小数点后两位

app/service/portfolio_diagnose.py

@@ -685,15 +685,15 @@ class PortfolioDiagnose(object):
         # 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 = fund_rank_data["max_drawdown_rank"].mean()
+        drawdown_rank = group_result[group_name]["max_drawdown"][0]
         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 >= 0.8,
-                                    0.7 <= drawdown_rank < 0.8,
-                                    0.6 <= drawdown_rank < 0.7,
-                                    drawdown_rank < 0.6], [0, 1, 2, 3]).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()
         # 收益稳健
         fund_rank_re = fund_rank_data[fund_rank_data["annual_return_rank"] > 0.8]
         return_rank_evaluate = ""

app/service/result_service_v2.py

@@ -158,14 +158,14 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             folio_report_data["group_nav_info"] = result_fund_nav_info
             folio_report_data["group_hoding_info"] = result_fund_hoding_info
             folio_report_data["group_hoding_info_total"] = \
-                {"total_cost": round(total_cost/10000.0, 2),
-                 "cur_month_profit": round(cur_month_profit/10000.0, 2),
-                 "cur_month_profit_ratio": round(cur_month_profit_ratio*100, 2),
-                 "ending_assets": round(ending_assets/10000.0, 2),
+                {"total_cost": ".2f" % round(total_cost/10000.0, 2),
+                 "cur_month_profit": ".2f" % round(cur_month_profit/10000.0, 2),
+                 "cur_month_profit_ratio": ".2f" % round(cur_month_profit_ratio*100, 2),
+                 "ending_assets": ".2f" % round(ending_assets/10000.0, 2),
                  "weight": 100,
-                 "cumulative_profit": round(cumulative_profit/10000.0, 2),
-                 "cumulative_return": round((cumulative_return-1)*100, 2),
-                 "return_ratio_year": round(return_ratio_year*100, 2)}
+                 "cumulative_profit": ".2f" % round(cumulative_profit/10000.0, 2),
+                 "cumulative_return": ".2f" % round((cumulative_return-1)*100, 2),
+                 "return_ratio_year": ".2f" % round(return_ratio_year*100, 2)}
 
             # 对应指数数据
             index_df = self.get_customer_index_nav_data()
@@ -308,8 +308,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             cur_month = int(d_index[5:])
             cur_profit = round(d_row/10000.0, 2)
             cur_net_amount = round(month_amount_sum.loc[d_index]/10000, 2)
-            month_return_data_dict[cur_year][cur_month]["profit"] = cur_profit
-            month_return_data_dict[cur_year][cur_month]["net_amount"] = cur_net_amount
+            month_return_data_dict[cur_year][cur_month]["profit"] = "%.2f"%cur_profit
+            month_return_data_dict[cur_year][cur_month]["net_amount"] = "%.2f"%cur_net_amount
         # 组合月度回报表
         report_data["month_return_data_dict"] = month_return_data_dict
 
@@ -371,10 +371,10 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             total_market_values = p_sum_profit + p_total_amount #   月末总市值
             fund_hoding_info = {"fund_strategy_name": dict_substrategy[int(row["substrategy"])], "fund_name": row["fund_name"]}
             fund_hoding_info["confirm_date"] = row["confirm_share_date"]
-            fund_hoding_info["hold_year"] = round((self.end_date - pd.to_datetime(row["confirm_share_date"])).days/365.0, 2)    # 存续年数
+            fund_hoding_info["hold_year"] = "%.2f" % round((self.end_date - pd.to_datetime(row["confirm_share_date"])).days/365.0, 2)    # 存续年数
             fund_hoding_info["market_values"] = round((float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav) + float(row["confirm_amount"]))/10000, 2)
-            fund_hoding_info["weight"] = round(fund_hoding_info["market_values"] / total_market_values * 10000.0 * 100, 2)  # 月末占比
-            fund_hoding_info["cost"] = round(float(row["confirm_amount"])/10000, 2)     # 投资本金
+            fund_hoding_info["weight"] = "%.2f" % round(float(fund_hoding_info["market_values"]) / total_market_values * 10000.0 * 100, 2)  # 月末占比
+            fund_hoding_info["cost"] = "%.2f" % round(float(row["confirm_amount"])/10000, 2)     # 投资本金
             # 当月收益
             last_month_cnav_serise = p_result_cnav_data[p_result_cnav_data.index<pd.to_datetime(self.month_start_date)][row["fund_id"]].dropna()
             if len(last_month_cnav_serise) == 0:
@@ -383,15 +383,15 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
                 last_month_cnav = float(last_month_cnav_serise.values[-1])
                 fund_hoding_info["profit"] = round(float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - last_month_cnav)/10000, 2)
             # 当月收益率
-            fund_hoding_info["month_return_ratio"] = round(fund_hoding_info["profit"] / fund_hoding_info["market_values"]*100, 2)
+            fund_hoding_info["month_return_ratio"] = "%.2f" % round(float(fund_hoding_info["profit"]) / float(fund_hoding_info["market_values"])*100, 2)
             # 累积收益
-            fund_hoding_info["cum_profit"] = round(float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav) / 10000, 2)
+            fund_hoding_info["cum_profit"] = "%.2f" % round(float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav) / 10000, 2)
             # 累积收益率
-            fund_hoding_info["cum_profit_ratio"] = round((fund_basic_info["cur_cnav"] - confirm_cnav)/confirm_cnav*100, 2)
+            fund_hoding_info["cum_profit_ratio"] = "%.2f" % round((fund_basic_info["cur_cnav"] - confirm_cnav)/confirm_cnav*100, 2)
             # 累积年化收益率
             cur_resample_df = resample_df[[row["fund_id"]]].dropna()
-            return_ratio_year = annual_return(fund_hoding_info["cum_profit_ratio"]/100.0, cur_resample_df, n_freq)
-            fund_hoding_info["return_ratio_year"] = round(float(return_ratio_year)*100, 2)
+            return_ratio_year = annual_return(float(fund_hoding_info["cum_profit_ratio"])/100.0, cur_resample_df, n_freq)
+            fund_hoding_info["return_ratio_year"] = "%.2f" % round(float(return_ratio_year)*100, 2)
             group_fund_hoding_info.append(fund_hoding_info)
         return group_fund_basic_info, group_fund_hoding_info
 

