chore: black style

Author: chilango74

main.py

@@ -1,18 +1,20 @@
 import matplotlib.pyplot as plt
 
 import okama as ok
+
 # Portfolio WithDrawls
-weights = [0.32, 0.31,  0.18, .19]
-portf = ok.Portfolio(['RGBITR.INDX', 'RUCBTRNS.INDX', 'MCFTR.INDX', 'GC.COMM'],
-                     ccy="RUB",
-                     weights=weights,
-                     inflation=True,
-                     symbol="retirement_portf.PF",
-                     rebalancing_period='year',
-                     cashflow=-200_000,
-                     initial_amount=39_000_000,
-                     discount_rate=None
-                     )
+weights = [0.32, 0.31, 0.18, 0.19]
+portf = ok.Portfolio(
+    ["RGBITR.INDX", "RUCBTRNS.INDX", "MCFTR.INDX", "GC.COMM"],
+    ccy="RUB",
+    weights=weights,
+    inflation=True,
+    symbol="retirement_portf.PF",
+    rebalancing_period="year",
+    cashflow=-200_000,
+    initial_amount=39_000_000,
+    discount_rate=None,
+)
 
 print(portf.discount_rate)
 print(portf)
@@ -33,10 +35,7 @@
 
 # Rolling / Expanding Risk
 
-al = ok.AssetList(['DJI.INDX',
-                   'BND.US'
-                   ])
-al.get_rolling_risk_annual(window=12*20).plot()
+al = ok.AssetList(["DJI.INDX", "BND.US"])
+al.get_rolling_risk_annual(window=12 * 20).plot()
 
 plt.show()
-

main_notebook.ipynb

@@ -44,8 +44,7 @@ def annualize_return(
 
     @staticmethod
     def annualize_risk(
-            risk: Union[float, pd.Series, pd.DataFrame],
-            mean_return: Union[float, pd.Series, pd.DataFrame]
+        risk: Union[float, pd.Series, pd.DataFrame], mean_return: Union[float, pd.Series, pd.DataFrame]
     ) -> Union[float, pd.Series, pd.DataFrame]:
         """
         Annualizes Risk.
@@ -168,11 +167,10 @@ def get_annual_return_ts_from_monthly(
         if isinstance(ts, pd.Series):
             ts.rename(ror_monthly.name, inplace=True)
         return ts
-    
+
     @staticmethod
     def get_wealth_indexes(
-        ror: Union[pd.Series, pd.DataFrame], 
-        initial_amount: float = 1000.
+        ror: Union[pd.Series, pd.DataFrame], initial_amount: float = 1000.0
     ) -> Union[pd.Series, pd.DataFrame]:
         """
         Returns wealth indexes for a list of assets (or for portfolio).
@@ -189,11 +187,11 @@ def get_wealth_indexes(
 
     @staticmethod
     def get_wealth_indexes_with_cashflow(
-        ror: Union[pd.Series, pd.DataFrame], 
+        ror: Union[pd.Series, pd.DataFrame],
         portfolio_symbol: Optional[str],
         inflation_symbol: Optional[str],
         discount_rate: float,
-        initial_amount: float = 1000.,
+        initial_amount: float = 1000.0,
         cashflow: float = 0,
     ) -> Union[pd.Series, pd.DataFrame]:
         """
@@ -222,8 +220,7 @@ def get_wealth_indexes_with_cashflow(
             s.iloc[0] = initial_amount  # replaces NaN with the first period return
             if inflation_symbol:
                 cum_inflation = Frame.get_wealth_indexes(
-                    ror=ror.loc[:, inflation_symbol],
-                    initial_amount=initial_amount
+                    ror=ror.loc[:, inflation_symbol], initial_amount=initial_amount
                 )
                 wealth_index = pd.concat([s, cum_inflation], axis="columns")
             else:
@@ -336,7 +333,7 @@ def skewness(ror: Union[pd.DataFrame, pd.Series]) -> Union[pd.Series, float]:
         The shape of time series should be at least 12. In the opposite case empty time series is returned.
         """
         sk = ror.expanding(min_periods=1).skew()
-        return sk.iloc[settings._MONTHS_PER_YEAR:]
+        return sk.iloc[settings._MONTHS_PER_YEAR :]
 
     @staticmethod
     def skewness_rolling(ror: Union[pd.DataFrame, pd.Series], window: int = 60) -> Union[pd.Series, float]:
@@ -356,7 +353,7 @@ def kurtosis(ror: Union[pd.Series, pd.DataFrame]):
         Kurtosis should be close to zero for normal distribution.
         """
         kt = ror.expanding(min_periods=1).kurt()
-        return kt.iloc[settings._MONTHS_PER_YEAR:]
+        return kt.iloc[settings._MONTHS_PER_YEAR :]
 
