fix: add return and refactor method in multi_period.py

Author: DNTNTRSRNM

okama/frontier/multi_period.py

@@ -701,15 +701,20 @@ def _target_cagr_range_left(self) -> np.ndarray:
         Full range of CAGR values (from min to max).
         """
         # Case 1: Left asset's CAGR is bigger than right asset's CAGR
-        if (hasattr(self, '_min_ratio_asset_left_to_max_cagr') and 
-            hasattr(self, '_max_ratio_asset_right_to_max_cagr') and
-            self._min_ratio_asset_left_to_max_cagr and
-            self._max_ratio_asset_right_to_max_cagr and
-            self._min_ratio_asset_left_to_max_cagr['min_asset_cagr'] >= 
-            self._max_ratio_asset_right_to_max_cagr['max_asset_cagr']):
+        has_min_ratio = hasattr(self, '_min_ratio_asset_left_to_max_cagr')
+        has_max_ratio = hasattr(self, '_max_ratio_asset_right_to_max_cagr')
+        
+        if has_min_ratio and has_max_ratio:
+            min_ratio_data = self._min_ratio_asset_left_to_max_cagr
+            max_ratio_data = self._max_ratio_asset_right_to_max_cagr
 
-            left_cagr = self._min_ratio_asset_left_to_max_cagr['min_asset_cagr']
-            return np.linspace(left_cagr, self.global_max_return_portfolio["CAGR"], self.n_points)
+            if min_ratio_data is not None and max_ratio_data is not None:
+                left_cagr = min_ratio_data.get('min_asset_cagr')
+                right_cagr = max_ratio_data.get('max_asset_cagr')
+                
+                if left_cagr is not None and right_cagr is not None:
+                    if left_cagr >= right_cagr:
+                        return np.linspace(left_cagr, self.global_max_return_portfolio["CAGR"], self.n_points)
 
         # Case 2: Left asset's CAGR is less than right asset's CAGR
         if self.full_frontier:
@@ -735,6 +740,7 @@ def _target_cagr_range_right(self) -> Optional[np.ndarray]:
                 number_of_points = round(self.n_points / k) + 1 if k > 1 else self.n_points
                 target_range = np.linspace(max_cagr, ticker_cagr, number_of_points)
                 return target_range[1:]  # skip the first point (max cagr) as it presents in the left part of the EF
+        return None  
 
     @property
     def target_risk_range(self) -> np.ndarray: