新版本推荐基金，支持ifa导入基金

app/service/data_service_v2_1.py

@@ -29,6 +29,8 @@ class UserCustomerDataAdaptor:
     end_date = ""
     group_data = {}
     trade_cal_date = None
+    all_fund_id_list = None
+    all_fund_type_dict = None
     all_fund_distribution = {}
     all_fund_performance = {}
 
@@ -38,7 +40,7 @@ class UserCustomerDataAdaptor:
         self.compare_index_id = index_id
         self.valueSex = ''
         p_end_date = pd.to_datetime(end_date).date()
-        p_end_date = datetime.date(year=p_end_date.year, month=p_end_date.month, day=1) - datetime.timedelta(days=1)
+        # p_end_date = datetime.date(year=p_end_date.year, month=p_end_date.month, day=1) - datetime.timedelta(days=1)
         self.end_date = pd.to_datetime(str(p_end_date))
         # self.end_date = pd.to_datetime("2020-12-25")
         p_start_date = datetime.date(year=p_end_date.year, month=p_end_date.month, day=1)
@@ -104,6 +106,20 @@ class UserCustomerDataAdaptor:
                 if len(fund_df) > 0:
                     product_df = product_df.append(fund_df)
                     product_df = product_df.drop_duplicates("fund_id")
+            fund_type3 = order_df[order_df["type"] == 3]
+            if len(fund_type3) > 0:
+                type3_fund_id = list(fund_type3["fund_id"].unique())
+                fund_list_str_3 = str(type3_fund_id).replace("[", "(").replace("]", ")")
+                sql_fund_3 = "select distinct `id`, `fund_name`, `substrategy` from `ifa_imported_fund_info` where  `id` in {}".format(
+                    fund_list_str_3)
+                cur = tamp_fund_session.execute(sql_fund_3)
+                data = cur.fetchall()
+                fund_df_3 = pd.DataFrame(list(data), columns=['fund_id', 'fund_name', 'substrategy'])
+                fund_df_3["freq"] = 1
+
+                if len(fund_df_3) > 0:
+                    product_df = product_df.append(fund_df_3)
+                    product_df = product_df.drop_duplicates("fund_id")
 
             user_customer_order_df = order_df.set_index('fund_id').join(product_df.set_index('fund_id')).reset_index()
             user_customer_order_df["confirm_share_date"] = user_customer_order_df["confirm_share_date"].apply(lambda x: pd.to_datetime(x.date()))
@@ -168,6 +184,10 @@ class UserCustomerDataAdaptor:
                     sql = """select distinct `price_date`, `nav`,`cumulative_nav` from `fund_nav` where `fund_id`='{}'  order by `price_date` ASC""".format(
                         cur_fund_id)
                     cur = tamp_product_session.execute(sql)
+                elif fund_type == 3:
+                    sql = """select distinct `price_date`, `nav`,`cumulative_nav` from `ifa_imported_fund_nav` where `fund_id`='{}'  order by `price_date` ASC""".format(
+                        cur_fund_id)
+                    cur = tamp_fund_session.execute(sql)
                 data = cur.fetchall()
                 cur_fund_nav_df = pd.DataFrame(list(data), columns=['price_date', 'nav', 'cnav'])
 
@@ -342,6 +362,8 @@ class UserCustomerDataAdaptor:
         cnav_df = p_cnav_df[p_cnav_df.index >= start_date].copy()
 
         p_fund_id_list = list(p_order_df["fund_id"].unique())
+        self.all_fund_id_list = p_fund_id_list
+        self.all_fund_type_dict = {values["fund_id"]: values["type"] for values in p_order_df[["fund_id", "type"]].drop_duplicates().to_dict(orient='records')}
         for p_fund_id in p_fund_id_list:
             order_min_date = p_order_df[p_order_df["fund_id"] == p_fund_id]["confirm_share_date"].min()
             if pd.to_datetime(order_min_date) > start_date:

app/service/result_service_v2.py

@@ -19,8 +19,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
     total_result_data = {}
     group_result_data = {}
 
-    def __init__(self, user_id, customer_id, end_date=str(datetime.date.today())):
-        super().__init__(user_id, customer_id, end_date)
+    def __init__(self, user_id, customer_id):
+        super().__init__(user_id, customer_id)
 
     # 组合结果数据
     def calculate_group_result_data(self):
@@ -57,6 +57,11 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             return_ratio_df, month_return_ratio_df, contribution_decomposition = self.combination_yield(resample_cur_folio_result_cnav_data,
                                                                                  fund_id_list)
             resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
+            if resample_df.index.values[-1] > self.end_date:
+                last = resample_df.index.values[-1]
+                resample_df["index_date"] = resample_df.index
+                resample_df.loc[last, "index_date"] = self.end_date
+                resample_df.set_index("index_date", inplace=True)
             resample_df = resample_df[resample_df.index <= self.end_date]
 
 
@@ -87,7 +92,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
 
             # 波动率
             volatility_ = volatility(resample_df["cum_return_ratio"], n_freq)
-            folio_report_data["volatility"] = float(volatility_)
+            folio_report_data["volatility"] = float(volatility_) if not math.isnan(volatility_) else 0.0
 
