From 583ca98e515809d6d4b005f7ab5795568da2caee Mon Sep 17 00:00:00 2001 From: Gourav Kumar Date: Sun, 24 Jul 2022 08:46:45 +0530 Subject: [PATCH] type hints and docs --- pyfacts/pyfacts.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/pyfacts/pyfacts.py b/pyfacts/pyfacts.py index 2a8e3af..2177067 100644 --- a/pyfacts/pyfacts.py +++ b/pyfacts/pyfacts.py @@ -262,7 +262,7 @@ class TimeSeries(TimeSeriesCore): return_period_unit: Literal["years", "months", "days"] = "years", return_period_value: int = 1, date_format: str = None, - ) -> float: + ) -> Tuple[datetime.datetime, float]: """Method to calculate returns for a certain time-period as on a particular date Parameters @@ -295,7 +295,7 @@ class TimeSeries(TimeSeriesCore): * fail: Raise a ValueError * nan: Return nan as the value - compounding : bool, optional + annual_compounded_returns : bool, optional Whether the return should be compounded annually. return_period_unit : 'years', 'months', 'days' @@ -321,14 +321,14 @@ class TimeSeries(TimeSeriesCore): Example -------- - >>> calculate_returns(datetime.date(2020, 1, 1), years=1) + >>> ts.calculate_returns(datetime.date(2020, 1, 1), years=1) (datetime.datetime(2020, 1, 1, 0, 0), .0567) """ as_on_delta, prior_delta = _preprocess_match_options(as_on_match, prior_match, closest) - 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) + prev_date = as_on - relativedelta(**{return_period_unit: return_period_value}) if current[1] != str("nan"): previous = _find_closest_date(self.data, prev_date, closest_max_days, prior_delta, if_not_found) @@ -368,16 +368,16 @@ class TimeSeries(TimeSeriesCore): End date for the returns calculation. frequency : str, optional - Frequency at which the returns should be calcualated. + Frequency at which the returns should be calculated. Valid values are {D, W, M, Q, H, Y} as_on_match : str, optional The match mode to be used for the as on date. - If not specified, the value for the closes parameter will be used. + If not specified, the value for the closest parameter will be used. prior_match : str, optional The match mode to be used for the prior date, i.e., the date against which the return will be calculated. - If not specified, the value for the closes parameter will be used. + If not specified, the value for the closest parameter will be used. closest : previous | next | exact The default match mode for dates. @@ -395,7 +395,7 @@ class TimeSeries(TimeSeriesCore): For instance, if the input date is before the starting of the first date of the time series, but match mode is set to previous. A DateOutOfRangeError will be raised in such cases. - compounding : bool, optional + annual_compounded_returns : bool, optional Should the returns be compounded annually. return_period_unit : years | month | days @@ -410,7 +410,7 @@ class TimeSeries(TimeSeriesCore): Returns ------- - Returs the rolling returns as a TimeSeries object. + Returns the rolling returns as a TimeSeries object. Raises ------ @@ -431,7 +431,7 @@ class TimeSeries(TimeSeriesCore): raise ValueError(f"Invalid argument for frequency {frequency}") if from_date is None: from_date = self.start_date + relativedelta( - days=int(_interval_to_years(return_period_unit, return_period_value) * 365 + 1) + days=math.ceil(_interval_to_years(return_period_unit, return_period_value) * 365) ) if to_date is None: @@ -476,7 +476,7 @@ class TimeSeries(TimeSeriesCore): ) -> float: """Calculates the volatility of the time series.add() - The volatility is calculated as the standard deviaion of periodic returns. + The volatility is calculated as the standard deviation of periodic returns. The periodicity of returns is based on the periodicity of underlying data. Parameters: @@ -761,10 +761,10 @@ class TimeSeries(TimeSeriesCore): Parameters: ----------- other: TimeSeries - Another object of TimeSeries class whose dates need to be syncronized + Another object of TimeSeries class whose dates need to be synchronized fill_method: ffill | bfill, default ffill - Method to use to fill missing values in time series when syncronizing + Method to use to fill missing values in time series when synchronizing Returns: -------- @@ -903,7 +903,7 @@ def read_csv( header = data[read_start_row] print(header) # fmt: off - # Black and pylance disagree on the foratting of the following line, hence formatting is disabled + # Black and pylance disagree on the formatting of the following line, hence formatting is disabled data = data[(read_start_row + 1):read_end_row] # fmt: on