Python 3.x 如何使用熊猫数据帧和买卖价格计算成交量加权平均价格（VWAP）？

如果我的表如下所示，如何创建另一个名为vwap的列来计算vwap

             time            bid_size   bid       ask  ask_size trade trade_size phase  
0   2019-01-07 07:45:01.064515  495   152.52    152.54    19     NaN      NaN    OPEN   
1   2019-01-07 07:45:01.110072  31    152.53    152.54    19     NaN      NaN    OPEN   
2   2019-01-07 07:45:01.116596  32    152.53    152.54    19     NaN      NaN    OPEN   
3   2019-01-07 07:45:01.116860  32    152.53    152.54    21     NaN      NaN    OPEN   
4   2019-01-07 07:45:01.116905  34    152.53    152.54    21     NaN      NaN    OPEN   
5   2019-01-07 07:45:01.116982  34    152.53    152.54    31     NaN      NaN    OPEN   
6   2019-01-07 07:45:01.147901  38    152.53    152.54    31     NaN      NaN    OPEN   
7   2019-01-07 07:45:01.189971  38    152.53    152.54    31     ask     15.0    OPEN   
8   2019-01-07 07:45:01.189971  38    152.53    152.54    16     NaN      NaN    OPEN   
9   2019-01-07 07:45:01.190766  37    152.53    152.54    16     NaN      NaN    OPEN   
10  2019-01-07 07:45:01.190856  37    152.53    152.54    15     NaN      NaN    OPEN
11  2019-01-07 07:45:01.190856  37    152.53    152.54    16     ask      1.0    OPEN   
12  2019-01-07 07:45:01.193938  37    152.53    152.55   108     NaN      NaN    OPEN   
13  2019-01-07 07:45:01.193938  37    152.53    152.54    15     ask     15.0    OPEN   
14  2019-01-07 07:45:01.194326  2     152.54    152.55   108     NaN      NaN    OPEN   
15  2019-01-07 07:45:01.194453  2     152.54    152.55    97     NaN      NaN    OPEN   
16  2019-01-07 07:45:01.194479  6     152.54    152.55    97     NaN      NaN    OPEN   
17  2019-01-07 07:45:01.194507  19    152.54    152.55    97     NaN      NaN    OPEN   
18  2019-01-07 07:45:01.194532  19    152.54    152.55    77     NaN      NaN    OPEN   
19  2019-01-07 07:45:01.194598  19    152.54    152.55    79     NaN      NaN    OPEN   

抱歉，该表不清楚，但最右边的第二列是trade_size，左边是trade，它显示了交易的一侧（买入或卖出）。如果trade_size和trade均为NaN，则表示在该时间戳没有发生交易

如果df['trade]==“ask”，则交易价格将是“ask”列中的价格，如果df['trade]==“bid”，则交易价格将是“bid”列中的价格。既然有两种价格，我可以问一下如何计算vwap，df['vwap']

---

我的想法是使用np.cumsum（）。谢谢大家!

这里有一种可能的方法

追加

VMAP

列，其中包含

NaN

s

df['VMAP'] = np.nan

计算

VMAP

（基于方程式）并根据

ask

或

bid

赋值

编辑

作为

@edinho

，

VMAP

与

trade\u price

列相同。

好的，在这里

df['trade_price'] = df.apply(lambda x: x['bid'] if x['trade']=='bid' else x['ask'], axis=1)
df['vwap'] = (df['trade_price'] * df['trade_size']).cumsum() / df['trade_size'].fillna(0).cumsum()

第一行：
它将交易价格保存在一个新列中，以便以后更容易检索。
如果您愿意，您可以删除这一行并创建一个函数（可能更容易阅读）。但我更愿意看到中间结果。
问：为什么即使没有交易，它也有价值？
答：因为lambda的书写方式。

else

捕获

ask

价格。但这不会有什么不同，因为下一步就是这样

第二行：
在这里进行实际计算。
第一部分计算到那一刻为止的总交易量（如你所说，使用累计金额使生活更轻松）。
第二部分计算截至该时刻的总交易量（同样是累计金额）。
如果您愿意，您可以打断此行并创建更多中间列。
问：为什么

fillna（0）

？
答：所以总体积不会得到

NaNs

，也不会得到除法错误 问：为什么在

vwap

栏中有这么多

nan

A:因为没有交易的线路。您可以用

0s

填充它们，但最好保留“无交易”信息

注：你可能会得到一个错误的结果，因为它只在同一个方向上考虑数量和价格。但是，您可以尝试反转一些信号，以按照您预期的方式固定音量（例如：将

ask

价格更改为负值）

此代码输出：

    trade_price vwap
1   152.54  NaN
2   152.54  NaN
3   152.54  NaN
4   152.54  NaN
5   152.54  NaN
6   152.54  NaN
7   152.54  NaN
8   152.54  152.54
9   152.54  NaN
10  152.54  NaN
11  152.54  NaN
12  152.54  152.54
13  152.55  NaN
14  152.54  152.54
15  152.55  NaN
16  152.55  NaN
17  152.55  NaN
18  152.55  NaN
19  152.55  NaN
20  152.55  NaN

根据

trade

列中的值，您可以使用从正确的列（

bid

或

ask

）中向您提供价格。请注意，当没有交易发生时，这会给出出价，但因为这会乘以

NaN

交易规模，所以这无关紧要。我还向前填充了VWAP

volume = df['trade_size']
price = np.where(df['trade'].eq('ask'), df['ask'], df['bid'])  
df = df.assign(VWAP=((volume * price).cumsum() / vol.cumsum()).ffill())

