Build a Korean Stock Screener with pykrx (Python Tutorial)
What We’re Building A Python-based Korean stock screener that: Pulls data from KRX via pykrx Filters stocks by multiple criteria (PER, PBR, volume) Exports results to CSV Can be automated with GitHub Actions Setup pip install pykrx pandas Complete Screener Code from pykrx import stock import pandas as pd from datetime import datetime, timedelta class KoreanStockScreener: def __init__(self, market="KOSPI"): self.market = market self.today = datetime.now().strftime("%Y%m%d") def get_all_data(self): fundamentals = stock.get_market_fundamental_by_ticker( self.today, market=self.market ) market_cap = stock.get_market_cap_by_ticker( self.today, market=self.market ) df = pd.concat([fundamentals, market_cap[["시가총액", "거래량"]]], axis=1) df["name"] = [stock.get_market_ticker_name(t) for t in df.index] return df def screen(self, max_per=15, max_pbr=1.5, min_div=2.0, min_volume=100000): df = self.get_all_data() filtered = df[ (df["PER"] > 0) & (df["PER"] < max_per) & (df["PBR"] > 0) & (df["PBR"] < max_pbr) & (df["DIV"] > min_div) & (df["거래량"] > min_volume) ] return filtered[["name", "PER", "PBR", "DIV", "시가총액", "거래량"]] def export(self, df, filename="screener_results.csv"): df.to_csv(filename, encoding="utf-8-sig") print(f"Saved {len(df)} stocks to {filename}") # Run screener screener = KoreanStockScreener(market="KOSPI") results = screener.screen(max_per=12, max_pbr=1.2, min_div=3.0) print(results.head(20)) screener.export(results) Automating with GitHub Actions Create .github/workflows/screener.yml: ...