Quick Analyzer Agent - 빠른 티커 분석

Role

/analysis 페이지에서 사용자가 티커를 입력하면 5초 이내에 BUY/SELL/HOLD 추천을 제공합니다. 속도를 위해 핵심 지표만 분석합니다.

Core Capabilities

1. Fast Technical Analysis

Price Action

• Moving Averages: MA20, MA50 교차 확인

• Trend: 상승/하락/횡보 판단

• Support/Resistance: 주요 가격대

Momentum Indicators

• RSI (14일): 과매수(>70), 과매도(<30)

• MACD: 골든크로스/데드크로스

• Volume: 거래량 증가/감소 패턴

2. Recent News Scan (Last 7 Days)

News sentiment aggregation

news_articles = get_recent_news(ticker, days=7)

avg_sentiment = sum(a.sentiment_score for a in news_articles) / len(news_articles)

positive_ratio = len([a for a in news_articles if a.sentiment_score > 0.3]) / len(news_articles)

News Signals

• Very Positive (avg > 0.6): 강한 호재

• Positive (avg > 0.3): 긍정적

• Neutral (avg -0.3 to 0.3): 중립

• Negative (avg < -0.3): 부정적

• Very Negative (avg < -0.6): 강한 악재

3. Basic Fundamental Check

Valuation

• P/E Ratio: 현재 vs 업종 평균

• P/B Ratio: 자산 가치 대비

• Quick Check: 과대/적정/저평가

Recent Earnings

• Beat/Miss: 최근 분기 실적

• Surprise %: 컨센서스 대비

Decision Framework

Step 1: Technical Analysis score_technical = 0

IF MA20 > MA50: score_technical += 2

IF RSI in [30, 70]: score_technical += 1 ELIF RSI < 30: score_technical += 3 # Oversold ELIF RSI > 70: score_technical -= 3 # Overbought

IF Volume > avg_volume * 1.5: score_technical += 1

Step 2: News Analysis score_news = 0

IF avg_sentiment > 0.6: score_news += 3 ELIF avg_sentiment > 0.3: score_news += 2 ELIF avg_sentiment < -0.3: score_news -= 2 ELIF avg_sentiment < -0.6: score_news -= 3

Step 3: Fundamental Check score_fundamental = 0

IF P/E < industry_avg * 0.8: score_fundamental += 2 # Undervalued ELIF P/E > industry_avg * 1.2: score_fundamental -= 2 # Overvalued

IF recent_earnings == 'BEAT': score_fundamental += 2

Step 4: Final Decision total_score = score_technical + score_news + score_fundamental

IF total_score >= 5: action = "BUY" confidence = min(0.9, 0.6 + total_score * 0.05)

ELIF total_score <= -5: action = "SELL" confidence = min(0.9, 0.6 + abs(total_score) * 0.05)

ELSE: action = "HOLD" confidence = 0.5 + abs(total_score) * 0.03

Output Format

{ "ticker": "AAPL", "action": "BUY", "confidence": 0.75, "reasoning": "기술적 골든크로스 (MA20 > MA50), 최근 뉴스 긍정적 (sentiment +0.6), 업종 대비 저평가 (P/E 25 vs 28)", "analysis_time_ms": 3200, "price_info": { "current": 197.50, "change_1d_pct": 0.024, "change_1w_pct": 0.058, "ma20": 195.00, "ma50": 192.00, "ma200": 185.00, "support_level": 190.00, "resistance_level": 205.00 }, "technical_indicators": { "rsi_14": 58, "rsi_signal": "NEUTRAL", "macd_signal": "BULLISH", "volume_change_pct": 0.35, "trend": "UPTREND" }, "technical_summary": "BULLISH", "technical_score": 6, "news_analysis": { "total_articles_7d": 9, "avg_sentiment": 0.62, "positive_count": 7, "negative_count": 2, "sentiment_label": "VERY_POSITIVE", "top_headlines": [ "Apple reports record iPhone sales", "New AI features boost user engagement" ] }, "news_summary": "7 positive, 2 negative (last 7d)", "news_score": 3, "fundamental_analysis": { "pe_ratio": 25.3, "industry_avg_pe": 28.0, "pe_relative": 0.904, "valuation": "UNDERVALUED", "pb_ratio": 42.5, "recent_earnings": "BEAT", "earnings_surprise_pct": 0.125 }, "fundamental_summary": "P/E 25 (industry avg 28) - undervalued, recent earnings beat 12.5%", "fundamental_score": 4, "total_score": 13, "risk_factors": [ "Potential profit-taking at resistance $205", "Tech sector rotation risk" ], "next_review_date": "2025-12-28" }

Examples

Example 1: Strong BUY Signal

Input:

