如果我的表如下所示，我如何创建另一个称为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，但是第二最右边的列是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，则表示在该时间戳下没有交易发生。
如果df ['trade'] ==“问”，则交易价格将为'ask'列中的价格；如果df ['trade] ==“出价”，则交易价格将为''列中的价格。出价'。既然有2个价格，请问我该如何计算vwap df ['vwap']？
我的想法是使用np.cumsum（）。谢谢！
答案 0 :(得分：1)
这是一种可能的方法
在ToArray() s的enumerators.Select(e => e.Current)列中追加
VMAP计算NaN（基于this公式provided by the OP）并基于df['VMAP'] = np.nan
或VMAP，as requierd by the OP
ask编辑
与bid correctly indicated一样，for trade in ['ask','bid']:
# Find indexes of `ask` or `buy`
bid_idx = df[df.trade==trade].index
# Slice DF based on `ask` or `buy`, using indexes
df.loc[bid_idx, 'VMAP'] = (
(df.loc[bid_idx, 'trade_size'] * df.loc[bid_idx, trade]).cumsum()
/
(df.loc[bid_idx, 'trade_size']).cumsum()
)
print(df.iloc[:,1:])
time bid_size bid ask ask_size trade trade_size phase VMAP
0 07:45:01.064515 495 152.52 152.54 19 NaN NaN OPEN NaN
1 07:45:01.110072 31 152.53 152.54 19 NaN NaN OPEN NaN
2 07:45:01.116596 32 152.53 152.54 19 NaN NaN OPEN NaN
3 07:45:01.116860 32 152.53 152.54 21 NaN NaN OPEN NaN
4 07:45:01.116905 34 152.53 152.54 21 NaN NaN OPEN NaN
5 07:45:01.116982 34 152.53 152.54 31 NaN NaN OPEN NaN
6 07:45:01.147901 38 152.53 152.54 31 NaN NaN OPEN NaN
7 07:45:01.189971 38 152.53 152.54 31 ask 15.0 OPEN 152.54
8 07:45:01.189971 38 152.53 152.54 16 NaN NaN OPEN NaN
9 07:45:01.190766 37 152.53 152.54 16 NaN NaN OPEN NaN
10 07:45:01.190856 37 152.53 152.54 15 NaN NaN OPEN NaN
11 07:45:01.190856 37 152.53 152.54 16 ask 1.0 OPEN 152.54
12 07:45:01.193938 37 152.53 152.55 108 NaN NaN OPEN NaN
13 07:45:01.193938 37 152.53 152.54 15 ask 15.0 OPEN 152.54
14 07:45:01.194326 2 152.54 152.55 108 NaN NaN OPEN NaN
15 07:45:01.194453 2 152.54 152.55 97 NaN NaN OPEN NaN
16 07:45:01.194479 6 152.54 152.55 97 NaN NaN OPEN NaN
17 07:45:01.194507 19 152.54 152.55 97 NaN NaN OPEN NaN
18 07:45:01.194532 19 152.54 152.55 77 NaN NaN OPEN NaN
19 07:45:01.194598 19 152.54 152.55 79 NaN NaN OPEN NaN
与@edinho列相同。
答案 1 :(得分：1)
好，就在这里
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trade_price保存在新列中，因此以后更容易检索它。
如果需要，您可以删除此行并创建一个函数（也许更容易阅读）。但我更喜欢看中介结果。
问：为什么即使没有交易也有价值？
答：由于lambda的编写方式。 else捕获ask的价格。但这不会有任何变化，因为下一步。
第二行：
在这里进行真正的计算。
第一部分计算到那一刻为止的总交易量（如您所说，使用累计总和可以使工作更轻松）。
第二部分计算到那一刻为止的总交易量（再次是累计金额）。
如果需要，可以中断此行并增加更多中间列。
问：为什么使用fillna(0)？
答：因此总交易量不会达到NaNs，而且您不会遇到除法错误
问：为什么NaNs列中有这么多vwap？
答：由于没有交易渠道。您可以用0s填充它们，但最好保留“禁止交易”的信息。
Ps .：您可能会得到错误的结果，因为它只考虑相同方向的数量和价格。但是，您可以尝试以某种方式反转一些信号以固定音量（例如：将ask价格更改为负数）。
，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
答案 2 :(得分：1)
您可以根据bid列中的值，使用np.where从正确的列（ask或trade）中为您提供价格。请注意，这将在没有交易发生时为您提供买入价，但是因为随后将其乘以NaN交易量就没有关系了。我也提前填写了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