core

All core functions and classes in ecfintools

Legacy function from former package finutilities

Set of functions that are transferred from my former package finutilities

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safe_date

 safe_date (dt:str|datetime.datetime)

*Return a datetime object from string or datetime

Accepted formats:

  • ‘YYYY-MM-DD’, ‘YYYY/MM/DD’, ‘YYYY.MM.DD’
  • ‘HH:MM:SS’ can be added to the above formats*
Type Details
dt str | datetime.datetime date time to validate or convert
Returns datetime datetime type object

This function converts date-time strings into a datetime object, as long as the strings are in one of the following formats:

  • 2024-12-15 21:02:17
  • 2024/12/15 21:02:17
  • 2024.12.15 21:02:17
  • 2024-12-15
  • 2024/12/15
  • 2024.12.15

The function also accepts a datetime as argument dt and can be used to ensure a data argument used in a function is a datetime object.

assert isinstance(safe_date('2024-12-15 21:02:17'), datetime)
assert isinstance(safe_date('2024/12/15 21:02:17'), datetime)
assert isinstance(safe_date('2024.12.15 21:02:17'), datetime)
assert isinstance(safe_date('2024-12-15'), datetime)
assert isinstance(safe_date('2024/12/15'), datetime)
assert isinstance(safe_date('2024.12.15'), datetime)
assert isinstance(safe_date(datetime.now()), datetime)

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time_slice_df

 time_slice_df (df:pandas.core.frame.DataFrame,
                start:str|datetime.datetime|None=None,
                end:str|datetime.datetime|None=None)

Slices DataFrame’s rows from start to end, or the closest datetime available in the DataTimeIndex.

Type Default Details
df DataFrame df that will be sliced, must have a DateTimeIndex
start str | datetime.datetime | None None start date for slicing
end str | datetime.datetime | None None end date for slicing
Returns DataFrame sliced df

Even if we pass start and end dates that are not in the datafrane DateTimeIndex, the function still will slice using the closest datetime(s) in the index.

Let’s take an test dataframe for illustration.

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load_test_df

 load_test_df ()
df = load_test_df()
df.iloc[:15, :]
Open High Low Close Volume
dt
2018-10-22 2759.02 2779.27 2747.27 2754.48 26562
2018-10-23 2753.11 2755.36 2690.69 2743.45 38777
2018-10-24 2744.83 2748.58 2651.23 2672.80 41777
2018-10-25 2670.80 2722.90 2657.93 2680.71 39034
2018-10-26 2675.59 2692.34 2627.59 2663.57 61436
2018-10-29 2667.70 2707.00 2603.33 2639.17 44960
2018-10-30 2639.55 2689.50 2633.05 2688.50 52786
2018-10-31 2688.88 2736.76 2681.25 2704.75 32374
2018-11-01 2707.13 2741.58 2706.88 2731.90 29565
2018-11-02 2725.28 2766.28 2699.96 2723.76 41892
2018-11-05 2721.51 2744.41 2713.14 2740.26 25161
2018-11-06 2739.39 2761.53 2730.14 2759.15 20481
2018-11-07 2757.28 2816.63 2745.53 2815.88 29723
2018-11-08 2814.76 2817.01 2794.68 2805.02 18369
2018-11-09 2803.40 2810.15 2763.78 2778.60 25123

In this example, the DataFrame misses a few days, e.g.: - 2018-10-27 and 2018-10-28 - 2018-11-03 and 2018-11-04

If we pick one of these dates for start and end respectively, we still get a sampled dataframe with the closed existing dates:

time_slice_df(df, '2018-10-27', '2018-11-04')
Open High Low Close Volume
dt
2018-10-29 2667.70 2707.00 2603.33 2639.17 44960
2018-10-30 2639.55 2689.50 2633.05 2688.50 52786
2018-10-31 2688.88 2736.76 2681.25 2704.75 32374
2018-11-01 2707.13 2741.58 2706.88 2731.90 29565
2018-11-02 2725.28 2766.28 2699.96 2723.76 41892