日期

app/service/portfolio_diagnose.py

@@ -440,6 +440,47 @@ def get_fund_name(fund, fund_type=1):
             return df
 
 
+def get_fund_substrategy(fund, fund_type=1):
+    try:
+        if fund_type == 0:
+            with TAMP_SQL(tamp_fund_engine) as tamp_fund:
+                tamp_fund_session = tamp_fund.session
+                sql = "SELECT fund_short_name FROM tx_fund_info WHERE `id`='{}'".format(fund)
+                # df = pd.read_sql(sql, con)
+                cur = tamp_fund_session.execute(sql)
+                data = cur.fetchall()
+                df = pd.DataFrame(list(data), columns=['fund_short_name'])
+                return str(df.values[0][0])
+        elif fund_type == 1 or fund_type == 2:
+            with TAMP_SQL(tamp_fund_engine) as tamp_fund:
+                tamp_fund_session = tamp_fund.session
+                sql = "SELECT fund_short_name FROM fund_info WHERE id='{}'".format(fund)
+                # df = pd.read_sql(sql, con)
+                cur = tamp_fund_session.execute(sql)
+                data = cur.fetchall()
+                df = pd.DataFrame(list(data), columns=['fund_short_name'])
+                if len(df) == 0:
+                    with TAMP_SQL(tamp_product_engine) as tamp_product:
+                        tamp_product_session = tamp_product.session
+                        sql = "SELECT fund_short_name FROM fund_info WHERE id='{}'".format(fund)
+                        # df = pd.read_sql(sql, con)
+                        cur = tamp_product_session.execute(sql)
+                        data = cur.fetchall()
+                        df = pd.DataFrame(list(data), columns=['fund_short_name'])
+                return str(df.values[0][0])
+        else:
+            with TAMP_SQL(tamp_fund_engine) as tamp_fund:
+                    tamp_fund_session = tamp_fund.session
+                    sql = "SELECT fund_name FROM ifa_imported_fund_info WHERE id='{}'".format(fund)
+                    # df = pd.read_sql(sql, con)
+                    cur = tamp_fund_session.execute(sql)
+                    data = cur.fetchall()
+                    df = pd.DataFrame(list(data), columns=['fund_short_name'])
+                    return str(df.values[0][0])
+    except:
+        return "-1"
+
+
 # 获取排名信息
 fund_rank = get_fund_rank()
 tx_fund_rank = get_fund_rank(0)
@@ -1016,7 +1057,7 @@ class PortfolioDiagnose(object):
 
         return ret, old_return_compare_result, old_indicator_compare
 
-    def new_evaluation(self, group_name, group_result, data_adaptor):
+    def new_evaluation(self, group_name, group_result, data_adaptor, fund_type_dict):
         try:
             group_result_data = group_result[group_name]
             hold_info = group_result_data["group_hoding_info"]
@@ -1038,20 +1079,45 @@ class PortfolioDiagnose(object):
             # propose_fund_id_list = list(propose_fund_return.columns)
             propose_fund_id_list = list(self.proposal_fund)
             # propose_fund_id_list.remove("return")
-            with TAMP_SQL(tamp_product_engine) as tamp_product:
-                tamp_product_session = tamp_product.session
-                sql_product = "select distinct `id`, `fund_short_name`, `nav_frequency`, `substrategy` from `fund_info`"
-                cur = tamp_product_session.execute(sql_product)
-                data = cur.fetchall()
-                product_df = pd.DataFrame(list(data), columns=['fund_id', 'fund_name', 'freq', 'substrategy'])
-            propose_fund_df = product_df[product_df["fund_id"].isin(propose_fund_id_list)]
+            if fund_tye_dict is None:
+                with TAMP_SQL(tamp_product_engine) as tamp_product, TAMP_SQL(tamp_fund_engine) as tamp_fund:
+                    tamp_product_session = tamp_product.session
+                    tamp_fund_session = tamp_fund.session
+                    sql_product = """select distinct `id`, `fund_short_name`, `nav_frequency`, `substrategy` from `fund_info` where `id` in {}""".format(
+                        str(tuple(propose_fund_id_list)))
+                    cur = tamp_product_session.execute(sql_product)
+                    cur2 = tamp_fund_session.execute(sql_product)
+                    data = cur.fetchall()
+                    data2 = cur2.fetchall()
+                    product_df = pd.DataFrame(list(data) + list(data2),
+                                              columns=['fund_id', 'fund_name', 'freq', 'substrategy'])
+                propose_fund_df = product_df[product_df["fund_id"].isin(propose_fund_id_list)]
+
+                for index, row in propose_fund_df.iterrows():
+                    if math.isnan(row["freq"]):
+                        propose_fund_df.loc[index, "freq"] = 2
+                    if math.isnan(row["substrategy"]):
+                        propose_fund_df.loc[index, "substrategy"] = 5010
+
+                # 基金名称，策略分级
+                propose_fund_id_name_list = [
+                    propose_fund_df[propose_fund_df["fund_id"] == fund_id]["fund_name"].values[0]
+                    for
+                    fund_id in propose_fund_id_list]
+                propose_fund_id_strategy_name_list = [
+                    get_substrategy_name(
+                        propose_fund_df[propose_fund_df["fund_id"] == fund_id]["substrategy"].values[0])
+                    for
+                    fund_id in propose_fund_id_list]
+            else:
+                propose_fund_id_name_list = [get_fund_name(fund_id, fund_tye_dict[fund_id]).values[0][0] for fund_id in
+                                             propose_fund_id_list]
+                propose_fund_id_strategy_name_list = [
+                    get_substrategy_name(get_fund_substrategy(fund_id, fund_tye_dict[fund_id])) for fund_id in
+                    propose_fund_id_list]
+                propose_fund_asset = [round(portfolio_diagnose.new_weights[i] * total_asset, 2) for i in
+                                      range(len(propose_fund_id_name_list))]
 
