调用接口获取韩国股票数据
前言
在全球化投资背景下，韩国作为亚洲重要经济体，其股市数据对投资者和开发者具有重要意义。本文将详细介绍如何通过API接口获取韩国股票市场的实时行情、历史数据及相关信息。
接口基础配置
基础信息
- 基础URL:
https://api.stocktv.top - 协议: HTTPS
- 认证方式: API Key认证
环境准备
import requests
import pandas as pd
import json
from datetime import datetime
class KoreaStockAPI:
def __init__(self, api_key):
self.base_url = "https://api.stocktv.top"
self.api_key = api_key
self.session = requests.Session()
def _make_request(self, endpoint, params):
"""统一的请求方法"""
url = f"{self.base_url}{endpoint}"
params['key'] = self.api_key
try:
response = self.session.get(url, params=params, timeout=10)
response.raise_for_status()
data = response.json()
if data.get('code') == 200:
return data['data']
else:
print(f"API Error: {data.get('message', 'Unknown error')}")
return None
except requests.exceptions.RequestException as e:
print(f"Request failed: {e}")
return None
核心接口详解
1. 获取韩国股票列表
def get_korea_stock_list(self, page_size=100, page=1):
"""
获取韩国股票市场列表
countryId: 韩国对应的国家ID需要确认（假设为16）
"""
endpoint = "/stock/stocks"
params = {
'countryId': 16, # 韩国国家ID，需确认具体数值
'pageSize': page_size,
'page': page
}
return self._make_request(endpoint, params)
# 使用示例
api = KoreaStockAPI("your_api_key")
stocks = api.get_korea_stock_list(page_size=50)
if stocks:
for stock in stocks['records']:
print(f"{stock['symbol']} - {stock['name']}: {stock['last']}")
返回字段说明:
symbol: 股票代码（如：005930）name: 公司名称（韩文或英文）last: 最新价格chgPct: 涨跌幅volume: 成交量high/low: 当日最高/低价open: 是否在交易中
2. 查询特定韩国股票
def search_korea_stock(self, symbol=None, name=None, pid=None):
"""通过代码、名称或PID查询特定股票"""
endpoint = "/stock/queryStocks"
params = {}
if symbol:
params['symbol'] = symbol
elif name:
params['name'] = name
elif pid:
params['id'] = pid
return self._make_request(endpoint, params)
# 查询三星电子
samsung = api.search_korea_stock(symbol="005930")
3. 获取韩国主要指数
def get_korea_indices(self):
"""获取韩国主要指数（KOSPI, KOSDAQ等）"""
endpoint = "/stock/indices"
params = {'countryId': 16}
return self._make_request(endpoint, params)
# 使用示例
indices = api.get_korea_indices()
if indices:
for index in indices:
print(f"{index['name']}: {index['last']} ({index['chgPct']}%)")
4. 获取历史K线数据
def get_stock_kline(self, pid, interval="P1D", limit=100):
"""
获取股票历史K线数据
interval: PT5M(5分钟), PT1H(1小时), P1D(日线), P1W(周线)
"""
endpoint = "/stock/kline"
params = {
'pid': pid,
'interval': interval
}
data = self._make_request(endpoint, params)
if data:
return self._process_kline_data(data)
return None
def _process_kline_data(self, kline_data):
"""处理K线数据为DataFrame格式"""
df = pd.DataFrame(kline_data)
df['time'] = pd.to_datetime(df['time'], unit='ms')
df.set_index('time', inplace=True)
return df
高级功能实现
1. 实时数据监控类
class KoreaStockMonitor:
def __init__(self, api_key, watch_list):
self.api = KoreaStockAPI(api_key)
self.watch_list = watch_list
self.price_alerts = {}
def add_price_alert(self, symbol, target_price, direction='above'):
"""添加价格预警"""
self.price_alerts[symbol] = {
'target_price': target_price,
'direction': direction,
'triggered': False
}
def check_alerts(self, current_price, symbol):
"""检查价格预警条件"""
if symbol in self.price_alerts:
alert = self.price_alerts[symbol]
if not alert['triggered']:
if (alert['direction'] == 'above' and current_price >= alert['target_price']) or \
(alert['direction'] == 'below' and current_price <= alert['target_price']):
alert['triggered'] = True
return True
return False
def start_monitoring(self, interval=60):
"""开始监控"""
import time
while True:
for symbol in self.watch_list:
stock_data = self.api.search_korea_stock(symbol=symbol)