     @staticmethod
     def kurtosis_rolling(ror: Union[pd.Series, pd.DataFrame], window: int = 60):
@@ -566,7 +563,7 @@ def tracking_difference_annualized(tracking_diff: pd.DataFrame) -> pd.DataFrame:
         y = abs(tracking_diff)
         diff = (y + 1.0).pow(pwr, axis=0) - 1.0
         diff = np.sign(tracking_diff) * diff
-        return diff.iloc[settings._MONTHS_PER_YEAR - 1:]  # returns for the first 11 months can't be annualized
+        return diff.iloc[settings._MONTHS_PER_YEAR - 1 :]  # returns for the first 11 months can't be annualized
 
     @staticmethod
     def tracking_error(ror: pd.DataFrame) -> pd.DataFrame:
@@ -597,7 +594,7 @@ def expanding_cov_cor(ror: pd.DataFrame, fn: str) -> pd.DataFrame:
         cov_matrix_ts = getattr(ror.expanding(), fn)()
         cov_matrix_ts = cov_matrix_ts.drop(index=ror.columns[1:], level=1).droplevel(1)
         cov_matrix_ts.drop(columns=ror.columns[0], inplace=True)
-        return cov_matrix_ts.iloc[settings._MONTHS_PER_YEAR:]
+        return cov_matrix_ts.iloc[settings._MONTHS_PER_YEAR :]
 
     @staticmethod
     def rolling_cov_cor(ror: pd.DataFrame, window: int = 60, fn: str = "corr") -> pd.DataFrame:
@@ -629,7 +626,7 @@ def beta(ror: pd.DataFrame) -> pd.DataFrame:
             raise ValueError("Beta coefficient is not defined for time periods < 1 year")
         cov = Index.expanding_cov_cor(ror, fn="cov")
         benchmark_var = ror.loc[:, ror.columns[0]].expanding().var()
-        benchmark_var = benchmark_var.iloc[settings._MONTHS_PER_YEAR:]
+        benchmark_var = benchmark_var.iloc[settings._MONTHS_PER_YEAR :]
         return cov.divide(benchmark_var, axis=0)
 
     @staticmethod

okama/common/helpers/helpers.py

@@ -67,7 +67,7 @@ def __init__(
         ).values()
         if first_date:
             self.first_date = max(self.first_date, pd.to_datetime(first_date))
-        self.assets_ror = self.assets_ror[self.first_date:]
+        self.assets_ror = self.assets_ror[self.first_date :]
         if last_date:
             self.last_date = min(self.last_date, pd.to_datetime(last_date))
         if inflation:
@@ -81,7 +81,7 @@ def __init__(
             # Add inflation to the date range dict
             self.assets_first_dates.update({self.inflation: macro.Inflation(self.inflation).first_date})
             self.assets_last_dates.update({self.inflation: macro.Inflation(self.inflation).last_date})
-        self.assets_ror: pd.DataFrame = self.assets_ror[self.first_date: self.last_date]
+        self.assets_ror: pd.DataFrame = self.assets_ror[self.first_date : self.last_date]
         self.period_length: float = round((self.last_date - self.first_date) / np.timedelta64(365, "D"), ndigits=1)
         self.pl = settings.PeriodLength(
             self.assets_ror.shape[0] // settings._MONTHS_PER_YEAR,
@@ -398,7 +398,7 @@ def plot_assets(
         kind: str = "mean",
         tickers: Union[str, list] = "tickers",
         pct_values: bool = False,
-        xy_text: tuple = (0, 10)
+        xy_text: tuple = (0, 10),
     ) -> plt.axes:
         """
         Plot the assets points on the risk-return chart with annotations.
@@ -428,7 +428,7 @@ def plot_assets(
             Percents (True)
 
         xy_text : tuple, default (0, 10)
-            The shift of the annotation text (x, y) from the point. 
+            The shift of the annotation text (x, y) from the point.
 