>>> df
        time    bid_size    bid ask ask_size    trade   trade_size  phase   VWAP
0   2019-01-07  07:45:01.064515 495 152.52  152.54  19  NaN NaN OPEN    NaN
1   2019-01-07  07:45:01.110072 31  152.53  152.54  19  NaN NaN OPEN    NaN
2   2019-01-07  07:45:01.116596 32  152.53  152.54  19  NaN NaN OPEN    NaN
3   2019-01-07  07:45:01.116860 32  152.53  152.54  21  NaN NaN OPEN    NaN
4   2019-01-07  07:45:01.116905 34  152.53  152.54  21  NaN NaN OPEN    NaN
5   2019-01-07  07:45:01.116982 34  152.53  152.54  31  NaN NaN OPEN    NaN
6   2019-01-07  07:45:01.147901 38  152.53  152.54  31  NaN NaN OPEN    NaN
7   2019-01-07  07:45:01.189971 38  152.53  152.54  31  ask 15.0    OPEN    152.54
8   2019-01-07  07:45:01.189971 38  152.53  152.54  16  NaN NaN OPEN    152.54
9   2019-01-07  07:45:01.190766 37  152.53  152.54  16  NaN NaN OPEN    152.54
10  2019-01-07  07:45:01.190856 37  152.53  152.54  15  NaN NaN OPEN    152.54
11  2019-01-07  07:45:01.190856 37  152.53  152.54  16  ask 1.0 OPEN    152.54
12  2019-01-07  07:45:01.193938 37  152.53  152.55  108 NaN NaN OPEN    152.54
13  2019-01-07  07:45:01.193938 37  152.53  152.54  15  ask 15.0    OPEN    152.54
14  2019-01-07  07:45:01.194326 2   152.54  152.55  108 NaN NaN OPEN    152.54
15  2019-01-07  07:45:01.194453 2   152.54  152.55  97  NaN NaN OPEN    152.54
16  2019-01-07  07:45:01.194479 6   152.54  152.55  97  NaN NaN OPEN    152.54
17  2019-01-07  07:45:01.194507 19  152.54  152.55  97  NaN NaN OPEN    152.54
18  2019-01-07  07:45:01.194532 19  152.54  152.55  77  NaN NaN OPEN    152.54
19  2019-01-07  07:45:01.194598 19  152.54  152.55  79  NaN NaN OPEN    152.54

---

VMAP的方程式是什么？你应该使用哪个价格？出价还是要价？如果df['trade']==“ask”，使用df['ask]。否则，如果df['trade]==“bid”，则使用df[“bid”]。如果df['trade']==NaN，则表示无交易“vol.cumsum（）中的vol应该是“volume”

volume = df['trade_size']
price = np.where(df['trade'].eq('ask'), df['ask'], df['bid'])  
df = df.assign(VWAP=((volume * price).cumsum() / vol.cumsum()).ffill())

>>> df
        time    bid_size    bid ask ask_size    trade   trade_size  phase   VWAP
0   2019-01-07  07:45:01.064515 495 152.52  152.54  19  NaN NaN OPEN    NaN
1   2019-01-07  07:45:01.110072 31  152.53  152.54  19  NaN NaN OPEN    NaN
2   2019-01-07  07:45:01.116596 32  152.53  152.54  19  NaN NaN OPEN    NaN
3   2019-01-07  07:45:01.116860 32  152.53  152.54  21  NaN NaN OPEN    NaN
4   2019-01-07  07:45:01.116905 34  152.53  152.54  21  NaN NaN OPEN    NaN
5   2019-01-07  07:45:01.116982 34  152.53  152.54  31  NaN NaN OPEN    NaN
6   2019-01-07  07:45:01.147901 38  152.53  152.54  31  NaN NaN OPEN    NaN
7   2019-01-07  07:45:01.189971 38  152.53  152.54  31  ask 15.0    OPEN    152.54
8   2019-01-07  07:45:01.189971 38  152.53  152.54  16  NaN NaN OPEN    152.54
9   2019-01-07  07:45:01.190766 37  152.53  152.54  16  NaN NaN OPEN    152.54
10  2019-01-07  07:45:01.190856 37  152.53  152.54  15  NaN NaN OPEN    152.54
11  2019-01-07  07:45:01.190856 37  152.53  152.54  16  ask 1.0 OPEN    152.54
12  2019-01-07  07:45:01.193938 37  152.53  152.55  108 NaN NaN OPEN    152.54
13  2019-01-07  07:45:01.193938 37  152.53  152.54  15  ask 15.0    OPEN    152.54
14  2019-01-07  07:45:01.194326 2   152.54  152.55  108 NaN NaN OPEN    152.54
15  2019-01-07  07:45:01.194453 2   152.54  152.55  97  NaN NaN OPEN    152.54
16  2019-01-07  07:45:01.194479 6   152.54  152.55  97  NaN NaN OPEN    152.54
17  2019-01-07  07:45:01.194507 19  152.54  152.55  97  NaN NaN OPEN    152.54
18  2019-01-07  07:45:01.194532 19  152.54  152.55  77  NaN NaN OPEN    152.54
19  2019-01-07  07:45:01.194598 19  152.54  152.55  79  NaN NaN OPEN    152.54