将历史实时股票数据获取到多次调用并按周范围获取

Question

由于我的代理的一些限制，要获取日内历史数据，单个rest调用中允许的最大日期范围是7天，任何违反此范围的请求都会返回一个错误。因此，我需要打破不同调用之间的时间间隔，以获取超出此允许日期范围的数据。到目前为止，我已经手动拆分它以获取数据并将其附加到一个数据帧中

def get_historical_data(scrip):
    data1 = historicData(scrip, "01/07/2019", "07/07/2019")
    data2 = historicData(scrip, "08/07/2019", "14/07/2019")
    data3 = historicData(scrip, "15/07/2019", "21/07/2019")
    data4 = historicData(scrip, "22/07/2019", "28/07/2019")
    data5 = historicData(scrip, "28/07/2019", "31/07/2019")
    data6 = historicData(scrip, "01/08/2019", "07/08/2019")
    data7 = historicData(scrip, "08/08/2019", "14/08/2019")
    data8 = historicData(scrip, "15/08/2019", "21/08/2019")
    data9 = historicData(scrip, "22/08/2019", "28/08/2019")
    data10 = historicData(scrip, "29/08/2019", "04/09/2019")
    data11 = historicData(scrip, "05/09/2019", "11/09/2019")
    data12 = historicData(scrip, "12/09/2019", "17/09/2019")
    data13 = historicData(scrip, "18/09/2019", "18/09/2019")

    data1 = data1.append(data2)
    data1 = data1.append(data3)
    data1 = data1.append(data4)
    data1 = data1.append(data5)
    data1 = data1.append(data6)
    data1 = data1.append(data7)
    data1 = data1.append(data9)
    data1 = data1.append(data10)
    data1 = data1.append(data11)
    data1 = data1.append(data12)
    data1 = data1.append(data13)

    data1 = data1.reset_index(drop=True)

    return data1

一个很好的方法是将日期范围拆分为一周的间隔，而不必手动执行，并在追加后返回完整的数据。如下所示：

def get_historical_data(scrip, start_date, end_date):

    #Some logic to split the start_date and end_date for one-week interval and fetch the data. 
    #After fetching the data, append into one df. 

    #return appended_data

Answer 1

您可以开始创建一个带有第一个日期的datetime，然后循环向其中添加timedelta（days=7），直到到达end_date