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股票分割 API 谷歌或雅虎

发布于 2024-11-09 13:29:01 字数 96 浏览 0 评论 0原文

我正在寻找一种获取股票分割信息的方法。使用雅虎股票 API，我可以获取任何交易品种的所有类型的信息，但我认为我无法获取分割比率，甚至无法获取它是否分割。有谁知道获取此信息的方法？

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静水深流 2024-11-16 13:29:01

这就是 quantmod R 包的方式做到了。分割信息位于“仅股息”CSV 中：
<一href="http://icart.finance.yahoo.com/x?s=IBM&a=00&b=2&c=1962&d=04&e=25&f=2011&g=v&y =0&z=30000 ">http://ichart.finance.yahoo.com/x?s=IBM&a=00&b=2&c=1962&d=04&e=25&f=2011&g=v&y= 0&z=30000

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在梵高的星空下 2024-11-16 13:29:01

您可以借助 pandas datareader 包在 python 3 中轻松完成此操作。
开始定义一个函数，该函数将以数据帧的形式返回分割历史记录：

def split_history(stock, date_start, date_end, limit_denominator=1000):
    from decimal import Decimal
    from fractions import Fraction
    from pandas_datareader import data as web
    df = web.DataReader(stock, data_source='yahoo-actions', start=date_start, end=date_end)
    is_split = df['action']=='SPLIT'
    df = df[is_split]
    ratios = []
    for index, row in df.iterrows():
        # Taking the inverse of the row['value'] as it is Yahoo finance convention
        ratio = Fraction(1/Decimal(row['value'])).limit_denominator(limit_denominator)
        ratios.append("{num} for {denom}".\
                            format(num=ratio.numerator, denom=ratio.denominator))
    df['ratio'] = ratios
    return df

现在我们可以以 Microsoft（“MSFT”）的分割为例：

stock = 'MSFT'
date_start = '1987-01-01'
date_end = '2020-07-22'
split_history(stock, date_start, date_end)

            action  value       ratio
2003-02-18  SPLIT   0.500000    2 for 1
1999-03-29  SPLIT   0.500000    2 for 1
1998-02-23  SPLIT   0.500000    2 for 1
1996-12-09  SPLIT   0.500000    2 for 1
1994-05-23  SPLIT   0.500000    2 for 1
1992-06-15  SPLIT   0.666667    3 for 2
1991-06-27  SPLIT   0.666667    3 for 2
1990-04-16  SPLIT   0.500000    2 for 1
1987-09-21  SPLIT   0.500000    2 for 1

它还可以正确处理反向股票分割：

stock = 'PHM.MC'
split_history(stock, date_start, date_end)

                action  value   ratio
 2020-07-22     SPLIT   12.0    1 for 12

ps：可能有更好的方法来输入日期。
ps2：此外，限制分母是为了避免错误的舍入。您可以在罕见的分流比情况下扩展它。

You can do it easily in python 3 with the help of the pandas datareader package.
Starting defining a function which will return the split history as a dataframe:

def split_history(stock, date_start, date_end, limit_denominator=1000):
    from decimal import Decimal
    from fractions import Fraction
    from pandas_datareader import data as web
    df = web.DataReader(stock, data_source='yahoo-actions', start=date_start, end=date_end)
    is_split = df['action']=='SPLIT'
    df = df[is_split]
    ratios = []
    for index, row in df.iterrows():
        # Taking the inverse of the row['value'] as it is Yahoo finance convention
        ratio = Fraction(1/Decimal(row['value'])).limit_denominator(limit_denominator)
        ratios.append("{num} for {denom}".\
                            format(num=ratio.numerator, denom=ratio.denominator))
    df['ratio'] = ratios
    return df

Now we can get the splits of Microsoft ('MSFT') as an example:

stock = 'MSFT'
date_start = '1987-01-01'
date_end = '2020-07-22'
split_history(stock, date_start, date_end)

            action  value       ratio
2003-02-18  SPLIT   0.500000    2 for 1
1999-03-29  SPLIT   0.500000    2 for 1
1998-02-23  SPLIT   0.500000    2 for 1
1996-12-09  SPLIT   0.500000    2 for 1
1994-05-23  SPLIT   0.500000    2 for 1
1992-06-15  SPLIT   0.666667    3 for 2
1991-06-27  SPLIT   0.666667    3 for 2
1990-04-16  SPLIT   0.500000    2 for 1
1987-09-21  SPLIT   0.500000    2 for 1

It handles also properly the reverse stock splits:

stock = 'PHM.MC'
split_history(stock, date_start, date_end)

                action  value   ratio
 2020-07-22     SPLIT   12.0    1 for 12

ps: probably there are better ways to input the dates.
ps2: also, the limit_denominator is there to avoid wrong roundings. You can extend it in rare split ratio cases.

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