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option to return nan in case of failure in return_calc

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return calc returns tuple
This commit is contained in:
Gourav Kumar 2022-02-25 10:38:20 +05:30
parent 9e4049a973
commit 870d0bd96b
1 changed files with 39 additions and 7 deletions
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46
fincal/fincal.py
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@ -122,9 +122,11 @@ class TimeSeries(TimeSeriesCore):
def calculate_returns(
self,
as_on: Union[str, datetime.datetime],
return_actual_date: bool = True,
as_on_match: str = "closest",
prior_match: str = "closest",
closest: str = "previous",
if_not_found: Literal['fail', 'nan'] = 'fail',
compounding: bool = True,
interval_type: Literal['years', 'months', 'days'] = 'years',
interval_value: int = 1,
@ -137,6 +139,10 @@ class TimeSeries(TimeSeriesCore):
as_on : datetime.datetime
The date as on which the return is to be calculated.
return_actual_date : bool, default True
If true, the output will contain the actual date based on which the return was calculated.
Set to False to return the date passed in the as_on argument.
as_on_match : str, optional
The mode of matching the as_on_date. Refer closest.
@ -147,15 +153,28 @@ class TimeSeries(TimeSeriesCore):
The mode of matching the closest date.
Valid values are 'exact', 'previous', 'next' and next.
if_not_found : 'fail' | 'nan'
What to do when required date is not found:
* fail: Raise a ValueError
* nan: Return nan as the value
compounding : bool, optional
Whether the return should be compounded annually.
years : int, optional
number of years for which the returns should be calculated
interval_type : 'years', 'months', 'days'
The type of time period to use for return calculation.
interval_value : int
The value of the specified interval type over which returns needs to be calculated.
date_format: str
The date format to use for this operation.
Should be passed as a datetime library compatible string.
Sets the date format only for this operation. To set it globally, use FincalOptions.date_format
Returns
-------
The float value of the returns.
A tuple containing the date and float value of the returns.
Raises
------
@ -170,13 +189,19 @@ class TimeSeries(TimeSeriesCore):
as_on = _parse_date(as_on, date_format)
as_on_delta, prior_delta = _preprocess_match_options(as_on_match, prior_match, closest)
original_as_on = as_on
while True:
current = self.data.get(as_on, None)
if current is not None:
break
elif not as_on_delta:
raise ValueError("As on date not found")
if if_not_found == 'fail':
raise ValueError(f"As on date {original_as_on} not found")
elif if_not_found == 'nan':
return as_on, float("NaN")
else:
raise ValueError(f"Invalid argument for if_not_found: {if_not_found}")
as_on += as_on_delta
prev_date = as_on - relativedelta(**{interval_type: interval_value})
@ -185,14 +210,19 @@ class TimeSeries(TimeSeriesCore):
if previous is not None:
break
elif not prior_delta:
raise ValueError("Previous date not found")
if if_not_found == 'fail':
raise ValueError(f"Previous date {previous} not found")
elif if_not_found == 'nan':
return (as_on if return_actual_date else original_as_on), float("NaN")
else:
raise ValueError(f"Invalid argument for if_not_found: {if_not_found}")
prev_date += prior_delta
returns = current / previous
if compounding:
years = _interval_to_years(interval_type, interval_value)
returns = returns ** (1 / years)
return returns - 1
return (as_on if return_actual_date else original_as_on), returns - 1
def calculate_rolling_returns(
self,
@ -202,6 +232,7 @@ class TimeSeries(TimeSeriesCore):
as_on_match: str = "closest",
prior_match: str = "closest",
closest: str = "previous",
if_not_found: Literal['fail', 'nan'] = 'fail',
compounding: bool = True,
interval_type: Literal['years', 'months', 'days'] = 'years',
interval_value: int = 1,
@ -234,8 +265,9 @@ class TimeSeries(TimeSeriesCore):
as_on_match=as_on_match,
prior_match=prior_match,
closest=closest,
if_not_found=if_not_found
)
rolling_returns.append((i, returns))
rolling_returns.append(returns)
rolling_returns.sort()
return self.__class__(rolling_returns, self.frequency.symbol)