2022-02-22 07:35:54 +00:00
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import datetime
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2022-02-20 06:06:56 +00:00
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import time
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2022-02-22 07:35:54 +00:00
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import timeit
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2022-02-20 06:06:56 +00:00
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import pandas
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2022-02-22 07:35:54 +00:00
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from fincal.fincal import AllFrequencies, TimeSeries, create_date_series
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2022-02-20 06:06:56 +00:00
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2022-02-22 07:35:54 +00:00
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dfd = pandas.read_csv('test_files/msft.csv')
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2022-02-20 06:06:56 +00:00
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dfm = pandas.read_csv('test_files/nav_history_monthly.csv')
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dfq = pandas.read_csv('test_files/nav_history_quarterly.csv')
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data_d = [(i.date, i.nav) for i in dfd.itertuples()]
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data_m = [{'date': i.date, 'value': i.nav} for i in dfm.itertuples()]
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data_q = {i.date: i.nav for i in dfq.itertuples()}
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2022-02-22 07:35:54 +00:00
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data_q.update({'14-02-2022': 93.7})
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2022-02-20 06:06:56 +00:00
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tsd = TimeSeries(data_d, frequency='D')
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tsm = TimeSeries(data_m, frequency='M', date_format='%d-%m-%Y')
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tsq = TimeSeries(data_q, frequency='Q', date_format='%d-%m-%Y')
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start = time.time()
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# ts.calculate_rolling_returns(datetime.datetime(2015, 1, 1), datetime.datetime(2022, 2, 1), years=1)
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2022-02-22 07:35:54 +00:00
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bdata = tsq.bfill()
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2022-02-20 06:06:56 +00:00
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# rr = tsd.calculate_rolling_returns(datetime.datetime(2022, 1, 1), datetime.datetime(2022, 2, 1), years=1)
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print(time.time() - start)
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