每日分享：利用API高效获取纳斯达克交易所数据并进行分析

import nasdaqdatalink

# 设置API密钥（注册后获取）
nasdaqdatalink.ApiConfig.api_key = 'your_api_key_here'

# 获取苹果公司历史股价数据
data = nasdaqdatalink.get("WIKI/AAPL", start_date="2024-01-01", end_date="2024-12-31")
print(data.head())

import requests
import pandas as pd

# API基础配置
BASE_URL = "https://api.stocktv.top"
COUNTRY_ID = 5  # 美股专用ID
API_KEY = "YOUR_API_KEY"  # 替换为实际密钥

def get_us_stocks(page=1, page_size=50):
    """获取美股实时行情列表"""
    url = f"{BASE_URL}/stock/stocks"
    params = {
        "countryId": COUNTRY_ID,
        "page": page,
        "pageSize": page_size,
        "key": API_KEY
    }
    
    response = requests.get(url, params=params)
    if response.status_code == 200:
        return response.json()
    else:
        print(f"请求失败: {response.status_code}")
        return None

# 获取第一页美股数据
stocks_data = get_us_stocks()
if stocks_data:
    print(f"获取到 {len(stocks_data.get('data', []))} 条股票数据")

def get_realtime_quotes(symbols):
    """获取指定股票的实时报价"""
    url = f"{BASE_URL}/stock/realtime"
    params = {
        "symbols": ",".join(symbols),
        "countryId": COUNTRY_ID,
        "key": API_KEY
    }
    
    response = requests.get(url, params=params)
    if response.status_code == 200:
        return response.json()
    return None

# 获取苹果、微软、特斯拉的实时行情
tech_stocks = get_realtime_quotes(["AAPL", "MSFT", "TSLA"])
if tech_stocks:
    for stock in tech_stocks.get('data', []):
        print(f"{stock['symbol']}: 最新价 ${stock['last']}, 涨跌幅 {stock['change_percent']}%")

def get_historical_data(symbol, start_date, end_date, interval="1d"):
    """获取股票历史数据"""
    url = f"{BASE_URL}/stock/historical"
    params = {
        "symbol": symbol,
        "countryId": COUNTRY_ID,
        "startDate": start_date,
        "endDate": end_date,
        "interval": interval,
        "key": API_KEY
    }
    
    response = requests.get(url, params=params)
    if response.status_code == 200:
        data = response.json()
        # 转换为DataFrame便于分析
        df = pd.DataFrame(data.get('data', []))
        if not df.empty:
            df['date'] = pd.to_datetime(df['date'])
            df.set_index('date', inplace=True)
        return df
    return pd.DataFrame()

# 获取苹果公司2024年日线数据
aapl_data = get_historical_data("AAPL", "2024-01-01", "2024-12-31", "1d")

if not aapl_data.empty:
    # 计算简单移动平均线
    aapl_data['MA20'] = aapl_data['close'].rolling(window=20).mean()
    aapl_data['MA50'] = aapl_data['close'].rolling(window=50).mean()
    
    # 显示最新数据
    print(aapl_data[['close', 'MA20', 'MA50']].tail())
    
    # 基础统计分析
    print(f"\n苹果股票2024年统计:")
    print(f"最高价: ${aapl_data['high'].max():.2f}")
    print(f"最低价: ${aapl_data['low'].min():.2f}")
    print(f"平均收盘价: ${aapl_data['close'].mean():.2f}")
    print(f"年化波动率: {(aapl_data['close'].pct_change().std() * (252**0.5) * 100):.2f}%")

def analyze_correlation(symbols, start_date, end_date):
    """分析多只股票的相关性"""
    close_prices = pd.DataFrame()
    
    for symbol in symbols:
        data = get_historical_data(symbol, start_date, end_date)
        if not data.empty:
            close_prices[symbol] = data['close']
    
    if not close_prices.empty:
        correlation_matrix = close_prices.corr()
        return correlation_matrix
    return None

# 分析科技股相关性
tech_symbols = ["AAPL", "MSFT", "GOOGL", "AMZN", "META"]
corr_matrix = analyze_correlation(tech_symbols, "2024-01-01", "2024-12-31")
if corr_matrix is not None:
    print("科技股相关性矩阵:")
    print(corr_matrix)