         Examples
         --------

okama/common/make_asset_list.py

@@ -332,7 +332,9 @@ def gmv_annual_values(self) -> Tuple[float, float]:
         >>> frontier.gmv_annual_values
         (0.03695845106087943, 0.04418318557516887)
         """
-        returns = helpers.Rebalance(period=self.rebalancing_period).return_ror_ts(self.gmv_annual_weights, self.assets_ror)
+        returns = helpers.Rebalance(period=self.rebalancing_period).return_ror_ts(
+            self.gmv_annual_weights, self.assets_ror
+        )
         return (
             helpers.Float.annualize_risk(returns.std(), returns.mean()),
             (returns + 1.0).prod() ** (settings._MONTHS_PER_YEAR / returns.shape[0]) - 1.0,

okama/frontier/multi_period.py

@@ -95,7 +95,7 @@ def __init__(
         inflation: bool = True,
         weights: Optional[List[float]] = None,
         rebalancing_period: str = "month",
-        initial_amount: float = 1000.,
+        initial_amount: float = 1000.0,
         cashflow: float = 0,
         discount_rate: Optional[float] = None,
         symbol: str = None,
@@ -2255,11 +2255,11 @@ def plot_forecast(
         return ax
 
     def plot_forecast_monte_carlo(
-            self,
-            distr: str = "norm",
-            years: int = 1,
-            n: int = 20,
-            figsize: Optional[tuple] = None,
+        self,
+        distr: str = "norm",
+        years: int = 1,
+        n: int = 20,
+        figsize: Optional[tuple] = None,
     ) -> None:
         """
         Plot Monte Carlo simulation for portfolio wealth indexes together with historical wealth index.
@@ -2422,7 +2422,7 @@ def cashflow_pv(self) -> Optional[float]:
         >>> pf.dcf.cashflow_pv
         -3004
         """
-        return self.parent.cashflow / (1. + self.parent.discount_rate) ** self.parent.period_length
+        return self.parent.cashflow / (1.0 + self.parent.discount_rate) ** self.parent.period_length
 
     @property
     def initial_amount_pv(self) -> Optional[float]:
@@ -2443,13 +2443,11 @@ def initial_amount_pv(self) -> Optional[float]:
         >>> pf.dcf.initial_amount_pv
         73650
         """
-        return self.parent.initial_amount / (1. + self.parent.discount_rate) ** self.parent.period_length
+        return self.parent.initial_amount / (1.0 + self.parent.discount_rate) ** self.parent.period_length
 
-    def _monte_carlo_wealth(self,
-                            first_value: float = 1000.,
-                            distr: str = "norm",
-                            years: int = 1,
-                            n: int = 100) -> pd.DataFrame:
+    def _monte_carlo_wealth(
+        self, first_value: float = 1000.0, distr: str = "norm", years: int = 1, n: int = 100
+    ) -> pd.DataFrame:
         """
         Generate portfolio wealth indexes with cash flows (withdrawals/contributions) by Monte Carlo simulation.
 
@@ -2498,7 +2496,7 @@ def _monte_carlo_wealth(self,
         df = return_ts.apply(
             helpers.Frame.get_wealth_indexes_with_cashflow,
             axis=0,
-            args=(None, None, self.parent.discount_rate, first_value, self.parent.cashflow)
+            args=(None, None, self.parent.discount_rate, first_value, self.parent.cashflow),
         )
 
         def remove_negative_values(s):
@@ -2581,10 +2579,10 @@ def plot_forecast_monte_carlo(
             s2.plot(legend=None)
 
     def monte_carlo_survival_period(
-            self,
-            distr: str = "norm",
-            years: int = 1,
-            n: int = 20,
+        self,
+        distr: str = "norm",
+        years: int = 1,
+        n: int = 20,
     ) -> pd.Series:
         """
         Generate a survival period distribution for a portfolio with cash flows by Monte Carlo simulation.
@@ -2631,12 +2629,7 @@ def monte_carlo_survival_period(
         >> s.quantile(50 / 100)
         2.7
         """
-        s2 = self._monte_carlo_wealth(
-            first_value=self.parent.initial_amount,
-            distr=distr,
-            years=years,
-            n=n
-        )
+        s2 = self._monte_carlo_wealth(first_value=self.parent.initial_amount, distr=distr, years=years, n=n)
         dates: pd.Series = helpers.Frame.get_survival_date(s2)
 
         return dates.apply(helpers.Date.get_period_length, args=(self.parent.last_date,))

