feat: add Sharpe ratio to Portfolio and helpers

main.py

@@ -1,3 +1,5 @@
 import okama as ok
 
-print(ok.search('aeroflot', namespace='MOEX', response_format='frame'))
+x = ok.Portfolio(['SPY.US', 'BND.US'], weights=[0.5, 0.5], ccy='RUB')
+
+print(x.get_sharpe_ratio(rf_return=0.07))

okama/__init__.py

@@ -36,7 +36,7 @@
     macro_namespaces,
     symbols_in_namespace,
 )
-from okama.common.helpers import Float, Frame, Rebalance, Date
+from okama.common.helpers.helpers import Float, Frame, Rebalance, Date
 import okama.settings
 
 __version__ = "1.0.2"

okama/asset.py

@@ -3,7 +3,7 @@
 import pandas as pd
 import numpy as np
 
-from .common.helpers import Frame
+from .common.helpers.helpers import Frame
 from .settings import default_ticker
 from .api.data_queries import QueryData
 from .api.namespaces import get_assets_namespaces

okama/asset_list.py

@@ -3,7 +3,7 @@
 import numpy as np
 import pandas as pd
 
-from .common.helpers import Frame, Float, Date, Index
+from .common.helpers.helpers import Frame, Float, Date, Index
 from .common.make_asset_list import ListMaker
 
 

okama/common/helpers.py → okama/common/helpers/helpers.py

@@ -5,7 +5,7 @@
 import numpy as np
 import scipy.stats
 
-from ..settings import _MONTHS_PER_YEAR
+from okama.settings import _MONTHS_PER_YEAR
 
 
 def check_rolling_window(
@@ -580,7 +580,7 @@ def tracking_difference(accumulated_return: pd.DataFrame) -> pd.DataFrame:
     @staticmethod
     def tracking_difference_annualized(tracking_diff: pd.DataFrame) -> pd.DataFrame:
         """
-        Annualizes the values of tracking difference time series.
+        Annualize the values of tracking difference time series.
         Annual values are available for periods of more than 12 months.
         Returns for less than 12 months can't be annualized.
         """

okama/common/helpers/rates.py

@@ -0,0 +1,8 @@
+"""Portfolio rates"""
+
+
+def get_sharpe_ratio(pf_return: float, rf_return: float, std_deviation: float) -> float:
+    """
+    Calculate Sharpe ratio.
+    """
+    return (pf_return - rf_return) / std_deviation

okama/frontier/multi_period.py

@@ -7,7 +7,7 @@
 from scipy.optimize import minimize
 
 from .. import AssetList
-from ..common.helpers import Float, Frame, Rebalance
+from ..common.helpers.helpers import Float, Frame, Rebalance
 from ..settings import _MONTHS_PER_YEAR
 
 

okama/frontier/single_period.py

@@ -1,12 +1,12 @@
-from typing import Optional, Tuple, Dict, List, Union
+from typing import Optional, Tuple, Dict, List
 
 import pandas as pd
 import numpy as np
 
 from scipy.optimize import minimize
 
 from .. import AssetList
-from ..common.helpers import Float, Frame
+from ..common.helpers.helpers import Float, Frame
 
 
 class EfficientFrontier(AssetList):

okama/macro.py

@@ -6,7 +6,7 @@
 
 from .api.data_queries import QueryData
 from .api.namespaces import get_macro_namespaces
-from .common.helpers import Float, Frame, Date
+from .common.helpers.helpers import Float, Frame, Date
 from .settings import default_macro, PeriodLength, _MONTHS_PER_YEAR
 
 

okama/plots.py

@@ -4,7 +4,7 @@
 from matplotlib import pyplot as plt
 
 from .asset_list import AssetList
-from .common.helpers import Float
+from .common.helpers.helpers import Float
 from .frontier.single_period import EfficientFrontier
 from .settings import default_ticker
 

okama/portfolio.py

@@ -6,7 +6,8 @@
 import scipy.stats
 from matplotlib import pyplot as plt
 
-from .common.helpers import Frame, Rebalance, Float, Date
+from .common.helpers.helpers import Frame, Rebalance, Float, Date
+from .common.helpers import rates
 from .common.make_asset_list import ListMaker
 from .common.validators import validate_real
 from .settings import _MONTHS_PER_YEAR
@@ -1867,6 +1868,33 @@ def kstest(self, distr: str = "norm") -> Dict[str, float]:
         """
         return Frame.kstest_series(self.ror, distr=distr)
 
+    def get_sharpe_ratio(self, rf_return: float = 0) -> float:
+        """
+        Calculate Sharpe ratio.
+
+        The Sharpe ratio is the average annual return in excess of the risk-free rate
+        per unit of risk (annualized standard deviation).
+
+        Parameters
+        ----------
+        rf_return : float, default 0
+            Risk-free rate of return.
+
+        Returns
+        -------
+        float
+
+        Examples
+        --------
+        >>> pf = ok.Portfolio(['VOO.US', 'BND.US'], weights=[0.40, 0.60])
+        >>> pf.get_sharpe_ratio(rf_return=0.04)
+        0.7412193684695373
+        """
+        return rates.get_sharpe_ratio(
+            pf_return=self.mean_return_annual,
+            rf_return=rf_return,
+            std_deviation=self.risk_annual)
+
     def plot_percentiles_fit(
         self, distr: str = "norm", figsize: Optional[tuple] = None
     ) -> None:

tests/test_portfolio.py

@@ -313,6 +313,10 @@ def test_jarque_bera(portfolio_rebalanced_month):
     )
 
 
+def test_get_sharpe_ratio(portfolio_no_inflation):
+    assert portfolio_no_inflation.get_sharpe_ratio(rf_return=0.05) == approx(0.631, rel=1e-2)
+
+
 # This test should be a last one, as it changes the weights
 def test_init_portfolio_failing():
     with pytest.raises(