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58 lines
1.4 KiB
58 lines
1.4 KiB
import pandas as pd
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from fincal.fincal import TimeSeries, create_date_series
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dfd = pd.read_csv("test_files/nav_history_daily - Copy.csv")
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dfd = dfd[dfd["amfi_code"] == 118825].reset_index(drop=True)
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ts = TimeSeries([(i.date, i.nav) for i in dfd.itertuples()], frequency="D")
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repr(ts)
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# print(ts[['2022-01-31', '2021-05-28']])
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# rr = ts.calculate_rolling_returns(from_date='2021-01-01', to_date='2022-01-01', frequency='D', interval_type='days', interval_value=30, compounding=False)
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# data = [
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# ("2020-01-01", 10),
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# ("2020-02-01", 12),
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# ("2020-03-01", 14),
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# ("2020-04-01", 16),
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# ("2020-05-01", 18),
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# ("2020-06-01", 20),
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# ("2020-07-01", 22),
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# ("2020-08-01", 24),
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# ("2020-09-01", 26),
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# ("2020-10-01", 28),
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# ("2020-11-01", 30),
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# ("2020-12-01", 32),
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# ("2021-01-01", 34),
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# ]
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# ts = TimeSeries(data, frequency="M")
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# rr = ts.calculate_rolling_returns(
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# "2020-02-01",
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# "2021-01-01",
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# if_not_found="nan",
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# compounding=False,
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# interval_type="months",
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# interval_value=1,
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# as_on_match="exact",
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# )
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# for i in rr:
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# print(i)
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# returns = ts.calculate_returns(
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# "2020-04-25",
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# return_actual_date=True,
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# closest_max_days=15,
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# compounding=True,
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# interval_type="days",
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# interval_value=90,
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# closest="previous",
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# if_not_found="fail",
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# )
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# print(returns)
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volatility = ts.volatility(start_date="2018-01-01", end_date="2021-01-01")
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print(volatility)
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