Python

What is the KIS API? The KIS API (Korea Investment & Securities API) is a REST/WebSocket API that allows developers to: Get real-time stock quotes Place buy/sell orders programmatically Access account balance and portfolio data Stream real-time market data via WebSocket Prerequisites A Korea Investment & Securities brokerage account KIS API access approval Python 3.8+ installed Step 1: Open a KIS Account Visit the Korea Investment & Securities website and apply for an account. Foreigners can apply online in many cases. ...

What is pykrx? pykrx is an open-source Python library that pulls data from the KRX website. It is the easiest way to get Korean stock market data without needing a paid API. Installation pip install pykrx Basic Usage Get Stock Price History from pykrx import stock # Get Samsung Electronics (005930) price history df = stock.get_market_ohlcv_by_date("20240101", "20241231", "005930") print(df.head()) Get All KOSPI Tickers from pykrx import stock # Get all KOSPI tickers tickers = stock.get_market_ticker_list(market="KOSPI") print(f"Total KOSPI stocks: {len(tickers)}") # Get company name from ticker name = stock.get_market_ticker_name("005930") print(f"005930 = {name}") # Samsung Electronics Get Market Cap Data # Get market cap for all KOSPI stocks df = stock.get_market_cap_by_ticker("20241231", market="KOSPI") print(df.sort_values("시가총액", ascending=False).head(10)) Get Fundamental Data # Get PER, PBR, dividend yield df = stock.get_market_fundamental_by_ticker("20241231", market="KOSPI") print(df[["PER", "PBR", "DIV"]].head()) Building a Simple Screener from pykrx import stock def screen_low_per_stocks(market="KOSPI", max_per=10): today = "20241231" fundamentals = stock.get_market_fundamental_by_ticker(today, market=market) low_per = fundamentals[ (fundamentals["PER"] > 0) & (fundamentals["PER"] < max_per) ] low_per["name"] = [stock.get_market_ticker_name(t) for t in low_per.index] return low_per[["name", "PER", "PBR", "DIV"]].sort_values("PER") results = screen_low_per_stocks() print(results.head(20)) pykrx vs KIS API Comparison Feature pykrx KIS API Real-time data No Yes Historical data Yes Yes Order placement No Yes Cost Free Free (with account) Best for Research, backtesting Live trading

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: ...

Overview If you’re building a Korean stock trading system in Python, you’ll encounter two main tools: KIS API and pykrx. Here’s a detailed breakdown of when to use each. What is pykrx? pykrx is an open-source library that scrapes publicly available data from the KRX website. It requires no account or API key. Best for: Historical data research Backtesting trading strategies Stock screening and analysis Learning and prototyping What is KIS API? KIS API is the official API from Korea Investment & Securities. It requires a brokerage account and API approval. ...

Background I’ve been actively trading Korean stocks on the KOSPI and KOSDAQ for years. After spending too many hours watching charts, I decided to automate part of my trading strategy using the KIS API. This post shares what I built, what worked, and what I learned — including mistakes. The Strategy My bot focuses on momentum scalping on KOSPI stocks: Screen for stocks with high volume breakouts in the first 30 minutes Enter positions with defined risk (1-2% of account per trade) Exit on target profit or stop loss Maximum 3 concurrent positions System Architecture GitHub Actions (scheduler) ↓ Python Script ↓ ↓ ↓ KIS API pykrx Telegram Bot (trading) (data) (alerts) Core Components 1. Market Scanner from pykrx import stock import pandas as pd def scan_breakouts(min_volume_ratio=2.0): """Find stocks with unusual volume""" today = stock.get_market_ticker_list(market="KOSPI") breakouts = [] for ticker in today[:100]: # Limit for demo try: df = stock.get_market_ohlcv_by_date( prev_date, today_date, ticker ) if len(df) < 20: continue avg_volume = df["거래량"].mean() current_volume = df["거래량"].iloc[-1] if current_volume > avg_volume * min_volume_ratio: breakouts.append({ "ticker": ticker, "volume_ratio": current_volume / avg_volume }) except: continue return sorted(breakouts, key=lambda x: x["volume_ratio"], reverse=True) 2. Order Execution (KIS API) def place_market_order(ticker, quantity, order_type="buy", token=None): url = "https://openapi.koreainvestment.com:9443/uapi/domestic-stock/v1/trading/order-cash" tr_id = "TTTC0802U" if order_type == "buy" else "TTTC0801U" headers = { "authorization": f"Bearer {token}", "appkey": APP_KEY, "appsecret": APP_SECRET, "tr_id": tr_id } body = { "CANO": ACCOUNT_NO, "ACNT_PRDT_CD": "01", "PDNO": ticker, "ORD_DVSN": "01", # Market order "ORD_QTY": str(quantity), "ORD_UNPR": "0" } res = requests.post(url, headers=headers, json=body) return res.json() Key Lessons Learned What Worked Automating the scanning phase saved significant time Telegram alerts for signals I review manually before execution Strict position sizing rules enforced by code What Did Not Work Fully automated execution without human review (too many false signals) Running during the first 5 minutes of market open (too volatile) Ignoring market regime (bot designed for trending markets struggled in choppy conditions) Risk Management Rules Never risk more than 2% of account per trade Maximum 3 concurrent positions Hard daily loss limit of 5% — bot stops automatically No trading during major news events Current Status The bot currently runs in semi-automated mode: it scans and sends signals via Telegram, but I manually approve each trade before execution. This hybrid approach has been more profitable than either full automation or pure manual trading.