How do i create another column called vwap which calculates the vwap if my table is as shown below?
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
Sorry, the table is not clear, but the second most right column is trade_size, on its left is trade, which shows the side of the trade( bid or ask). if both trade_size and trade are NaN, it indicates that no trade occur at that timestamp.
If df['trade'] == "ask", trade price will be the price in column 'ask' and if df['trade] == "bid", the trade price will be the price in column 'bid'. Since there are 2 prices, may I ask how can i calculate the vwap, df['vwap']?
My idea is to use np.cumsum().
You can use np.where to give you the price from the correct column (bid or ask) depending on the value in the trade column. Note that this gives you the bid price when no trade occurs, but because this is then multiplied by a NaN trade size it won't matter. I also forward filled the 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