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changed interval_type to return_period_unit

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Added documentation to volatility function
This commit is contained in:
Gourav Kumar 2022-03-13 14:28:08 +05:30
parent d7b06fbe24
commit 810e0bfb65
1 changed files with 40 additions and 21 deletions
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61
fincal/fincal.py
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@ -200,8 +200,8 @@ class TimeSeries(TimeSeriesCore):
closest_max_days: int = -1,
if_not_found: Literal["fail", "nan"] = "fail",
annual_compounded_returns: bool = True,
interval_type: Literal["years", "months", "days"] = "years",
interval_value: int = 1,
return_period_unit: Literal["years", "months", "days"] = "years",
return_period_value: int = 1,
date_format: str = None,
) -> float:
"""Method to calculate returns for a certain time-period as on a particular date
@ -239,10 +239,10 @@ class TimeSeries(TimeSeriesCore):
compounding : bool, optional
Whether the return should be compounded annually.
interval_type : 'years', 'months', 'days'
return_period_unit : 'years', 'months', 'days'
The type of time period to use for return calculation.
interval_value : int
return_period_value : int
The value of the specified interval type over which returns needs to be calculated.
date_format: str
@ -268,7 +268,7 @@ class TimeSeries(TimeSeriesCore):
as_on_delta, prior_delta = _preprocess_match_options(as_on_match, prior_match, closest)
prev_date = as_on - relativedelta(**{interval_type: interval_value})
prev_date = as_on - relativedelta(**{return_period_unit: return_period_value})
current = _find_closest_date(self.data, as_on, closest_max_days, as_on_delta, if_not_found)
if current[1] != str("nan"):
previous = _find_closest_date(self.data, prev_date, closest_max_days, prior_delta, if_not_found)
@ -278,7 +278,7 @@ class TimeSeries(TimeSeriesCore):
returns = current[1] / previous[1]
if annual_compounded_returns:
years = _interval_to_years(interval_type, interval_value)
years = _interval_to_years(return_period_unit, return_period_value)
returns = returns ** (1 / years)
return (current[0] if return_actual_date else as_on), returns - 1
@ -293,8 +293,8 @@ class TimeSeries(TimeSeriesCore):
closest: Literal["previous", "next", "exact"] = "previous",
if_not_found: Literal["fail", "nan"] = "fail",
annual_compounded_returns: bool = True,
interval_type: Literal["years", "months", "days"] = "years",
interval_value: int = 1,
return_period_unit: Literal["years", "months", "days"] = "years",
return_period_value: int = 1,
date_format: str = None,
) -> TimeSeries:
"""Calculate the returns on a rolling basis.
@ -339,10 +339,10 @@ class TimeSeries(TimeSeriesCore):
compounding : bool, optional
Should the returns be compounded annually.
interval_type : years | month | days
return_period_unit : years | month | days
The interval for the return calculation.
interval_value : int, optional
return_period_value : int, optional
The value of the interval for return calculation.
date_format : str, optional
@ -380,8 +380,8 @@ class TimeSeries(TimeSeriesCore):
returns = self.calculate_returns(
as_on=i,
annual_compounded_returns=annual_compounded_returns,
interval_type=interval_type,
interval_value=interval_value,
return_period_unit=return_period_unit,
return_period_value=return_period_value,
as_on_match=as_on_match,
prior_match=prior_match,
closest=closest,
@ -396,22 +396,41 @@ class TimeSeries(TimeSeriesCore):
self,
from_date: Union[datetime.date, str] = None,
to_date: Union[datetime.date, str] = None,
annualize_volatility: bool = True,
traded_days: int = None,
frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
return_period_unit: Literal["years", "months", "days"] = "days",
return_period_value: int = 1,
as_on_match: str = "closest",
prior_match: str = "closest",
closest: Literal["previous", "next", "exact"] = "previous",
if_not_found: Literal["fail", "nan"] = "fail",
annual_compounded_returns: bool = None,
interval_type: Literal["years", "months", "days"] = "days",
interval_value: int = 1,
date_format: str = None,
annualize_volatility: bool = True,
traded_days: int = None,
):
"""Calculates the volatility of the time series.add()
The volatility is calculated as the standard deviaion of periodic returns.
The periodicity of returns is based on the periodicity of underlying data.
Parameters:
----------
from_date: datetime.datetime | str, optional
Starting date for the volatility calculation.
Default is the first date on which volatility can be calculated based on the interval type.
to_date: datetime.datetime | str, optional
Ending date for the volatility calculation.
Default is the last date in the TimeSeries.
annualize_volatility: bool, default True
Whether the volatility number should be annualized.
Multiplies the standard deviation with the square root of the number of periods in a year
traded_days: bool, optional
Number of traded days per year to be considered for annualizing volatility.
Only used when annualizing volatility for a time series with daily frequency.
If not provided, will use the value in FincalOptions.traded_days.
"""
if frequency is None:
@ -423,7 +442,7 @@ class TimeSeries(TimeSeriesCore):
raise ValueError(f"Invalid argument for frequency {frequency}")
if from_date is None:
from_date = self.start_date + relativedelta(**{interval_type: interval_value})
from_date = self.start_date + relativedelta(**{return_period_unit: return_period_value})
if to_date is None:
to_date = self.end_date
@ -439,17 +458,17 @@ class TimeSeries(TimeSeriesCore):
closest=closest,
if_not_found=if_not_found,
annual_compounded_returns=annual_compounded_returns,
interval_type=interval_type,
interval_value=interval_value,
return_period_unit=return_period_unit,
return_period_value=return_period_value,
)
sd = statistics.stdev(rolling_returns.values)
if annualize_volatility:
if traded_days is None:
traded_days = FincalOptions.traded_days
if interval_type == "months":
if return_period_unit == "months":
sd *= math.sqrt(12)
elif interval_type == "days":
elif return_period_unit == "days":
sd *= math.sqrt(traded_days)
return sd