指数整合

import akshare as ak
import pandas as pd

# 获取000300的历史行情数据
df_000300 = ak.index_zh_a_hist(symbol="000300")

# 获取sh000852的历史行情数据
df_sh000852 = ak.stock_zh_index_daily(symbol="sh000852")
# 获取HSI的历史行情数据
df_HSI= ak.stock_hk_index_daily_sina(symbol="HSI")
# 获取IXIC的历史行情数据
df_IXIC = ak.index_us_stock_sina(symbol=".IXIC")
df_Au = ak.spot_hist_sge(symbol='Au99.99')
df_300etf = ak.stock_zh_index_daily_em(symbol="sh510300")
df_1000etf = ak.stock_zh_index_daily_em(symbol="sh512100")
df_hz = ak.stock_zh_index_daily_em(symbol="sh513600")
# 获取黄金指数的历史行情数据
df_goldetf = ak.stock_zh_index_daily_em(symbol="sz159834")
# 获取国债指数的历史行情数据
df_bondetf = ak.stock_zh_index_daily_em(symbol="sh511100")
# 获取纳指指数的历史行情数据
df_nzetf = ak.stock_zh_index_daily_em(symbol="sz159509")
# 将日期列转换为日期时间类型
df_000300['日期'] = pd.to_datetime(df_000300['日期'])  # Ensure this column is datetime
df_sh000852['date'] = pd.to_datetime(df_sh000852['date'])  # Ensure this column is datetime
df_HSI['date'] = pd.to_datetime(df_HSI['date'])  # Ensure this column is datetime
df_IXIC['date'] = pd.to_datetime(df_IXIC['date'])  # Ensure this column is datetime
df_Au['date'] = pd.to_datetime(df_Au['date'])  # Ensure this column is datetime
df_300etf['date'] = pd.to_datetime(df_300etf['date'])  # Ensure this column is datetime
df_1000etf['date'] = pd.to_datetime(df_1000etf['date'])  # Ensure this column is datetime
df_hz['date'] = pd.to_datetime(df_hz['date'])  # Ensure this column is datetime
df_goldetf['date'] = pd.to_datetime(df_goldetf['date'])  # Ensure this column is datetime
df_bondetf['date'] = pd.to_datetime(df_bondetf['date'])  # Ensure this column is datetime
df_nzetf['date'] = pd.to_datetime(df_nzetf['date'])  # Ensure this column is datetime
start_date='2024-12-01'
end_date='2024-12-20'
# 筛选出指定日期范围内的数据
df_sh000852 = df_sh000852[(df_sh000852['date'] >= start_date) & (df_sh000852['date'] <= end_date)]
df_000300=df_000300[(df_000300['日期'] >= start_date) & (df_000300['日期'] <= end_date)]
df_HSI=df_HSI[(df_HSI['date'] >= start_date) & (df_HSI['date'] <= end_date)]
df_IXIC=df_IXIC[(df_IXIC['date'] >= start_date) & (df_IXIC['date'] <= end_date)]
df_Au=df_Au[(df_Au['date'] >= start_date) & (df_Au['date'] <= end_date)]
df_300etf=df_300etf[(df_300etf['date'] >= start_date) & (df_300etf['date'] <= end_date)]
df_1000etf=df_1000etf[(df_1000etf['date'] >= start_date) & (df_1000etf['date'] <= end_date)]
df_hz=df_hz[(df_hz['date'] >= start_date) & (df_hz['date'] <= end_date)]
df_goldetf=df_goldetf[(df_goldetf['date'] >= start_date) & (df_goldetf['date'] <= end_date)]
df_bondetf=df_bondetf[(df_bondetf['date'] >= start_date) & (df_bondetf['date'] <= end_date)]
df_nzetf=df_nzetf[(df_nzetf['date'] >= start_date) & (df_nzetf['date'] <= end_date)]
# 选取需要的列并进行重命名
df_000300 = df_000300[['日期', '收盘', '最高', '最低', '成交量']].rename(columns={
    '日期': 'date',
    '收盘': '000300_close',
    '最高': '000300_high',
    '最低': '000300_low',
    '成交量': '000300_volume'
})

df_sh000852 = df_sh000852[['date', 'close', 'high', 'low', 'volume']].rename(columns={
    'close': 'sh000852_close',
    'high': 'sh000852_high',
    'low': 'sh000852_low',
    'volume': 'sh000852_volume'
})

df_HSI = df_HSI[['date', 'close', 'high', 'low', 'volume']].rename(columns={
    'close': 'HSI_close',
    'high': 'HSI_high',
    'low': 'HSI_low',
    'volume': 'HSI_volume'
}) 

df_IXIC = df_IXIC[['date', 'close', 'high', 'low', 'volume']].rename(columns={
    'close': 'IXIC_close',
    'high': 'IXIC_high',
    'low': 'IXIC_low',
    'volume': 'IXIC_volume'
}) 

df_Au = df_Au[['date', 'close', 'high', 'low']].rename(columns={
    'close': 'Au_close',
    'high': 'Au_high',
    'low': 'Au_low'
}) 

df_300etf = df_300etf[['date','close']].rename(columns={
    'close': '300etf_close'})

df_1000etf = df_1000etf[['date','close']].rename(columns={
    'close': '1000etf_close'})

df_hz = df_hz[['date','close']].rename(columns={
    'close': 'hz_close'})
df_goldetf = df_goldetf[['date','close']].rename(columns={
    'close': 'goldetf_close'})
df_bondetf = df_bondetf[['date','close']].rename(columns={
    'close': 'bondetf_close'})
df_nzetf = df_nzetf[['date','close']].rename(columns={
    'close': 'nzetf_close'})

# Merge the two DataFrames on the 'date' column
for n,i in enumerate([df_000300, df_sh000852, df_HSI,df_IXIC,df_Au,df_300etf,df_1000etf,df_hz,df_goldetf,df_bondetf,df_nzetf]):
    if n == 0:
        result = i
    else:
        result = pd.merge(result, i, on='date', how='inner')

# Display the merged DataFrame
result

 导出数据

df = pd.DataFrame(result)
excel_file_name = "data_export.xlsx"
df.to_excel(excel_file_name, index=False)