             # 最大回撤
             drawdown = max_drawdown(resample_df["cum_return_ratio"])
@@ -100,7 +105,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
                 sharpe = sharpe_ratio(exc, sim, n_freq)
             except ZeroDivisionError:
                 sharpe = 0.0
-            folio_report_data["sharpe"] = float(sharpe)
+            folio_report_data["sharpe"] = float(sharpe) if not math.isnan(sharpe) else 0.0
 
             # 期末资产
             ending_assets = cumulative_profit + total_cost
@@ -135,7 +140,10 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             if len(last_profit_ratio) <= 0:
                 cur_year_profit_ratio = cur_profit_ratio[-1] - 1
             else:
-                cur_year_profit_ratio = (cur_profit_ratio[-1] - last_profit_ratio[-1]) / last_profit_ratio[-1]
+                if len(cur_profit_ratio) < 1:
+                    cur_year_profit_ratio = 0
+                else:
+                    cur_year_profit_ratio = (cur_profit_ratio[-1] - last_profit_ratio[-1]) / last_profit_ratio[-1]
             folio_report_data["cur_year_profit_ratio"] = float(cur_year_profit_ratio)
 
             # 累积收益率
@@ -226,6 +234,11 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         resample_cur_folio_result_cnav_data = resample_cur_folio_result_cnav_data[resample_cur_folio_result_cnav_data.index <=self.end_date]
         return_ratio_df, month_return_ratio_df, contribution_decomposition = self.combination_yield(resample_cur_folio_result_cnav_data, fund_id_list)
         resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
+        if resample_df.index.values[-1] > self.end_date:
+            last = resample_df.index.values[-1]
+            resample_df["index_date"] = resample_df.index
+            resample_df.loc[last, "index_date"] = self.end_date
+            resample_df.set_index("index_date", inplace=True)
         resample_df = resample_df[resample_df.index <= self.end_date]
 
         # 总成本
@@ -289,7 +302,13 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         if len(last_profit_ratio) <= 0:
             cur_year_profit_ratio = cur_profit_ratio[-1] - 1
         else:
-            cur_year_profit_ratio = (cur_profit_ratio[-1] - last_profit_ratio[-1]) / last_profit_ratio[-1]
+            if len(cur_profit_ratio) < 1:
+                cur_year_profit_ratio = 0.0
+            else:
+                if len(cur_profit_ratio) < 1:
+                    cur_year_profit_ratio = 0
+                else:
+                    cur_year_profit_ratio = (cur_profit_ratio[-1] - last_profit_ratio[-1]) / last_profit_ratio[-1]
         report_data["cur_year_profit_ratio"] = float(cur_year_profit_ratio)
 
         # 月度回报
@@ -422,7 +441,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             # 基金持仓数据
             total_market_values = p_sum_profit + p_total_amount #   月末总市值
             fund_strategy_name = dict_substrategy[int(row["substrategy"])]
-            if "长富" in row["fund_name"] or "盈沛" in row["fund_name"] :
+            if "长富" in row["fund_name"] or "盈沛" in row["fund_name"]:
                 fund_strategy_name = "FOF"
             fund_hoding_info = {"fund_strategy_name": fund_strategy_name, "fund_name": row["fund_name"]}
             fund_hoding_info["confirm_date"] = row["confirm_share_date"].strftime("%Y-%m-%d")
@@ -571,7 +590,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
 
         # 波动率
         volatility_ = volatility(fund_nav_df["cum_return_ratio"], n_freq)
-        result["volatility"] = float(volatility_)
+        result["volatility"] = float(volatility_) if not math.isnan(volatility_) else 0.0
 
         # 最大回撤
         drawdown = max_drawdown(fund_nav_df["cum_return_ratio"])

app/utils/jinjia2html_v2.py

@@ -45,7 +45,6 @@ class DataIntegrate:
         # 月度回报表格
         self.get_month_table_return()
 
-
     # 分组和计算个基点评以及新增基金等结果
     def get_group_result(self):
         for group_name, group_result in self.d.items():
@@ -83,7 +82,10 @@ 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=float(invest_amount),
+        if invest_amount < 10000000:
+            invest_amount = 10000000
+        portfolio_diagnose = PortfolioDiagnose(client_type=client_type, portfolio=self.user_customer.all_fund_type_dict,
+                                               invest_amount=float(invest_amount),
                                                start_date=self.user_customer.start_date)
         portfolio_diagnose.optimize()
         return portfolio_diagnose
@@ -299,7 +301,7 @@ class DataIntegrate:
         template = env.get_template('/v2/monthReportV2.1.html')  # 获取一个模板文件
         monthReport_html = template.render(self.data).replace('None', 'none')  # 渲染
         # 保存 monthReport_html
-        # save_file = "app/html/monthReport.html"
+        # save_file = "app/pdf/monthReport.html"
         # with open(save_file, 'w', encoding="utf-8") as f:
         #     f.write(monthReport_html)