# Vectorize
vectorizer = TfidfVectorizer(max_features=100, stop_words='english')
tfidf_matrix = vectorizer.fit_transform(articles)

# Cluster
kmeans = KMeans(n_clusters=n_themes, random_state=42)
kmeans.fit(tfidf_matrix)

# Extract theme keywords
themes = []
feature_names = vectorizer.get_feature_names_out()

for i, cluster_center in enumerate(kmeans.cluster_centers_):
    top_indices = cluster_center.argsort()[-5:][::-1]
    theme_keywords = [feature_names[idx] for idx in top_indices]
    
    # Count articles in theme
    article_count = (kmeans.labels_ == i).sum()
    
    themes.append({
        "theme_id": i,
        "keywords": theme_keywords,
        "article_count": article_count,
        "theme_name": generate_theme_name(theme_keywords)
    })

return sorted(themes, key=lambda x: x['article_count'], reverse=True)

5. Ticker Buzz Score

def calculate_ticker_buzz( ticker: str, news_articles: List[NewsArticle], timeframe_hours: int = 24 ) -> Dict: """Calculate how much a ticker is being discussed"""

# Filter articles mentioning ticker
ticker_articles = [
    a for a in news_articles
    if ticker in (a.ticker or '') or ticker in (a.content or '').upper()
]

# Recency weight (more recent = higher weight)
now = datetime.now()
weighted_mentions = 0

for article in ticker_articles:
    hours_ago = (now - article.created_at).total_seconds() / 3600
    
    if hours_ago <= 0 timeframe_hours:
        # Exponential decay
        weight = math.exp(-hours_ago / (timeframe_hours / 2))
        weighted_mentions += weight

# Normalize to 0-100 scale
buzz_score = min(100, weighted_mentions * 10)

# Sentiment breakdown
sentiments = [a.sentiment_score for a in ticker_articles if a.sentiment_score]
avg_sentiment = sum(sentiments) / len(sentiments) if sentiments else 0

return {
    "ticker": ticker,
    "buzz_score": buzz_score,
    "mention_count": len(ticker_articles),
    "avg_sentiment": avg_sentiment,
    "timeframe_hours": timeframe_hours,
    "trending": "UP" if buzz_score > 50 else "NORMAL"
}

Decision Framework

Step 1: Fetch News Articles articles = db.query(NewsArticle).filter( NewsArticle.created_at >= datetime.now() - timedelta(hours=24) ).all()

Step 2: Batch Sentiment Analysis FOR each article in articles: sentiment = calculate_sentiment(article.content) article.sentiment_score = sentiment article.sentiment_label = categorize_sentiment(sentiment)

Step 3: Extract Keywords all_text = [a.content for a in articles] keywords = extract_keywords(all_text, top_n=20)

Step 4: Detect Themes themes = detect_themes([a.headline + ' ' + a.content for a in articles])

Step 5: Calculate Ticker Buzz unique_tickers = set(a.ticker for a in articles if a.ticker)

buzz_scores = {} FOR ticker in unique_tickers: buzz_scores[ticker] = calculate_ticker_buzz(ticker, articles)

Step 6: Aggregate Results return { "total_articles": len(articles), "sentiment_distribution": count_by_sentiment(articles), "top_keywords": keywords, "trending_themes": themes, "ticker_buzz": buzz_scores, "timestamp": datetime.now() }

Output Format

{ "analysis_timestamp": "2025-12-21T13:00:00Z", "timeframe": "last_24_hours", "total_articles_analyzed": 237,

"sentiment_distribution": { "VERY_POSITIVE": 45, "POSITIVE": 89, "NEUTRAL": 67, "NEGATIVE": 28, "VERY_NEGATIVE": 8 },

"market_sentiment_summary": { "overall_score": 0.32, "overall_label": "POSITIVE", "confidence": 0.85, "interpretation": "시장 전반적으로 긍정적 뉴스 우세" },

"top_keywords": [ { "keyword": "ai growth", "frequency": 67, "importance_score": 0.92 }, { "keyword": "earnings beat", "frequency": 54, "importance_score": 0.88 }, { "keyword": "fed rate", "frequency": 48, "importance_score": 0.85 }, { "keyword": "semiconductor", "frequency": 42, "importance_score": 0.80 }, { "keyword": "tech rally", "frequency": 38, "importance_score": 0.75 } ],

"trending_themes": [ { "theme_id": 0, "theme_name": "AI 붐", "keywords": ["ai", "chip", "nvidia", "demand", "growth"], "article_count": 78, "avg_sentiment": 0.68, "interpretation": "AI 관련 긍정적 뉴스 주도" }, { "theme_id": 1, "theme_name": "Fed 금리 논의", "keywords": ["fed", "rate", "inflation", "policy", "powell"], "article_count": 56, "avg_sentiment": 0.12, "interpretation": "금리 관련 중립적 논의" }, { "theme_id": 2, "theme_name": "실적 시즌", "keywords": ["earnings", "beat", "guidance", "revenue", "profit"], "article_count": 43, "avg_sentiment": 0.45, "interpretation": "실적 호조 뉴스 다수" } ],

