From 5b8f1a5526f306e02f1b786fea7aa7d6879b21df Mon Sep 17 00:00:00 2001
From: JVthearchitect <120519530+JVthearchitect@users.noreply.github.com>
Date: Mon, 8 Jan 2024 08:48:58 -0500
Subject: [PATCH 1/2] closes #65

add dividends_annual property to Portfolio
includes test file "t_test_portfolio_dividends_annual.py"

Please let me know if there are any errors or follow ups.
---
 okama/portfolio.py                   | 28 ++++++++++++++++++++++++++++
 t_test_portfolio_dividends_annual.py | 21 +++++++++++++++++++++
 2 files changed, 49 insertions(+)
 create mode 100644 t_test_portfolio_dividends_annual.py

diff --git a/okama/portfolio.py b/okama/portfolio.py
index fe7dbb2..1303f91 100644
--- a/okama/portfolio.py
+++ b/okama/portfolio.py
@@ -820,6 +820,34 @@ def dividend_yield(self) -> pd.Series:
         div_yield_series.rename(self.symbol, inplace=True)
         return div_yield_series
 
+    @property
+    def dividends_annual(self) -> pd.DataFrame:
+        """
+        Return calendar year dividends sum time series for each asset.
+
+        Returns
+        -------
+        DataFrame
+            Annual dividends time series for each asset.
+
+        See Also
+        --------
+        dividend_yield : Dividend yield time series.
+        dividend_yield_annual : Calendar year dividend yield time series.
+        dividend_paying_years : Number of years of consecutive dividend payments.
+        dividend_growing_years : Number of years when the annual dividend was growing.
+        get_dividend_mean_yield : Arithmetic mean for annual dividend yield.
+        get_dividend_mean_growth_rate : Geometric mean of annual dividends growth rate.
+
+        Examples
+        --------
+        >>> import matplotlib.pyplot as plt
+        >>> x = ok.AssetList(['T.US', 'XOM.US'], first_date='2010-01', last_date='2020-12')
+        >>> x.dividends_annual.plot(kind='bar')
+        >>> plt.show()
+        """
+        return self._get_assets_dividends().resample("Y").sum()
+
     @property
     def assets_dividend_yield(self):
         """
diff --git a/t_test_portfolio_dividends_annual.py b/t_test_portfolio_dividends_annual.py
new file mode 100644
index 0000000..a6eec66
--- /dev/null
+++ b/t_test_portfolio_dividends_annual.py
@@ -0,0 +1,21 @@
+import pytest
+import okama as ok
+
+tickers = [
+    "BND.US",
+    "VTI.US",
+    "VXUS.US"
+]
+
+w = [0.34, 0.33, 0.33]
+
+portfolio = ok.Portfolio(tickers, weights=w, rebalancing_period="none", inflation=False)
+
+def test_dividend_yield():
+    return portfolio.assets_dividend_yield
+
+def test_dividends_annual():
+    return portfolio.dividends_annual
+
+# portfolio.assets_dividend_yield
+# portfolio.dividends_annual
\ No newline at end of file

From 3d6a85b9d2967ae0417f460a1c23187a2c5ae9c2 Mon Sep 17 00:00:00 2001
From: Sourcery AI <>
Date: Mon, 8 Jan 2024 19:12:32 +0000
Subject: [PATCH 2/2] 'Refactored by Sourcery'

---
 okama/portfolio.py | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/okama/portfolio.py b/okama/portfolio.py
index 1303f91..38ecf1e 100644
--- a/okama/portfolio.py
+++ b/okama/portfolio.py
@@ -1118,9 +1118,7 @@ def recovery_period(self) -> int:
         s1 = s.where(s == 0).notnull().astype(int)
         s1_1 = s.where(s == 0).isnull().astype(int).cumsum()
         s2 = s1.groupby(s1_1).cumsum()
-        # Max recovery period date should not be in the border (means it's not recovered)
-        max_period = s2.max() if s2.idxmax().to_timestamp() != self.last_date else np.NAN
-        return max_period
+        return s2.max() if s2.idxmax().to_timestamp() != self.last_date else np.NAN
 
     def describe(self, years: Tuple[int] = (1, 5, 10)) -> pd.DataFrame:
         """
@@ -1332,7 +1330,7 @@ def percentile_inverse_cagr(
         """
         if distr == "hist":
             cagr_distr = self.get_rolling_cagr(years * settings._MONTHS_PER_YEAR).loc[:, [self.symbol]].squeeze()
-        elif distr in ["norm", "lognorm"]:
+        elif distr in {"norm", "lognorm"}:
             if not n:
                 n = 1000
             cagr_distr = self._get_cagr_distribution(distr=distr, years=years, n=n)
@@ -1381,7 +1379,7 @@ def percentile_history_cagr(self, years: int, percentiles: List[int] = [10, 50,
                 self.get_rolling_cagr(years * 12).loc[:, self.symbol].quantile(percentile / 100)
                 for years in period_range
             ]
-            returns_dict.update({percentile: percentile_returns_list})
+            returns_dict[percentile] = percentile_returns_list
         df = pd.DataFrame(returns_dict, index=list(period_range))
         df.index.rename("years", inplace=True)
         return df
@@ -1613,7 +1611,7 @@ def percentile_distribution_cagr(
         results = {}
         for percentile in percentiles:
             value = cagr_distr.quantile(percentile / 100)
-            results.update({percentile: value})
+            results[percentile] = value
         return results
 
     def percentile_wealth(
@@ -1669,12 +1667,12 @@ def percentile_wealth(
         """
         if distr == "hist":
             results = self.percentile_wealth_history(years=years, percentiles=percentiles).iloc[-1].to_dict()
-        elif distr in ["norm", "lognorm"]:
+        elif distr in {"norm", "lognorm"}:
             results = {}
             wealth_indexes = self._monte_carlo_wealth(distr=distr, years=years, n=n)
             for percentile in percentiles:
                 value = wealth_indexes.iloc[-1, :].quantile(percentile / 100)
-                results.update({percentile: value})
+                results[percentile] = value
         else:
             raise ValueError('distr should be "norm", "lognorm" or "hist".')
         if today_value: