默认收益和回撤评价

app/service/portfolio_diagnose.py

@@ -684,16 +684,17 @@ class PortfolioDiagnose(object):
         total_profit = round(group_result[group_name]["cumulative_profit"] / 10000, 2)
         # 整体表现 回撤能力
         fund_rank_data = fund_rank[fund_rank["fund_id"].isin(self.portfolio)]
-        z_score = fund_rank_data["z_score"].mean()
-        drawdown_rank = group_result[group_name]["max_drawdown"][0]
+        z_score = (group_result[group_name]["cumulative_return"] - 1)*100
+        drawdown_rank = group_result[group_name]["max_drawdown"][0]*100
         return_rank_df = fund_rank_data["annual_return_rank"]
-        z_score_level = np.select([z_score >= 80,
-                                   50 <= z_score < 80,
-                                   z_score < 50], [0, 1, 2]).item()
-        drawdown_level = np.select([drawdown_rank <= 0.05,
-                                    0.05 <= drawdown_rank < 0.1,
-                                    0.1 <= drawdown_rank < 0.15,
-                                    drawdown_rank > 0.15], [0, 1, 2, 3]).item()
+        z_score_level = np.select([z_score > 20,
+                                   15 <= z_score < 20,
+                                   10 <= z_score < 15,
+                                   z_score < 10], [0, 1, 2, 3]).item()
+        drawdown_level = np.select([drawdown_rank <= 5,
+                                    5 <= drawdown_rank < 7,
+                                    7 <= drawdown_rank < 10,
+                                    drawdown_rank > 10], [0, 1, 2, 3]).item()
         # 收益稳健
         fund_rank_re = fund_rank_data[fund_rank_data["annual_return_rank"] > 0.8]
         return_rank_evaluate = ""
@@ -748,7 +749,7 @@ class PortfolioDiagnose(object):
             fund_corr_evaluate = "；"
 
         num_fund = len(self.portfolio)
-        evaluate_enum = [["优秀", "良好", "一般"],
+        evaluate_enum = [["优秀", "良好", "一般", "较差"],
                          ["优秀", "良好", "合格", "较差"]]
 
         if data_adaptor.total_result_data["cumulative_profit"] < 0 and z_score_level == 0: