fincal/core.py

@@ -882,49 +882,3 @@ class TimeSeriesCore:
     def update(self, items: dict):
         for k, v in items.items():
             self[k] = v
-
-    def to_dict(self, date_as_string: bool = False, string_date_format: str = "default") -> dict:
-        """Convert time series to a dictionary
-
-        Parameters
-        ----------
-        date_as_string: boolean, default False
-            Whether date should be converted to string.
-            If False, then the output will contain datetime.datetime objects
-
-        string_date_format: datetime library compatible format string
-            If date is to be output as string, pass the format here.
-            If it is left as default, the format set in fincal_options will be used.
-        """
-
-        if not date_as_string:
-            return self.data
-
-        if string_date_format == "default":
-            string_date_format = FincalOptions.date_format
-
-        data = {datetime.datetime.strftime(dt, string_date_format): val for dt, val in self.data.items()}
-        return data
-
-    def to_list(self, date_as_string: bool = False, string_date_format: str = "default") -> List[tuple]:
-        """Convert time series to a list of tuples
-
-        Parameters
-        ----------
-        date_as_string: boolean, optional
-            Whether date should be converted to string.
-            If False, then the output will contain datetime.datetime objects
-
-        string_date_format : str, optional
-            If date is to be output as string, pass the format here.
-            If it is left as default, the format set in fincal_options will be used.
-        """
-
-        if not date_as_string:
-            return list(self.data.items())
-
-        if string_date_format == "default":
-            string_date_format = FincalOptions.date_format
-
-        data = [(datetime.datetime.strftime(dt, string_date_format), val) for dt, val in self.data.items()]
-        return data

fincal/statistics.py

@@ -57,19 +57,19 @@ def sharpe_ratio(
     frequency:
         The frequency at which returns should be calculated.
 
-    return_period_unit: 'years', 'months', 'days'
+    return_period_unit : 'years', 'months', 'days'
         The type of time period to use for return calculation.
 
-    return_period_value: int
+    return_period_value : int
         The value of the specified interval type over which returns needs to be calculated.
 
-    as_on_match: str, optional
+    as_on_match : str, optional
             The mode of matching the as_on_date. Refer closest.
 
-    prior_match: str, optional
+    prior_match : str, optional
         The mode of matching the prior_date. Refer closest.
 
-    closest: str, optional
+    closest : str, optional
         The mode of matching the closest date.
         Valid values are 'exact', 'previous', 'next' and next.
 
@@ -142,10 +142,10 @@ def beta(
 
     Parameters
     ----------
-    asset_data: TimeSeries
+    asset_data : TimeSeries
         The time series data of the asset
 
-    market_data: TimeSeries
+    market_data : TimeSeries
         The time series data of the relevant market index
 
     from_date:
@@ -159,19 +159,19 @@ def beta(
     frequency:
         The frequency at which returns should be calculated.
 
-    return_period_unit: 'years', 'months', 'days'
+    return_period_unit : 'years', 'months', 'days'
         The type of time period to use for return calculation.
 
-    return_period_value: int
+    return_period_value : int
         The value of the specified interval type over which returns needs to be calculated.
 
-    as_on_match: str, optional
+    as_on_match : str, optional
             The mode of matching the as_on_date. Refer closest.
 
-    prior_match: str, optional
+    prior_match : str, optional
         The mode of matching the prior_date. Refer closest.
 
-    closest: str, optional
+    closest : str, optional
         The mode of matching the closest date.
         Valid values are 'exact', 'previous', 'next' and next.
 
@@ -216,7 +216,6 @@ def beta(
     return beta
 
 
-@date_parser(4, 5)
 def jensens_alpha(
     asset_data: TimeSeries,
     market_data: TimeSeries,
@@ -244,10 +243,10 @@ def jensens_alpha(
 
     Parameters
     ----------
-    asset_data: TimeSeries
+    asset_data : TimeSeries
         The time series data of the asset
 
-    market_data: TimeSeries
+    market_data : TimeSeries
         The time series data of the relevant market index
 
     risk_free_data:
@@ -271,19 +270,19 @@ def jensens_alpha(
     frequency:
         The frequency at which returns should be calculated.
 
-    return_period_unit: 'years', 'months', 'days'
+    return_period_unit : 'years', 'months', 'days'
         The type of time period to use for return calculation.
 
-    return_period_value: int
+    return_period_value : int
         The value of the specified interval type over which returns needs to be calculated.
 
-    as_on_match: str, optional
+    as_on_match : str, optional
             The mode of matching the as_on_date. Refer closest.
 
-    prior_match: str, optional
+    prior_match : str, optional
         The mode of matching the prior_date. Refer closest.
 
-    closest: str, optional
+    closest : str, optional
         The mode of matching the closest date.
         Valid values are 'exact', 'previous', 'next' and next.
 
@@ -347,109 +346,3 @@ def jensens_alpha(
 
     jensens_alpha = realized_return[1] - risk_free_rate + beta_value * (market_return[1] - risk_free_rate)
     return jensens_alpha
-
-
-@date_parser(2, 3)
-def correlation(
-    data1: TimeSeries,
-    data2: TimeSeries,
-    from_date: str | datetime.datetime = None,
-    to_date: str | datetime.datetime = None,
-    frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
-    return_period_unit: Literal["years", "months", "days"] = "years",
-    return_period_value: int = 1,
-    as_on_match: str = "closest",
-    prior_match: str = "closest",
-    closest: Literal["previous", "next"] = "previous",
-    date_format: str = None,
-) -> float:
-    """Calculate the correlation between two assets
-
-    correlation calculation is done based on rolling returns.
-    It must be noted that correlation is not calculated directly on the asset prices.
-    The asset prices used to calculate returns and correlation is then calculated based on these returns.
-    Hence this function requires all parameters for rolling returns calculations.
-
-    Parameters
-    ----------
-    data1: TimeSeries
-        The first time series data
-
-    data2: TimeSeries
-        The second time series data
-
-    from_date:
-        Start date from which returns should be calculated.
-        Defaults to the first date of the series.
-
-    to_date:
-        End date till which returns should be calculated.
-        Defaults to the last date of the series.
-
-    frequency:
-        The frequency at which returns should be calculated.
-
-    return_period_unit: 'years', 'months', 'days'
-        The type of time period to use for return calculation.
-
-    return_period_value: int
-        The value of the specified interval type over which returns needs to be calculated.
-
-    as_on_match: str, optional
-            The mode of matching the as_on_date. Refer closest.
-
-    prior_match: str, optional
-        The mode of matching the prior_date. Refer closest.
-
-    closest: str, optional
-        The mode of matching the closest date.
-        Valid values are 'exact', 'previous', 'next' and next.
-
-    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 value of beta as a float.
-
-    Raises
-    ------
-    ValueError:
-        * If frequency of both TimeSeries do not match
-        * If both time series do not have data between the from date and to date
-    """
-    interval_years = _interval_to_years(return_period_unit, return_period_value)
-    interval_days = int(interval_years * 365 + 1)
-
-    annual_compounded_returns = True if interval_years > 1 else False
-
-    if from_date is None:
-        from_date = data1.start_date + datetime.timedelta(days=interval_days)
-    if to_date is None:
-        to_date = data1.end_date
-
-    if data1.frequency != data2.frequency:
-        raise ValueError("Correlation calculation requires both time series to be of same frequency")
-
-    if from_date < data2.start_date or to_date > data2.end_date:
-        raise ValueError("Data between from_date and to_date must be present in both time series")
-
-    common_params = {
-        "from_date": from_date,
-        "to_date": to_date,
-        "frequency": frequency,
-        "return_period_unit": return_period_unit,
-        "return_period_value": return_period_value,
-        "as_on_match": as_on_match,
-        "prior_match": prior_match,
-        "closest": closest,
-        "date_format": date_format,
-        "annual_compounded_returns": annual_compounded_returns,
-    }
-
-    asset_rr = data1.calculate_rolling_returns(**common_params)
-    market_rr = data2.calculate_rolling_returns(**common_params)
-
-    cor = statistics.correlation(asset_rr.values, market_rr.values)
-    return cor