app/templates/v2/monthReportV2.1.html

@@ -1582,7 +1582,7 @@
                 <td>{{one.fund_name}}</td>
                 <td>{{one.confirm_date}}</td>
                 <td>{{one.hold_year}}</td>
-                <td>{{one.cost| round(precision=2)}}万</td>
+                <td>{{one.cost}}万</td>
                 <td>{{one.profit}}万</td>
                 <td>{{one.month_return_ratio}}%</td>
                 <td>{{one.market_values}}万</td>

app/utils/jinjia2html_v2.py

@@ -78,7 +78,7 @@ class DataIntegrate:
             self.all_folio_result[group_name] = cur_group_portfolio_result
 
     def get_portfolio_diagnose(self, portfolio, client_type=1, invest_amount=10000000):
-        portfolio_diagnose = PortfolioDiagnose(client_type=client_type, portfolio=portfolio, invest_amount=invest_amount,
+        portfolio_diagnose = PortfolioDiagnose(client_type=client_type, portfolio=portfolio, invest_amount=float(invest_amount),
                                                start_date=self.user_customer.start_date)
         portfolio_diagnose.optimize()
         return portfolio_diagnose
@@ -86,7 +86,7 @@ class DataIntegrate:
     # 全部数据综述结果
     def get_summarize(self):
         """投资总览."""
-        self.total_cost = round(self.df["total_cost"], 2)   # 投资成本
+        self.total_cost = int(self.df["total_cost"])  # 投资成本
         self.now_yield = round((self.df['cumulative_return']-1)*100, 2)     # 成立以来累计收益率
         self.now_annualised_return = round(self.df["return_ratio_year"] * 100, 2)  # 年化收益率
         self.index_yield = round((self.df["index_result"]["return_ratio"]-1)*100, 2)    # 指数收益率
@@ -203,17 +203,17 @@ class DataIntegrate:
     def objectives_performance(self, group_result, cur_group_portfolio_result):
         """目标与业绩"""
 
-        cur_group_portfolio_result["totoal_rate_of_return"] = round((group_result['cumulative_return']-1)*100, 2)       # 成立以来累计收益率
-        cur_group_portfolio_result["annualised_return"] = round(group_result["return_ratio_year"]*100, 2)     # 年化收益率
-        cur_group_portfolio_result["volatility"] = round(group_result["volatility"]*100, 2)
-        cur_group_portfolio_result["max_withdrawal"] = round(group_result["max_drawdown"][0]*100, 2)
-        cur_group_portfolio_result["sharpe_ratio"] = round(group_result["sharpe"], 2)
-        cur_group_portfolio_result["cost_of_investment"] = round(group_result["total_cost"]/10000.0, 2)    # 投资成本
-        cur_group_portfolio_result["index_section_return"] = round((group_result["index_result"]["return_ratio"]-1)*100, 2)
-        cur_group_portfolio_result["index_annualised_return"] = round(group_result["index_result"]["return_ratio_year"]*100, 2)     # 年化收益率
-        cur_group_portfolio_result["index_volatility"] = round(group_result["index_result"]["volatility"]*100, 2)
-        cur_group_portfolio_result["index_max_withdrawal"] = round(group_result["index_result"]["max_drawdown"][0]*100, 2)
-        cur_group_portfolio_result["index_sharpe_ratio"] = round(group_result["index_result"]["sharpe"], 2)
+        cur_group_portfolio_result["totoal_rate_of_return"] = "%.2f" % round((group_result['cumulative_return']-1)*100, 2)       # 成立以来累计收益率
+        cur_group_portfolio_result["annualised_return"] = "%.2f" % round(group_result["return_ratio_year"]*100, 2)     # 年化收益率
+        cur_group_portfolio_result["volatility"] = "%.2f" % round(group_result["volatility"]*100, 2)
+        cur_group_portfolio_result["max_withdrawal"] = "%.2f" % round(group_result["max_drawdown"][0]*100, 2)
+        cur_group_portfolio_result["sharpe_ratio"] = "%.2f" % round(group_result["sharpe"], 2)
+        cur_group_portfolio_result["cost_of_investment"] = "%.2f" % round(group_result["total_cost"]/10000.0, 2)    # 投资成本
+        cur_group_portfolio_result["index_section_return"] = "%.2f" % round((group_result["index_result"]["return_ratio"]-1)*100, 2)
+        cur_group_portfolio_result["index_annualised_return"] = "%.2f" % round(group_result["index_result"]["return_ratio_year"]*100, 2)     # 年化收益率
+        cur_group_portfolio_result["index_volatility"] = "%.2f" % round(group_result["index_result"]["volatility"]*100, 2)
+        cur_group_portfolio_result["index_max_withdrawal"] = "%.2f" % round(group_result["index_result"]["max_drawdown"][0]*100, 2)
+        cur_group_portfolio_result["index_sharpe_ratio"] = "%.2f" % round(group_result["index_result"]["sharpe"], 2)
 
         cur_group_portfolio_result["group_nav_info"] = group_result["group_nav_info"]
         cur_group_portfolio_result["group_hoding_info"] = group_result["group_hoding_info"]