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Historical data returning strange candles

amit123
amit123 in Python client
1 minute data fetched is sometimes returning candle which start like 11:54:33

This is happening quite often. havent tested for other time intervals. a

date open high low close volume
37618 2015-06-29 11:50:00 70.000000 70.150002 70.000000 70.050003 134
37619 2015-06-29 11:51:00 70.000000 70.000000 70.000000 70.000000 100
37620 2015-06-29 11:52:00 70.000000 70.000000 70.000000 70.000000 0
37621 2015-06-29 11:53:00 70.000000 70.150002 70.000000 70.150002 272
37622 2015-06-29 11:54:00 70.050003 70.050003 70.000000 70.000000 1068
37623 2015-06-29 11:54:33 70.000000 70.000000 70.000000 70.000000 34
37624 2015-06-29 11:55:00 70.000000 70.099998 70.000000 70.099998 550
37625 2015-06-29 11:56:00 70.099998 70.150002 70.099998 70.150002 117
37626 2015-06-29 11:57:00 70.150002 70.199997 70.150002 70.199997 250
37627 2015-06-29 11:58:00 70.099998 70.099998 69.949997 69.949997 1914
37628 2015-06-29 11:59:00 69.949997 70.000000 69.949997 69.949997 638
Tagged:
  • amit123
    amit123 edited December 2023
    Here is more sample for : token = 3756033
    1 minute historical data fetched for multiple days in dataframe df

    df[df.date.dt.second!=0]
    date open high low close volume
    35588 2015-06-22 09:15:01 72.599998 72.849998 72.599998 72.699997 3881
    35963 2015-06-23 09:15:01 73.599998 73.900002 73.599998 73.800003 14103
    36338 2015-06-24 09:15:01 74.000000 74.199997 73.849998 73.900002 4606
    37088 2015-06-26 09:15:01 72.199997 72.349998 72.099998 72.300003 2590
    37463 2015-06-29 09:15:01 71.000000 71.000000 70.099998 70.400002 7673
    37623 2015-06-29 11:54:33 70.000000 70.000000 70.000000 70.000000 34
    38589 2015-07-02 09:15:01 74.250000 74.500000 74.250000 74.400002 10713
    38782 2015-07-02 12:28:26 73.250000 73.300003 73.250000 73.250000 1015
    38964 2015-07-03 09:15:01 73.400002 73.500000 73.349998 73.400002 2105
    41589 2015-07-14 09:15:01 78.949997 79.250000 78.849998 78.849998 32744
    41964 2015-07-15 09:15:01 79.699997 79.849998 79.550003 79.699997 31103
    42714 2015-07-17 09:15:01 84.400002 84.500000 83.699997 83.699997 631703
    43089 2015-07-20 09:15:01 82.449997 82.900002 82.199997 82.699997 70128
    43173 2015-07-20 10:39:03 82.349998 82.349998 82.349998 82.349998 958
    43174 2015-07-20 10:40:25 82.099998 82.300003 82.099998 82.300003 10318
    43192 2015-07-20 10:58:10 82.300003 82.400002 82.300003 82.400002 1910
    43201 2015-07-20 11:08:05 82.949997 82.949997 82.800003 82.800003 154987
    43202 2015-07-20 11:09:23 82.599998 82.699997 82.599998 82.699997 29883
    43206 2015-07-20 11:15:48 82.550003 82.550003 82.550003 82.550003 0
    43207 2015-07-20 11:17:05 82.449997 82.500000 82.449997 82.449997 36184
    43212 2015-07-20 11:22:05 82.449997 82.650002 82.449997 82.599998 7212
    43460 2015-07-21 09:15:01 83.900002 84.400002 83.900002 84.400002 27374
    44210 2015-07-23 09:15:01 82.699997 82.800003 82.699997 82.800003 2155
    44960 2015-07-27 09:15:01 78.900002 78.949997 78.650002 78.949997 3680
    45335 2015-07-28 09:15:03 76.000000 76.000000 75.849998 75.900002 6397
    49844 2015-08-13 09:23:56 69.699997 69.699997 69.699997 69.699997 0


    This is definitely a bug. please fix it as it is a code breaking bug.
  • abhishek384
    abhishek384
    yes, I found the same issue, in a single timeframe I am getting different timestamp
  • rakeshr
    rakeshr
    @amit123 @abhishek384
    Thank you for highlighting this. We are checking on this.
  • Harsh_Kantariya
    Harsh_Kantariya edited December 2023
    @amit123 @abhishek384 @rakeshr yes that issue started arising since few days. When calling historical_data api we give from_date and to_date and in that arguments, if it contains second, then the return data will have candles which starts from that second and ends at that second and next candle will also be same.
    For instance if I pass in from_date = datetime.datetime(2023, 12, 14, 10, 32, 18, 655849) and to_date = datetime.datetime(2023, 12, 14, 13, 17, 6, 884108). it will return candles like below:
    date open high low close volume
    0 2023-12-14 10:32:18+05:30 21253.50 21255.95 21235.00 21241.05 78200
    1 2023-12-14 10:37:18+05:30 21241.05 21244.00 21232.10 21238.90 39100
    2 2023-12-14 10:42:18+05:30 21238.90 21247.40 21228.55 21242.55 65300
    3 2023-12-14 10:47:18+05:30 21242.55 21258.80 21242.55 21253.60 56350
    4 2023-12-14 10:52:18+05:30 21253.60 21259.00 21242.55 21249.25 39500
    5 2023-12-14 10:57:18+05:30 21249.25 21253.40 21245.80 21249.95 23650
    6 2023-12-14 11:02:18+05:30 21250.00 21252.20 21245.60 21252.20 18500
    7 2023-12-14 11:07:18+05:30 21252.20 21255.00 21247.00 21253.25 25800
    8 2023-12-14 11:12:18+05:30 21253.25 21257.30 21246.00 21254.20 34500
    9 2023-12-14 11:17:18+05:30 21254.20 21254.20 21245.10 21245.10 17050
    10 2023-12-14 11:22:18+05:30 21245.10 21255.20 21245.10 21248.00 25500
    11 2023-12-14 11:27:18+05:30 21248.00 21248.70 21242.30 21246.05 18200
    12 2023-12-14 11:32:18+05:30 21246.05 21254.90 21245.20 21252.85 27750
    13 2023-12-14 11:37:18+05:30 21252.00 21267.60 21250.00 21264.25 62300
    14 2023-12-14 11:42:18+05:30 21265.00 21268.00 21260.30 21265.45 42450.....
    so just a simple solution is to replace seconds as 0 in datetime object.
    Hope this helps.
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