README.md

@@ -29,13 +29,12 @@ Fincal aims to simplify things by allowing you to:
 - [x] Sync two TimeSeries
 - [x] Average rolling return
 - [x] Sharpe ratio
-- [x] Jensen's Alpha
-- [x] Beta
+- [ ] Jensen's Alpha
+- [ ] Beta
 - [ ] Sortino ratio
 - [ ] Correlation & R-squared
 - [ ] Treynor ratio
 - [x] Max drawdown
-- [ ] Moving average
 
 ### Pending implementation
 - [x] Use limit parameter in ffill and bfill

fincal/statistics.py

@@ -22,71 +22,7 @@ def sharpe_ratio(
     prior_match: str = "closest",
     closest: Literal["previous", "next"] = "previous",
     date_format: str = None,
-) -> float:
-    """Calculate the Sharpe ratio of any time series
-
-    Sharpe ratio is a measure of returns per unit of risk,
-    where risk is measured by the standard deviation of the returns.
-
-    The formula for Sharpe ratio is:
-        (average asset return - risk free rate)/volatility of asset returns
-
-    Parameters
-    ----------
-    time_series_data:
-        The time series for which Sharpe ratio needs to be calculated
-
-    risk_free_data:
-        Risk free rates as time series data.
-        This should be the time series of risk free returns,
-        and not the underlying asset value.
-
-    risk_free_rate:
-        Risk free rate to be used.
-        Either risk_free_data or risk_free_rate needs to be provided.
-        If both are provided, the time series data will be used.
-
-    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
-    -------
-        Value of Sharpe ratio as a float.
-
-    Raises
-    ------
-    ValueError
-        If risk free data or risk free rate is not provided.
-    """
-
+):
     interval_days = int(_interval_to_years(return_period_unit, return_period_value) * 365 + 1)
 
     if from_date is None:
@@ -135,58 +71,9 @@ def beta(
     prior_match: str = "closest",
     closest: Literal["previous", "next"] = "previous",
     date_format: str = None,
-) -> float:
-    """Beta is a measure of sensitivity of asset returns to market returns
+):
 
-    The formula for beta is:
-
-    Parameters
-    ----------
-    asset_data : TimeSeries
-        The time series data of the asset
-
-    market_data : TimeSeries
-        The time series data of the relevant market index
-
-    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.
-    """
-    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
+    interval_days = int(_interval_to_years(return_period_unit, return_period_value) * 365 + 1)
 
     if from_date is None:
         from_date = asset_data.start_date + datetime.timedelta(days=interval_days)
@@ -203,7 +90,6 @@ def beta(
         "prior_match": prior_match,
         "closest": closest,
         "date_format": date_format,
-        "annual_compounded_returns": annual_compounded_returns,
     }
 
     asset_rr = asset_data.calculate_rolling_returns(**common_params)
@@ -214,135 +100,3 @@ def beta(
 
     beta = cov / market_var
     return beta
-
-
-def jensens_alpha(
-    asset_data: TimeSeries,
-    market_data: TimeSeries,
-    risk_free_data: TimeSeries = None,
-    risk_free_rate: float = None,
-    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:
-    """
-    This function calculates the Jensen's alpha for a time series.
-    The formula for Jensen's alpha is:
-        Ri - Rf + B x (Rm - Rf)
-    where:
-        Ri = Realized return of the portfolio or investment
-        Rf = The risk free rate during the return time frame
-        B = Beta of the portfolio or investment
-        Rm = Realized return of the market index
-
-    Parameters
-    ----------
-    asset_data : TimeSeries
-        The time series data of the asset
-
-    market_data : TimeSeries
-        The time series data of the relevant market index
-
-    risk_free_data:
-        Risk free rates as time series data.
-        This should be the time series of risk free returns,
-        and not the underlying asset value.
-
-    risk_free_rate:
-        Risk free rate to be used.
-        Either risk_free_data or risk_free_rate needs to be provided.
-        If both are provided, the time series data will be used.
-
-    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 Jensen's alpha as a float.
-    """
-
-    interval_years = _interval_to_years(return_period_unit, return_period_value)
-    interval_days = int(interval_years * 365 + 1)
-
-    if from_date is None:
-        from_date = asset_data.start_date + datetime.timedelta(days=interval_days)
-    if to_date is None:
-        to_date = asset_data.end_date
-
-    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,
-    }
-
-    num_days = (to_date - from_date).days
-    compound_realised_returns = True if num_days > 365 else False
-    realized_return = asset_data.calculate_returns(
-        as_on=to_date,
-        return_period_unit="days",
-        return_period_value=num_days,
-        annual_compounded_returns=compound_realised_returns,
-        as_on_match=as_on_match,
-        prior_match=prior_match,
-        closest=closest,
-        date_format=date_format,
-    )
-    market_return = market_data.calculate_returns(
-        as_on=to_date,
-        return_period_unit="days",
-        return_period_value=num_days,
-        annual_compounded_returns=compound_realised_returns,
-        as_on_match=as_on_match,
-        prior_match=prior_match,
-        closest=closest,
-        date_format=date_format,
-    )
-    beta_value = beta(asset_data=asset_data, market_data=market_data, **common_params)
-
-    if risk_free_data is None and risk_free_rate is None:
-        raise ValueError("At least one of risk_free_data or risk_free rate is required")
-    elif risk_free_data is not None:
-        risk_free_rate = risk_free_data.mean()
-
-    jensens_alpha = realized_return[1] - risk_free_rate + beta_value * (market_return[1] - risk_free_rate)
-    return jensens_alpha