Question

def generate_dollar_volume_weights(close, volume):
     """
     Generate dollar volume weights.

     Parameters
     ----------
     close : DataFrame
           Close price for each ticker and date
     volume : str
            Volume for each ticker and date

     Returns
     -------
     dollar_volume_weights : DataFrame
                           The dollar volume weights for each ticker and date
      """
     assert close.index.equals(volume.index)
     assert close.columns.equals(volume.columns)

     #TODO: Implement function
     dollar_volume_weights=np.cumsum(close * volume) / np.cumsum(volume)

     return dollar_volume_weights
 project_tests.test_generate_dollar_volume_weights(generate_dollar_volume_weights)#testing the function

所以我的结果如下

INPUT close:
                  XHNP        FCUB        ZYRP
2005-09-09 35.44110000 34.17990000 34.02230000
2005-09-10 92.11310000 91.05430000 90.95720000
2005-09-11 57.97080000 57.78140000 58.19820000
2005-09-12 34.17050000 92.45300000 58.51070000

INPUT volume:
                        XHNP              FCUB              ZYRP
2005-09-09  9836830.00000000 17807200.00000000  8829820.00000000
2005-09-10 82242700.00000000 68531500.00000000 48160100.00000000
2005-09-11 16234800.00000000 13052700.00000000  9512010.00000000
2005-09-12 10674200.00000000 56831300.00000000  9316010.00000000

OUTPUT dollar_volume_weights:
                  XHNP        FCUB        ZYRP
2005-09-09 35.44110000 34.17990000 34.02230000
2005-09-10 86.05884636 79.32405834 82.13590461
2005-09-11 81.84884187 76.49494177 78.71200871
2005-09-12 77.57172242 82.30022612 76.22980471

EXPECTED OUTPUT FOR dollar_volume_weights:
                 XHNP       FCUB       ZYRP
2005-09-09 0.27719777 0.48394253 0.23885970
2005-09-10 0.41632975 0.34293308 0.24073717
2005-09-11 0.41848548 0.33536102 0.24615350
2005-09-12 0.05917255 0.85239760 0.08842984

我是这里的初学者，我真的不明白我在这里缺少什么，我认为用于生成美元交易量权重的表达式应该类似于dollar_volume_weights=np.cumsum(close * volume) / np.cumsum(volume) 有人可以告诉我为什么我的结果与众不同吗？

Answer 1

我认为您应该进行close * volume，然后除以np.sum(x, axis=1)的总和（volume * close）并指定axis=1。此外，您可以使用div()函数进行此划分并指定axis=0。

Answer 2

您能解释一下您希望如何从输入中计算出dollar_volume_weights以获得预期结果吗？

为清楚代码的作用，以下内容显示了计算结果的逻辑，

2005-09-09 close * volume / volume  ...
2005-09-10 (close1*volume1) + (close2*volume2) / (volume1+volume2) ...

Answer 3

我在这里同意@joseph。我假设您正在谈论Udacity的Smart Beta项目。他们在先前牢房中的榜样令人误解，这让我很困惑。无论如何，我解决问题的方式如下：

def generate_dollar_volume_weights(close, volume):
    """
    Generate dollar volume weights.

    Parameters
    ----------
    close : DataFrame
        Close price for each ticker and date
    volume : str
        Volume for each ticker and date

    Returns
    -------
    dollar_volume_weights : DataFrame
        The dollar volume weights for each ticker and date
    """
    assert close.index.equals(volume.index)
    assert close.columns.equals(volume.columns)
    
    #TODO: Implement function
    print(close)
    print(volume)
    
    dollar_volume = close * volume
    print(dollar_volume)

    total_dollar_volume = dollar_volume.sum(axis=1)
    print(total_dollar_volume)
    
    dollar_volume_weights = dollar_volume.div(total_dollar_volume,axis=0)
    print(dollar_volume_weights)

    return dollar_volume_weights

project_tests.test_generate_dollar_volume_weights(generate_dollar_volume_weights)

Smart Beta投资组合：如何根据每个日期的交易量产生权重

3 个答案: