如何使用带有询价和出价的熊猫数据框计算体积加权平均价格（VWAP）？
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我如何创建另一个名为vwap的列，如果我的表如下所示，它会计算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
对不起，表格不清楚，但第二个最右边的列是trade_size，左边是交易，它显示交易的一面（买入或卖出）。如果trade_size和trade都是NaN，则表示该时间戳没有交易。
如果df ['trade'] ==“ask”，交易价格将是'ask'栏中的价格，如果df ['trade] ==“bid”，交易价格将是'bid'栏中的价格。由于有2个价格，请问如何计算vwap，df ['vwap']？
我的想法是使用np.cumsum（）。谢谢！
答案
您可以使用np.where从正确的列（bid或ask）中获取价格，具体取决于trade列中的值。请注意，这可以在没有交易发生时为您提供出价，但因为这会乘以NaN交易规模而无关紧要。我也向前填补了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
另一答案
这是一种可能的方法
附加VMAP列充满NaNs
df['VMAP'] = np.nan
计算VMAP（基于this方程provided by the OP）并根据ask或bid，as requierd by the OP分配值
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
编辑
作为@edinho correctly indicated，VMAP与trade_price列相同。
另一答案
好的，这是
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，可以删除此行并创建一个函数（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：因为编写lambda的方式。 else捕获了ask的价格。但由于下一步，它不会有所作为。
第二行：
这里进行了真正的计算。
第一部分计算到那个时刻交易的总交易量（如你所说，使用累计金额使生活更轻松）。
第二部分计算到那个时刻交易的总交易量（再次，累计总和）。
如果需要，可以断开此行并创建更多中间列。
问：为什么fillna(0)？
- 答：所以总量不得NaNs，你没有得到除法错误问：为什么NaNs列中有这么多vwap？
答：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。
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，您可以尝试反转某些信号以按预期方式修复音量（例如：将ask价格更改为负值）。
而这段代码输出：
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
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