feat: redefine dividend_yield for Portfolio

Portfolio dividend yield calculated as a weighted sum of the assets dividend yields (adjusted to the portfolio base currency).

main.py

@@ -1,3 +1,3 @@
 import okama as ok
-x = ok.Portfolio(['SBERP.MOEX'], ccy='RUB', inflation=True)
-print(x.assets_dividend_yield)
+y = ok.AssetList(['T.US', 'CLDN.LSE'], ccy='GBP')
+print(y.assets_ror)

okama/common/make_asset_list.py

@@ -244,7 +244,10 @@ def assets_dividend_yield(self) -> pd.DataFrame:
         """
         Calculate last twelve months (LTM) dividend yield time series (monthly) for each asset.
 
-        All yields are calculated in the asset list base currency after adjusting the dividends time series.
+        LTM dividend yield is the sum trailing twelve months of common dividends per share divided by
+        the current price per share.
+
+        All yields are calculated in the asset list base currency after adjusting the dividends and price time series.
         Forecasted (future) dividends are removed.
         Zero value time series are created for assets without dividends.
 

okama/portfolio.py

@@ -59,7 +59,7 @@ def __init__(
         self.assets_weights = dict(zip(self.symbols, self.weights))
         self._rebalancing_period = None
         self.rebalancing_period = rebalancing_period
-        self._symbol = symbol if symbol else f'portfolio_{randint(1000, 9999)}.PF'
+        self._symbol = symbol or f'portfolio_{randint(1000, 9999)}.PF'
 
     def __repr__(self):
         dic = {
@@ -128,7 +128,10 @@ def weights_ts(self) -> pd.DataFrame:
         -------
         DataFrame
         """
-        return Rebalance.assets_weights_ts(ror=self.assets_ror, period=self.rebalancing_period, weights=self.weights)
+        if self.rebalancing_period != 'month':
+            return Rebalance.assets_weights_ts(ror=self.assets_ror, period=self.rebalancing_period, weights=self.weights)
+        values = np.tile(self.weights, (self.ror.shape[0], 1))
+        return pd.DataFrame(values, index=self.ror.index, columns=self.symbols)
 
     @property
     def rebalancing_period(self) -> str:
@@ -491,23 +494,36 @@ def annual_return_ts(self) -> pd.DataFrame:
     @property
     def dividend_yield(self) -> pd.Series:
         """
-        Calculates dividend yield time series in all base currencies of portfolio assets.
-        For every currency dividend yield is a weighted sum of the assets dividend yields.
-        # TODO: finish the docstring
-        """
-        div_yield_assets = self.assets_dividend_yield
+        Calculate last twelve months (LTM) dividend yield time series (monthly) for the portfolio.
 
-        for currency in currencies_list:
-            assets_with_the_same_currency = [
-                x for x in currencies_dict if currencies_dict[x] == currency
-            ]
-            weights = self.weights_ts[assets_with_the_same_currency]
-            normalized_weights = weights.divide(weights.sum(axis=1), axis=0)
-            df = div_yield_assets[assets_with_the_same_currency] @ normalized_weights.T
-            div_yield_series = pd.Series(np.diag(df), index=df.index)
-            div_yield_series.rename(currency, inplace=True)
-            div_yield_df = pd.concat([div_yield_df, div_yield_series], axis=1)
-        return div_yield_df
+        Portfolio dividend yield is a weighted sum of the assets dividend yields (adjusted to
+        the portfolio base currency).
+
+        For an asset LTM dividend yield is the sum trailing twelve months of common dividends per share divided by
+        the current price per share.
+
+        Returns
+        -------
+        Series
+            Portfolio LTM dividend yield monthly time series.
+
+        Examples
+        --------
+        >>> pf = ok.Portfolio(['T.US', 'XOM.US'], weights=[0.8, 0.2], first_date='2010-01', last_date='2021-01', ccy='USD')
+        >>> pf.dividend_yield
+        2010-01    0.013249
+        2010-02    0.014835
+        2010-03    0.014257
+                     ...
+        2020-11    0.076132
+        2020-12    0.074743
+        2021-01    0.073643
+        Freq: M, Name: LTM dividend yild, Length: 133, dtype: float64
+        """
+        df = self.assets_dividend_yield @ self.weights_ts.T
+        div_yield_series = pd.Series(np.diag(df), index=df.index)
+        div_yield_series.rename('LTM dividend yield', inplace=True)
+        return div_yield_series
 
     @property
     def real_mean_return(self) -> float:

tests/test_portfolio.py

@@ -59,16 +59,9 @@ def test_get_rolling_cumulative_return(portfolio_rebalanced_month):
     ] == approx(0.1226, rel=1e-2)
 
 
-@mark.xfail
 def test_dividend_yield(portfolio_dividends):
-    assert portfolio_dividends.assets_dividend_yield["USD"].iloc[-1] == approx(
-        0.0544, rel=1e-2
-    )
-    assert portfolio_dividends.assets_dividend_yield["GBX"].iloc[-1] == approx(
-        8.9935e-05, rel=1e-2
-    )
-    assert portfolio_dividends.assets_dividend_yield["RUB"].iloc[-1] == approx(
-        0.06344, rel=1e-2
+    assert portfolio_dividends.dividend_yield.iloc[-1] == approx(
+        0.03949, rel=1e-2
     )