"ticker_buzz_rankings": [ { "rank": 1, "ticker": "NVDA", "buzz_score": 92, "mention_count": 45, "avg_sentiment": 0.75, "trending": "UP", "summary": "AI 수요 급증 관련 압도적 언급" }, { "rank": 2, "ticker": "AAPL", "buzz_score": 78, "mention_count": 38, "avg_sentiment": 0.58, "trending": "UP", "summary": "iPhone 판매 호조 뉴스" }, { "rank": 3, "ticker": "TSLA", "buzz_score": 65, "mention_count": 32, "avg_sentiment": -0.25, "trending": "UP", "summary": "가격 인하 관련 우려 섞인 논의" } ],

"sector_sentiment": { "Technology": { "article_count": 128, "avg_sentiment": 0.52, "label": "POSITIVE", "top_tickers": ["NVDA", "AAPL", "MSFT"] }, "Finance": { "article_count": 45, "avg_sentiment": 0.18, "label": "NEUTRAL", "top_tickers": ["JPM", "BAC", "GS"] }, "Healthcare": { "article_count": 34, "avg_sentiment": 0.35, "label": "POSITIVE", "top_tickers": ["JNJ", "PFE", "MRNA"] } },

"alerts": [ { "type": "HIGH_BUZZ", "ticker": "NVDA", "message": "NVDA buzz score 92 (매우 높음)", "severity": "INFO" }, { "type": "SENTIMENT_SPIKE", "theme": "AI 붐", "message": "AI 관련 뉴스 sentiment +0.68 (매우 긍정)", "severity": "INFO" } ] }

Examples

Example 1: Tech Rally Day

Input: 237 articles (last 24h)

Output:

• Overall Sentiment: +0.45 (POSITIVE)
• Top Theme: "AI Growth" (78 articles)
• Top Buzz: NVDA (92), AAPL (78), MSFT (65)
• Keywords: "ai growth", "earnings beat", "chip demand"

Example 2: Market Correction Day

Input: 189 articles

Output:

• Overall Sentiment: -0.38 (NEGATIVE)
• Top Theme: "Fed Rate Hike Fears" (92 articles)
• Top Buzz: SPY (88), VIX (76), TLT (54)
• Keywords: "rate hike", "inflation", "recession fears"

Guidelines

Do's ✅

• 배치 처리: 효율성 극대화

• Ticker Buzz 추적: 시장 주목도 파악

• Theme Detection: 숨겨진 패턴 발견

• Sector Breakdown: 섹터별 sentiment

Don'ts ❌

• 단일 기사만 분석 금지 (Quick/Deep Reasoning 역할)

• Theme 너무 세분화 금지 (5개 이내)

• Buzz score 과신 금지 (quality over quantity)

• Historical context 무시 금지

Integration

Batch Processing Endpoint

@router.post("/api/news/batch-analyze") async def batch_analyze_news( timeframe_hours: int = 24, db: Session = Depends(get_db) ): """Batch analyze recent news"""

# Fetch articles
cutoff = datetime.now() - timedelta(hours=timeframe_hours)
articles = db.query(NewsArticle).filter(
    NewsArticle.created_at >= cutoff
).all()

# Run News Intelligence Agent
agent = NewsIntelligenceAgent()

result = await agent.execute({
    'articles': articles,
    'timeframe_hours': timeframe_hours
})

return result

Real-Time Updates (WebSocket)

from fastapi import WebSocket

@router.websocket("/ws/news-intel") async def news_intel_websocket(websocket: WebSocket): """Stream news intelligence updates"""

await websocket.accept()

while True:
    # Run analysis every 5 minutes
    result = await batch_analyze_news(timeframe_hours=1)
    
    await websocket.send_json(result)
    
    await asyncio.sleep(300)  # 5 minutes

Performance Metrics

• Batch Processing Speed: 목표 < 10초 for 100 articles

• Sentiment Accuracy: > 80%

• Theme Detection Quality: > 75% (사람 판단과 일치)

• Ticker Buzz Precision: > 85%

Comparison

Agent Scope Speed Use Case

News Intelligence 배치 (100+ articles) 10초 시장 전체 흐름

Quick Analyzer 단일 ticker 5초 개별 종목 확인

Deep Reasoning 단일 news 30초 중요한 뉴스 심층 분석

Version History

• v1.0 (2025-12-21): Initial release with batch processing and theme detection