if stock_data and len(stock_data) > 0:
current_price = stock_data[0]['last']
if self.check_alerts(current_price, symbol):
self._send_alert(symbol, current_price)
time.sleep(interval)
def _send_alert(self, symbol, price):
"""发送预警通知"""
print(f"Alert: {symbol} reached {price}")
# 这里可以集成邮件、短信等通知方式
2. 数据分析工具
class KoreaStockAnalyzer:
def __init__(self, api_key):
self.api = KoreaStockAPI(api_key)
def calculate_technical_indicators(self, pid, period=20):
"""计算技术指标"""
kline_data = self.api.get_stock_kline(pid, "P1D", 100)
if kline_data is None:
return None
# 计算移动平均线
kline_data['MA20'] = kline_data['close'].rolling(window=period).mean()
# 计算RSI
delta = kline_data['close'].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
rs = gain / loss
kline_data['RSI'] = 100 - (100 / (1 + rs))
return kline_data
def get_market_overview(self):
"""获取市场概览"""
stocks = self.api.get_korea_stock_list(page_size=200)
if not stocks:
return None
advancing = 0
declining = 0
total_volume = 0
for stock in stocks['records']:
if stock.get('chgPct', 0) > 0:
advancing += 1
elif stock.get('chgPct', 0) < 0:
declining += 1
total_volume += stock.get('volume', 0)
return {
'total_stocks': len(stocks['records']),
'advancing': advancing,
'declining': declining,
'total_volume': total_volume
}
实际应用示例
1. 构建简单的股票看板
def create_stock_dashboard(api_key, symbols):
"""创建股票数据看板"""
api = KoreaStockAPI(api_key)
dashboard_data = []
for symbol in symbols:
stock_data = api.search_korea_stock(symbol=symbol)
if stock_data and len(stock_data) > 0:
stock = stock_data[0]
dashboard_data.append({
'Symbol': symbol,
'Name': stock.get('name', 'N/A'),
'Price': stock.get('last', 0),
'Change %': stock.get('chgPct', 0),
'Volume': stock.get('volume', 0)
})
df = pd.DataFrame(dashboard_data)
return df
# 使用示例
symbols = ["005930", "000660", "035420"] # 三星, SK海力士, Naver
dashboard = create_stock_dashboard("your_api_key", symbols)
print(dashboard)
2. 数据可视化
import matplotlib.pyplot as plt
import seaborn as sns
def plot_stock_trend(api_key, symbol, days=30):
"""绘制股票趋势图"""
api = KoreaStockAPI(api_key)
stock_data = api.search_korea_stock(symbol=symbol)
if stock_data and len(stock_data) > 0:
pid = stock_data[0]['id']
kline_data = api.get_stock_kline(pid, "P1D", days)
if kline_data is not None:
plt.figure(figsize=(12, 6))
plt.plot(kline_data.index, kline_data['close'], label='Close Price')
plt.title(f'{symbol} Price Trend ({days} Days)')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid(True)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
# 绘制三星电子近期走势
plot_stock_trend("your_api_key", "005930", 60)
注意事项和最佳实践
1. 错误处理增强
def robust_api_call(api_func, max_retries=3, delay=1):
"""带重试机制的API调用"""
import time
for attempt in range(max_retries):
try:
result = api_func()
if result is not None:
return result
except Exception as e:
print(f"Attempt {attempt + 1} failed: {e}")
if attempt < max_retries - 1:
time.sleep(delay * (2 ** attempt)) # 指数退避
print("All retry attempts failed")
return None
2. 数据缓存策略
from functools import lru_cache
import time
class CachedKoreaStockAPI(KoreaStockAPI):
@lru_cache(maxsize=100)
def get_korea_stock_list(self, page_size=100, page=1):
# 基础方法已有缓存，这里可以添加时间戳验证
return super().get_korea_stock_list(page_size, page)
3. 性能优化建议
- 批量请求: 避免频繁的单次请求，合理设置请求间隔
- 数据缓存: 对不常变的数据进行本地缓存
- 异步处理: 对于大量数据请求使用异步方式
- 连接复用: 使用Session保持HTTP连接
总结
通过本文介绍的API接口，开发者可以方便地获取韩国股票市场的各类数据。在实际应用中，建议：
- 合理控制请求频率，遵守API使用限制
- 实现完善的数据验证和错误处理机制
- 对敏感数据进行安全存储和处理
- 根据实际需求选择合适的数据更新策略
这些接口为构建韩国股票分析工具、投资决策系统和市场监控平台提供了坚实的数据基础。 重要提示: 实际使用时请确认韩国的正确countryId，并确保遵守相关数据使用协议和法律法规。
