Examples

Introduction pykrx is the easiest way to get Korean stock market data in Python. No API key needed, no account required — just install and start pulling data. This guide covers 10 real-world examples from basic price data to advanced screening. Setup pip install pykrx pandas from pykrx import stock import pandas as pd from datetime import datetime, timedelta # Helper: today's date TODAY = datetime.now().strftime("%Y%m%d") MONTH_AGO = (datetime.now() - timedelta(days=30)).strftime("%Y%m%d") YEAR_AGO = (datetime.now() - timedelta(days=365)).strftime("%Y%m%d") Example 1: Get Stock Price History from pykrx import stock # Samsung Electronics (005930) df = stock.get_market_ohlcv_by_date("20240101", "20241231", "005930") df.columns = ["Open", "High", "Low", "Close", "Volume"] print(df.tail()) Example 2: Get All KOSPI Tickers from pykrx import stock kospi = stock.get_market_ticker_list(market="KOSPI") kosdaq = stock.get_market_ticker_list(market="KOSDAQ") print(f"KOSPI stocks: {len(kospi)}") print(f"KOSDAQ stocks: {len(kosdaq)}") # Get company name name = stock.get_market_ticker_name("005930") print(f"005930 = {name}") # 삼성전자 Example 3: Market Cap Rankings from pykrx import stock # Top 10 KOSPI by market cap df = stock.get_market_cap_by_ticker(TODAY, market="KOSPI") top10 = df.sort_values("시가총액", ascending=False).head(10) for ticker, row in top10.iterrows(): name = stock.get_market_ticker_name(ticker) market_cap_trillion = row["시가총액"] / 1e12 print(f"{name}: {market_cap_trillion:.1f}T KRW") Example 4: Fundamental Data (PER, PBR, Dividend) from pykrx import stock # Get fundamentals for all KOSPI stocks df = stock.get_market_fundamental_by_ticker(TODAY, market="KOSPI") # Find undervalued stocks (low PER, low PBR) undervalued = df[ (df["PER"] > 0) & (df["PER"] < 10) & (df["PBR"] > 0) & (df["PBR"] < 1.0) ].copy() undervalued["name"] = [stock.get_market_ticker_name(t) for t in undervalued.index] print(undervalued[["name", "PER", "PBR", "DIV"]].sort_values("PER").head(10)) Example 5: KOSPI Index Data from pykrx import stock # Get KOSPI index history # "1001" = KOSPI, "2001" = KOSDAQ kospi_index = stock.get_index_ohlcv_by_date("20240101", "20241231", "1001") print(kospi_index.tail()) # Year high/low print(f"2024 KOSPI High: {kospi_index['고가'].max():,}") print(f"2024 KOSPI Low: {kospi_index['저가'].min():,}") Example 6: Foreign Investor Trading Data from pykrx import stock # Foreign net buying/selling for Samsung df = stock.get_market_trading_volume_by_date( "20240101", "20241231", "005930" ) print(df.tail()) # Days with heavy foreign buying foreign_buying = df[df["외국인"] > 1000000] print(f"Heavy foreign buying days: {len(foreign_buying)}") Example 7: Sector Performance from pykrx import stock # Get sector (theme) data # Major KOSPI sector tickers sectors = { "1001": "KOSPI", "1028": "KOSPI200", "2001": "KOSDAQ", "1163": "KOSPI IT", "1150": "KOSPI Finance" } for code, name in sectors.items(): df = stock.get_index_ohlcv_by_date(MONTH_AGO, TODAY, code) if not df.empty: start = df["종가"].iloc[0] end = df["종가"].iloc[-1] change = (end - start) / start * 100 print(f"{name}: {change:+.2f}%") Example 8: Volume Surge Detector from pykrx import stock import pandas as pd def find_volume_surges(market="KOSPI", multiplier=3.0): """Find stocks with volume 3x above their 20-day average""" tickers = stock.get_market_ticker_list(market=market) surges = [] for ticker in tickers[:50]: # Limit for demo try: df = stock.get_market_ohlcv_by_date(MONTH_AGO, TODAY, ticker) if len(df) < 20: continue avg_vol = df["거래량"].iloc[:-1].mean() today_vol = df["거래량"].iloc[-1] if today_vol > avg_vol * multiplier: name = stock.get_market_ticker_name(ticker) surges.append({ "ticker": ticker, "name": name, "volume_ratio": today_vol / avg_vol, "close": df["종가"].iloc[-1] }) except: continue return sorted(surges, key=lambda x: x["volume_ratio"], reverse=True) surges = find_volume_surges() for s in surges[:5]: print(f"{s['name']}: {s['volume_ratio']:.1f}x average volume") Example 9: Simple Backtesting from pykrx import stock import pandas as pd def backtest_moving_average(ticker, short=5, long=20): """Simple moving average crossover backtest""" df = stock.get_market_ohlcv_by_date("20230101", "20241231", ticker) df.columns = ["Open", "High", "Low", "Close", "Volume"] df[f"MA{short}"] = df["Close"].rolling(short).mean() df[f"MA{long}"] = df["Close"].rolling(long).mean() # Signal: 1 = buy, -1 = sell df["signal"] = 0 df.loc[df[f"MA{short}"] > df[f"MA{long}"], "signal"] = 1 df.loc[df[f"MA{short}"] < df[f"MA{long}"], "signal"] = -1 # Returns df["returns"] = df["Close"].pct_change() df["strategy"] = df["signal"].shift(1) * df["returns"] total_return = (1 + df["strategy"].dropna()).prod() - 1 buy_hold = (df["Close"].iloc[-1] - df["Close"].iloc[0]) / df["Close"].iloc[0] print(f"Strategy return: {total_return:.2%}") print(f"Buy & hold return: {buy_hold:.2%}") return df result = backtest_moving_average("005930") Example 10: Export to Excel from pykrx import stock import pandas as pd # Get data for multiple stocks watchlist = { "005930": "Samsung Electronics", "000660": "SK Hynix", "373220": "LG Energy Solution", "005380": "Hyundai Motor", "035420": "NAVER" } with pd.ExcelWriter("korean_stocks.xlsx") as writer: for ticker, name in watchlist.items(): df = stock.get_market_ohlcv_by_date("20240101", "20241231", ticker) df.columns = ["Open", "High", "Low", "Close", "Volume"] df.to_excel(writer, sheet_name=name[:30]) print(f"Saved {name}") print("Excel file created: korean_stocks.xlsx") Common Mistakes Mistake Fix Date with dashes "2024-01-01" Remove dashes: "20240101" Using company name as ticker Use 6-digit number: "005930" Calling too frequently Add time.sleep(0.5) between calls Empty result on weekends KRX is closed Sat/Sun Key Takeaways pykrx is free and requires no API key Date format is always YYYYMMDD Column names default to Korean — rename for easier use Add delays between multiple API calls to avoid rate limiting For real-time data and trading, use KIS API instead Related Guides pykrx get_market_ohlcv_by_date: Complete Guide Build a Korean Stock Screener with pykrx KIS API vs pykrx: Which Should You Use?