I have 1-min ticker information of Apple in a dataframe as so:
Local time Open High Low Close Volume
0 2018-04-19 15:00:00 46.707 46.708 46.687 46.687 0.0150
1 2018-04-19 15:01:00 46.688 46.688 46.667 46.688 0.0320
2 2018-04-19 15:02:00 46.687 46.728 46.677 46.728 0.0091
3 2018-04-19 15:03:00 46.727 46.728 46.708 46.717 0.0332
4 2018-04-19 15:04:00 46.708 46.718 46.677 46.677 0.0243
I have converted the "Local time" column into datetime using pd.to_datetime(df['Local time']). I want to go through each day individually to backtest a strategy. But I do not know how to loop through chunks of the df one at a time defined by a change in date. I tried using some for loops but they did not work since the number of minutes traded is apparently different on some days (not 390):
index = 390 #Number of traded minutes on most days
rows = 286155 #number of rows in the dataset
for x in range(286155/390):
index = index * x
index2 = index * (x-1)
for y in df[index2:index]:
'''Strategy to be Executed for that day'''
How can I achieve what I want to do?
As suggested by @Ben.T:
for dt, df in data.groupby(data["Local time"].dt.date):
print(f"\n[{dt}]")
print(df.head())
# do stuff here
[2021-04-16]
Local time Value
0 2021-04-16 00:00:00 28.15
1 2021-04-16 00:01:00 25.33
2 2021-04-16 00:02:00 82.04
3 2021-04-16 00:03:00 17.81
4 2021-04-16 00:04:00 80.71
[2021-04-17]
Local time Value
1440 2021-04-17 00:00:00 67.72
1441 2021-04-17 00:01:00 52.91
1442 2021-04-17 00:02:00 26.40
1443 2021-04-17 00:03:00 69.11
1444 2021-04-17 00:04:00 91.88
[2021-04-18]
Local time Value
2880 2021-04-18 00:00:00 13.03
2881 2021-04-18 00:01:00 53.42
2882 2021-04-18 00:02:00 9.28
2883 2021-04-18 00:03:00 77.74
2884 2021-04-18 00:04:00 24.91