fix: mean return for Portfolio must use ror.mean() formula

don't use Markowitz weights.T @ ror.mean() formula for rebalanced portfolios

Author: chilango74

main.py

@@ -25,7 +25,7 @@
     inflation=True
 )
 
-print(portf_div.describe())
+print(portf_div.mean_return_monthly)
 
 
 

main_notebook.ipynb

@@ -7,8 +7,8 @@
      "is_executing": true
     },
     "ExecuteTime": {
-     "end_time": "2025-10-21T09:46:06.335187Z",
-     "start_time": "2025-10-21T09:46:03.212159Z"
+     "end_time": "2025-11-03T08:51:09.040420Z",
+     "start_time": "2025-11-03T08:51:06.158525Z"
     }
    },
    "source": [
@@ -44,8 +44,8 @@
      "outputs_hidden": false
     },
     "ExecuteTime": {
-     "end_time": "2025-10-21T09:46:08.576786Z",
-     "start_time": "2025-10-21T09:46:08.137842Z"
+     "end_time": "2025-11-03T08:51:09.130226Z",
+     "start_time": "2025-11-03T08:51:09.058541Z"
     }
    },
    "source": [
@@ -432,8 +432,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-21T09:47:36.767644Z",
-     "start_time": "2025-10-21T09:47:36.697787Z"
+     "end_time": "2025-11-03T08:51:42.589398Z",
+     "start_time": "2025-11-03T08:51:42.522003Z"
     }
    },
    "source": [
@@ -445,7 +445,7 @@
     "assets4 = [\"GSPC.INDX\"]"
    ],
    "outputs": [],
-   "execution_count": 9
+   "execution_count": 4
   },
   {
    "cell_type": "code",
@@ -455,8 +455,8 @@
      "outputs_hidden": false
     },
     "ExecuteTime": {
-     "end_time": "2025-10-21T09:47:56.632738Z",
-     "start_time": "2025-10-21T09:47:37.394244Z"
+     "end_time": "2025-11-03T08:52:15.663625Z",
+     "start_time": "2025-11-03T08:51:51.214302Z"
     }
    },
    "source": [
@@ -480,7 +480,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "symbol                                                       portfolio_6232.PF\n",
+      "symbol                                                       portfolio_2072.PF\n",
       "assets                       [RGBITR.INDX, RUCBTRNS.INDX, MCFTR.INDX, GC.COMM]\n",
       "weights                                                   [0.2, 0.2, 0.4, 0.2]\n",
       "rebalancing_period                                                        year\n",
@@ -489,13 +489,13 @@
       "currency                                                                   USD\n",
       "inflation                                                             USD.INFL\n",
       "first_date                                                             2003-01\n",
-      "last_date                                                              2025-08\n",
-      "period_length                                               22 years, 8 months\n",
+      "last_date                                                              2025-09\n",
+      "period_length                                               22 years, 9 months\n",
       "dtype: object\n"
      ]
     }
    ],
-   "execution_count": 10
+   "execution_count": 5
   },
   {
    "cell_type": "code",
@@ -8263,28 +8263,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-15T08:19:31.590235Z",
-     "start_time": "2025-08-15T08:19:31.546011Z"
+     "end_time": "2025-11-03T08:52:21.835039Z",
+     "start_time": "2025-11-03T08:52:21.748132Z"
     }
    },
+   "source": [
+    "pf.mean_return_monthly"
+   ],
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "np.float64(0.011438815808118081)"
+       "np.float64(0.008952255472527475)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-03T08:52:40.222993Z",
+     "start_time": "2025-11-03T08:52:40.144054Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "pf.ror.mean()",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.009401680619363958)"
       ]
      },
      "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "pf.mean_return_monthly"
-   ]
+   "execution_count": 7
   },
   {
    "cell_type": "code",

okama/common/helpers/helpers.py

@@ -173,11 +173,11 @@ def get_portfolio_return_ts(cls, weights: list, ror: pd.DataFrame) -> pd.Series:
 
     @classmethod
     def get_portfolio_mean_return(cls, weights: Union[list, np.array], ror: pd.DataFrame) -> float:
-        # sourcery skip: assign-if-exp, reintroduce-else
         """
-        Computes mean return of a portfolio (monthly as ROR time series are monthly in okama).
+        Computes arithmetic mean return of a portfolio return (monthly as ROR time series are monthly in okama).
+
+        WARNING: This function is correct only for monthly rebalancing strategy (or for single asset portfolio)..
         """
-        # cls.weights_sum_is_one(weights)
         weights = np.asarray(weights)
         if isinstance(ror.mean(), float):  # required for a single asset portfolio
             return ror.mean()

okama/portfolios/core.py

@@ -454,7 +454,7 @@ def mean_return_monthly(self) -> float:
         >>> pf
         0.0001803312727272665
         """
-        return helpers.Frame.get_portfolio_mean_return(self.weights, self.assets_ror)
+        return self.ror.mean()
 
     @property
     def mean_return_annual(self) -> float: