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