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

source

safe_date


def safe_date(
    dt:str | datetime.datetime, # date time to validate or convert
)->datetime: # datetime type object

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

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)

source

time_slice_df


def time_slice_df(
    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
)->DataFrame: # sliced `df`

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

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.

source

load_test_df


def load_test_df(
    
)->DataFrame:

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