⚙️ 개발 환경 세팅 Ollama + LangChain + 금융 데이터

import yfinance as yf
import pandas as pd

# 단일 종목
ticker = yf.Ticker("AAPL")

# 기본 정보
info = ticker.info
print(f"회사명: {info.get('longName')}")
print(f"현재가: ${info.get('currentPrice')}")
print(f"시총: ${info.get('marketCap'):,}")
print(f"PER: {info.get('trailingPE'):.2f}")

# 6개월 일봉 데이터
hist = ticker.history(period="6mo")
print(hist[['Open', 'High', 'Low', 'Close', 'Volume']].tail())

# 여러 종목 동시 조회
tickers = yf.download(["AAPL", "MSFT", "NVDA", "GOOGL"], period="1mo")
print(tickers['Close'].tail())

# 지수
sp500 = yf.Ticker("^GSPC")   # S&P 500
nasdaq = yf.Ticker("^IXIC")  # NASDAQ
print(f"S&P500: ${sp500.info.get('regularMarketPrice')}")

from pykrx import stock
from pykrx import bond
import pandas as pd
from datetime import datetime, timedelta

today = datetime.today().strftime("%Y%m%d")
yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y%m%d")

# 종목 코드로 종목명 조회
name = stock.get_market_ticker_name("005930")
print(f"005930 = {name}")  # 삼성전자

# 삼성전자 최근 30일 OHLCV
df = stock.get_market_ohlcv_by_date("20250101", today, "005930")
print(df.tail())

# KOSPI 전체 종목 시세
kospi = stock.get_market_ohlcv_by_ticker(today, market="KOSPI")
print(f"KOSPI 종목 수: {len(kospi)}")
print(kospi.sort_values("거래량", ascending=False).head(5))

# 시장 지수
index_df = stock.get_index_ohlcv_by_date("20250101", today, "1001")  # KOSPI
print(index_df.tail())

# 외국인 순매수 상위 종목
foreigner = stock.get_market_net_purchases_of_equities_by_ticker(
    today, today, "KOSPI", "외국인"
)
print(foreigner.head(10))

import requests
import os
from dotenv import load_dotenv

load_dotenv()
DART_KEY = os.getenv("DART_API_KEY")

def get_company_info(corp_name: str) -> dict:
    """기업명으로 DART 기업 코드 조회"""
    url = "https://opendart.fss.or.kr/api/company.json"
    params = {"crtfc_key": DART_KEY, "corp_name": corp_name}
    res = requests.get(url, params=params)
    return res.json()

def get_disclosures(corp_code: str, days: int = 30) -> list:
    """최근 공시 목록 조회"""
    from datetime import datetime, timedelta
    bgn = (datetime.today() - timedelta(days=days)).strftime("%Y%m%d")
    url = "https://opendart.fss.or.kr/api/list.json"
    params = {
        "crtfc_key": DART_KEY,
        "corp_code": corp_code,
        "bgn_de": bgn,
        "sort": "date",
        "sort_mth": "desc",
    }
    return requests.get(url, params=params).json().get("list", [])

# 테스트
company = get_company_info("삼성전자")
print(company)

from langchain_ollama import ChatOllama
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import HumanMessage, SystemMessage
import os
from dotenv import load_dotenv

load_dotenv()

# 로컬 Ollama 모델 (무료, CPU 추론)
local_llm = ChatOllama(
    model="llama3.1:8b",
    base_url="http://localhost:11434",
    temperature=0,          # 금융 분석: 재현성 우선
    num_predict=2048,       # 최대 출력 토큰
)

# Claude API (복잡한 분석용)
claude_llm = ChatAnthropic(
    model="claude-sonnet-4-6",
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    temperature=0,
    max_tokens=4096,
)

def ask_local(question: str, system: str = "") -> str:
    messages = []
    if system:
        messages.append(SystemMessage(content=system))
    messages.append(HumanMessage(content=question))
    return local_llm.invoke(messages).content

def ask_claude(question: str, system: str = "") -> str:
    messages = []
    if system:
        messages.append(SystemMessage(content=system))
    messages.append(HumanMessage(content=question))
    return claude_llm.invoke(messages).content

# 테스트
if __name__ == "__main__":
    print("=== Ollama (로컬) ===")
    print(ask_local("NVIDIA 주가에 영향을 주는 핵심 요소 3가지는?"))

    print("\n=== Claude (API) ===")
    print(ask_claude("삼성전자 반도체 사업 리스크를 분석해주세요."))

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from llm_setup import ask_local

app = FastAPI(title="금융 AI Agent API", version="1.0.0")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

class QueryRequest(BaseModel):
    question: str
    model: str = "local"   # "local" | "claude"

class QueryResponse(BaseModel):
    answer: str
    model_used: str

@app.get("/health")
async def health():
    return {"status": "ok"}

@app.post("/ask", response_model=QueryResponse)
async def ask(req: QueryRequest):
    if req.model == "claude":
        from llm_setup import ask_claude
        answer = ask_claude(req.question)
    else:
        answer = ask_local(req.question)
    return QueryResponse(answer=answer, model_used=req.model)

"""전체 환경 동작 검증 스크립트"""
import sys

def check(label, fn):
    try:
        fn()
        print(f"  ✓ {label}")
    except Exception as e:
        print(f"  ✗ {label}: {e}")
        sys.exit(1)

print("=== 금융 AI Agent 환경 검증 ===\n")

# 1. Ollama
def test_ollama():
    import requests
    r = requests.get("http://localhost:11434/api/tags")
    assert r.status_code == 200
    models = [m["name"] for m in r.json().get("models", [])]
    assert any("llama3.1" in m for m in models), f"llama3.1 모델 없음: {models}"
check("Ollama + llama3.1:8b", test_ollama)

# 2. yfinance
def test_yfinance():
    import yfinance as yf
    t = yf.Ticker("AAPL")
    assert t.info.get("currentPrice") is not None
check("yfinance (미국 주식)", test_yfinance)

# 3. pykrx
def test_pykrx():
    from pykrx import stock
    from datetime import datetime, timedelta
    d = (datetime.today() - timedelta(days=3)).strftime("%Y%m%d")
    df = stock.get_market_ohlcv_by_date(d, d, "005930")
    assert not df.empty
check("pykrx (한국 주식)", test_pykrx)

# 4. DART API
def test_dart():
    import requests, os
    from dotenv import load_dotenv
    load_dotenv()
    key = os.getenv("DART_API_KEY", "")
    assert key, "DART_API_KEY 미설정"
    r = requests.get(f"https://opendart.fss.or.kr/api/company.json",
                     params={"crtfc_key": key, "corp_name": "삼성전자"})
    assert r.status_code == 200
check("DART API", test_dart)

# 5. LangChain + Ollama
def test_langchain():
    from llm_setup import ask_local
    ans = ask_local("1+1=?")
    assert len(ans) > 0
check("LangChain + Ollama", test_langchain)

# 6. ChromaDB
def test_chroma():
    import chromadb
    client = chromadb.Client()
    col = client.create_collection("test")
    col.add(documents=["test doc"], ids=["1"])
    assert col.count() == 1
check("ChromaDB", test_chroma)

print("\n✅ 모든 환경 검증 완료. 02 시장 데이터 에이전트로 이동하세요.")