okama/portfolio.py

@@ -167,8 +167,8 @@ def portfolio_cashflows_inflation(init_portfolio_values):
 @pytest.fixture(scope="package")
 def portfolio_cashflows_NO_inflation(init_portfolio_values):
     _portfolio_cashflows_NO_inflation = deepcopy(init_portfolio_values)
-    _portfolio_cashflows_NO_inflation["cashflow"] = -100.
-    _portfolio_cashflows_NO_inflation["initial_amount"] = 100_000.
+    _portfolio_cashflows_NO_inflation["cashflow"] = -100.0
+    _portfolio_cashflows_NO_inflation["initial_amount"] = 100_000.0
     _portfolio_cashflows_NO_inflation["inflation"] = False
     _portfolio_cashflows_NO_inflation["discount_rate"] = 0.09
     return ok.Portfolio(**_portfolio_cashflows_NO_inflation)
@@ -177,8 +177,8 @@ def portfolio_cashflows_NO_inflation(init_portfolio_values):
 @pytest.fixture(scope="package")
 def portfolio_cashflows_NO_inflation_NO_discount_rate(init_portfolio_values):
     _portfolio_cashflows_NO_inflation_NO_discount_rate = deepcopy(init_portfolio_values)
-    _portfolio_cashflows_NO_inflation_NO_discount_rate["cashflow"] = -100.
-    _portfolio_cashflows_NO_inflation_NO_discount_rate["initial_amount"] = 100_000.
+    _portfolio_cashflows_NO_inflation_NO_discount_rate["cashflow"] = -100.0
+    _portfolio_cashflows_NO_inflation_NO_discount_rate["initial_amount"] = 100_000.0
     _portfolio_cashflows_NO_inflation_NO_discount_rate["inflation"] = False
     _portfolio_cashflows_NO_inflation_NO_discount_rate["discount_rate"] = None
     return ok.Portfolio(**_portfolio_cashflows_NO_inflation_NO_discount_rate)
@@ -211,6 +211,7 @@ def portfolio_short_history(init_portfolio_values):
     _portfolio_short_history["first_date"] = "2019-02"
     return ok.Portfolio(**_portfolio_short_history)
 
+
 # Efficient Frontier Single Period
 @pytest.fixture(scope="module")
 def init_efficient_frontier_values1():

tests/conftest.py

@@ -86,31 +86,35 @@ def test_make_asset_list(self):
         assert self.asset_list.assets_ror.columns.name == "Symbols"
 
     def test_calculate_wealth_indexes(self):
-        assert self.asset_list.wealth_indexes.sum(axis=1)[-1] == approx(
-            3306.19, rel=1e-2
-        )  # last month indexes sum
+        assert self.asset_list.wealth_indexes.sum(axis=1)[-1] == approx(3306.19, rel=1e-2)  # last month indexes sum
 
     def test_risk(self):
         assert self.asset_list.risk_monthly.iloc[-1]["USDRUB.CBR"] == approx(0.0258, rel=1e-2)
         assert self.asset_list.risk_monthly.iloc[-1]["MCFTR.INDX"] == approx(0.0264, rel=1e-2)
         assert self.asset_list.risk_annual.iloc[-1]["USDRUB.CBR"] == approx(0.0825, rel=1e-2)
         assert self.asset_list.risk_annual.iloc[-1]["MCFTR.INDX"] == approx(0.1222, rel=1e-2)
 
-    @mark.parametrize("window, expected", [
-        pytest.param(12, 0.1874, id="12-months"),
-        pytest.param(6, 0.2205, id="6-months"),
-    ])
+    @mark.parametrize(
+        "window, expected",
+        [
+            pytest.param(12, 0.1874, id="12-months"),
+            pytest.param(6, 0.2205, id="6-months"),
+        ],
+    )
     def test_get_rolling_risk_annual(self, window, expected):
         result = self.asset_list.get_rolling_risk_annual(window).iloc[-1]
         assert result.sum() == approx(expected, abs=1e-2)
 
-    @pytest.mark.parametrize("window, exception", [
-        pytest.param(0, ValueError, id="error-0-months"),
-        pytest.param(-1, ValueError, id="error-negative-months"),
-        pytest.param(34, ValueError, id="error - window period is too long"),
-        pytest.param("twelve", TypeError, id="error-string-input"),
-        pytest.param(None, TypeError, id="error-none-input"),
-    ])
+    @pytest.mark.parametrize(
+        "window, exception",
+        [
+            pytest.param(0, ValueError, id="error-0-months"),
+            pytest.param(-1, ValueError, id="error-negative-months"),
+            pytest.param(34, ValueError, id="error - window period is too long"),
+            pytest.param("twelve", TypeError, id="error-string-input"),
+            pytest.param(None, TypeError, id="error-none-input"),
+        ],
+    )
     def test_get_rolling_risk_annual_error_cases(self, window, exception):
         with pytest.raises(exception):
             self.spy.get_rolling_risk_annual(window)