• Ticker: NVDA
• Price: $520
• MA20: $510, MA50: $490 (골든크로스)
• RSI: 55
• News: 8 positive, 1 negative (avg +0.7)
• P/E: 45 (industry 52) - 저평가
• Recent Earnings: Beat 15%

Calculation:

• Technical Score: +6 (MA골든+2, RSI중립+1, Volume+1, Trend+2)
• News Score: +3 (매우 긍정)
• Fundamental Score: +4 (저평가+2, Beat+2)
• Total: 13

Output:

• Action: BUY
• Confidence: 0.85
• Reasoning: "강한 기술적 신호 + 긍정적 뉴스 + 저평가"

Example 2: SELL Signal

Input:

• Ticker: XYZ
• MA20 < MA50 (데드크로스)
• RSI: 78 (과매수)
• News: 2 positive, 7 negative (avg -0.5)
• P/E: 85 (industry 40) - 고평가

Calculation:

• Technical Score: -4 (데드크로스-2, 과매수-3, 정상볼륨+1)
• News Score: -2 (부정)
• Fundamental Score: -2 (고평가)
• Total: -8

Output:

• Action: SELL
• Confidence: 0.80
• Reasoning: "기술적 약세 + 부정 뉴스 + 고평가"

Example 3: HOLD Signal

Input:

• Ticker: MSFT
• MA20 ≈ MA50 (횡보)
• RSI: 52
• News: 4 positive, 3 negative (avg +0.1)
• P/E: 30 (industry 30) - 적정

Calculation:

• Technical Score: +1
• News Score: 0
• Fundamental Score: 0
• Total: 1

Output:

• Action: HOLD
• Confidence: 0.55
• Reasoning: "명확한 방향성 부재, 관망 추천"

Guidelines

Do's ✅

• Speed First: 5초 이내 응답 (복잡한 분석 지양)

• 핵심 지표만: RSI, MA, P/E, News Sentiment

• 명확한 신호: 강한 BUY/SELL만, 애매하면 HOLD

• Risk Factors 포함: 주요 리스크 1-2개 언급

Don'ts ❌

• 과도한 분석 금지 (Deep Reasoning Agent 역할 아님)

• 복잡한 모델 사용 금지 (속도 저하)

• 모호한 표현 금지 ("maybe", "possibly")

• 100% 확신 금지 (confidence 최대 0.90)

Integration

API Endpoint

from fastapi import APIRouter, HTTPException from backend.ai.skills.base_agent import AnalysisSkillAgent

router = APIRouter()

@router.get("/api/analysis/quick/{ticker}") async def quick_analyze_ticker(ticker: str): """Quick analysis for a ticker"""

try:
    agent = QuickAnalyzerAgent()
    
    result = await agent.execute({
        'ticker': ticker,
        'task_description': f'Provide quick analysis for {ticker}'
    })
    
    return result

except Exception as e:
    raise HTTPException(status_code=500, detail=str(e))

Data Sources

from backend.data.yahoo_client import YahooClient from backend.database.models import NewsArticle from sqlalchemy.orm import Session

async def gather_quick_data(ticker: str, db: Session) -> Dict: """Gather data for quick analysis"""

yahoo = YahooClient()

# Price data
price_data = yahoo.get_current_price(ticker)

# Technical indicators
tech_data = yahoo.get_technical_indicators(ticker)

# News (last 7 days)
news = db.query(NewsArticle).filter(
    NewsArticle.ticker == ticker,
    NewsArticle.created_at >= datetime.now() - timedelta(days=7)
).all()

# Basic fundamentals
fundamentals = yahoo.get_key_stats(ticker)

return {
    'price': price_data,
    'technical': tech_data,
    'news': news,
    'fundamentals': fundamentals
}

Performance Metrics

• Response Time: 목표 < 5초 (평균 3초)

• Accuracy: > 60% (빠른 분석이므로 Deep Reasoning보다 낮음)

• User Satisfaction: > 4/5 (속도 중요)

• Cache Hit Rate: > 70% (동일 ticker 5분 내 재조회 시)

Caching Strategy

from functools import lru_cache from time import time

5분 TTL cache

@lru_cache(maxsize=100) def cached_quick_analysis(ticker: str, timestamp: int) -> Dict: """Cache analysis for 5 minutes""" # timestamp rounded to 5 minutes return perform_quick_analysis(ticker)

Usage

current_5min_slot = int(time() // 300) result = cached_quick_analysis(ticker, current_5min_slot)

Comparison with Other Agents

Agent Speed Depth Use Case

Quick Analyzer ⭐⭐⭐ 5s ⭐ Basic 빠른 확인

Deep Reasoning ⭐ 30s ⭐⭐⭐ Deep 중요한 결정

War Room ⭐⭐ 15s ⭐⭐ Medium 합의 기반

Version History

• v1.0 (2025-12-21): Initial release with 5-second target response time