I have an intra day series of log returns over multiple days that I would like to downsample to daily ohlc. I can do something like
hi = series.resample('B', how=lambda x: np.max(np.cumsum()))
low = series.resample('B', how=lambda x: np.min(np.cumsum()))
But it seems inefficient to compute cumsum on each call. Is there a way to first compute the cumsums and then apply 'ohcl' to the data?
1999-08-09 12:30:00-04:00 -0.000486
1999-08-09 12:31:00-04:00 -0.000606
1999-08-09 12:32:00-04:00 -0.000120
1999-08-09 12:33:00-04:00 -0.000037
1999-08-09 12:34:00-04:00 -0.000337
1999-08-09 12:35:00-04:00 0.000100
1999-08-09 12:36:00-04:00 0.000219
1999-08-09 12:37:00-04:00 0.000285
1999-08-09 12:38:00-04:00 -0.000981
1999-08-09 12:39:00-04:00 -0.000487
1999-08-09 12:40:00-04:00 0.000476
1999-08-09 12:41:00-04:00 0.000362
1999-08-09 12:42:00-04:00 -0.000038
1999-08-09 12:43:00-04:00 -0.000310
1999-08-09 12:44:00-04:00 -0.000337
...
1999-09-28 06:45:00-04:00 0.000000
1999-09-28 06:46:00-04:00 0.000000
1999-09-28 06:47:00-04:00 0.000000
1999-09-28 06:48:00-04:00 0.000102
1999-09-28 06:49:00-04:00 -0.000068
1999-09-28 06:50:00-04:00 0.000136
1999-09-28 06:51:00-04:00 0.000566
1999-09-28 06:52:00-04:00 0.000469
1999-09-28 06:53:00-04:00 0.000000
1999-09-28 06:54:00-04:00 0.000000
1999-09-28 06:55:00-04:00 0.000000
1999-09-28 06:56:00-04:00 0.000000
1999-09-28 06:57:00-04:00 0.000000
1999-09-28 06:58:00-04:00 0.000000
1999-09-28 06:59:00-04:00 0.000000
df.groupby([df.index.year, df.index.month, df.index.day]).transform(np.cumsum).resample('B', how='ohlc')
I think this might be what I want but I have to test.
EDIT: After zelazny7's repsonse:
df.groupby(pd.TimeGrouper('D')).transform(np.cumsum).resample('D', how='ohlc')
works and is also more efficient than my previous solution.
UPDATE:
pd.TimeGrouper('D') is deprecated since pandas v0.21.0.
Use pd.Grouper()
instead:
df.groupby(pd.Grouper(freq='D')).transform(np.cumsum).resample('D', how='ohlc')