-            # 基金名称，策略分级
-            propose_fund_id_name_list = [propose_fund_df[propose_fund_df["fund_id"] == fund_id]["fund_name"].values[0] for
-                                         fund_id in propose_fund_id_list]
-            propose_fund_id_strategy_name_list = [dict_substrategy(propose_fund_df[propose_fund_df["fund_id"] == fund_id]["substrategy"].values[0]) for
-                                         fund_id in propose_fund_id_list]
-            propose_fund_asset = [round(self.new_weights[i] * total_asset, 2) for i in range(len(propose_fund_id_name_list))]
 
             propose_info = {propose_fund_id_strategy_name_list[i]:
                                 {"fund_name": propose_fund_id_name_list[i],
@@ -1214,7 +1280,7 @@ class PortfolioDiagnose(object):
         if fund_id_type == 0:
             rank_df = tx_fund_rank
         else:
-            rank_df = tx_fund_rank
+            rank_df = fund_rank
 
         z_score = search_rank(rank_df, fund_id, metric='z_score')
         total_level = np.select([z_score >= 80,

app/service/result_service_v2.py

@@ -21,8 +21,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
     total_result_data = {}
     group_result_data = {}
 
-    def __init__(self, user_id, customer_id):
-        super().__init__(user_id, customer_id)
+    def __init__(self, user_id, customer_id, end_date=str(datetime.datetime.now().date())):
+        super().__init__(user_id, customer_id, end_date=end_date)
 
     # 组合结果数据
     def calculate_group_result_data(self):
@@ -480,10 +480,10 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             else:
                 last_month_cnav = float(last_month_cnav_serise.values[-1])
                 temp_profit = float(cal_month_cnav_result[cur_fund_id + "_earn"].sum())
-                fund_hoding_info["profit"] = round(float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - last_month_cnav)/10000, 2)
+                fund_hoding_info["profit"] = round(temp_profit/10000, 2)
                 temp_profit_ratio = (fund_basic_info["cur_cnav"] - last_month_cnav) / last_month_cnav
             # 当月收益率
-            fund_hoding_info["month_return_ratio"] = "%.2f" % round(temp_profit / temp_market_values*100, 2)
+            fund_hoding_info["month_return_ratio"] = "%.2f" % round(temp_profit / temp_cost*100, 2)
             # fund_hoding_info["month_return_ratio"] = "%.2f" % round(temp_profit_ratio * 100, 2)
             # 累积收益
             cum_profit = float(p_result_cnav_data[cur_fund_id + "_earn"].sum())

app/templates/v2/monthReportV2.1.html

@@ -141,6 +141,13 @@
       font-weight: bold;
     }
 
+    .box0 .cover_title .cover_title_text .cover_date .cover_time .cover_time_span {
+      font-size: 22px;
+      line-height: 48px;
+      color: #ffffff;
+      font-weight: bold;
+    }
+
     .box_line {
       height: 15px;
       background: #F1F1F1;
@@ -976,7 +983,7 @@
           {{cover_title2}}
           <div class="cover_date">
             <div class="cover_time">
-              {{year_month}}
+              {{end_date}}<div class="cover_time_span">最新净值日期： {{latest_worth_day}}</div>
             </div>
           </div>
         </div>

app/utils/jinjia2html_v2.py

@@ -23,8 +23,9 @@ from app.utils.radar_chart import gen_radar_chart
 
 
 class DataIntegrate:
-    def __init__(self, ifa_id='USER_INFO15917850824287', customer_id='6716613802534121472', pdf_name=str(uuid.uuid4()) + '.pdf', type=1):
-        self.user_customer = UserCustomerResultAdaptor(ifa_id, customer_id)
+    def __init__(self, ifa_id='USER_INFO15917850824287', customer_id='6716613802534121472',
+                 pdf_name=str(uuid.uuid4()) + '.pdf', type=1, end_date=str(datetime.now().date())):
+        self.user_customer = UserCustomerResultAdaptor(ifa_id, customer_id, end_date=end_date)
         self.customer_name = self.user_customer.customer_real_name
         self.ifa_name = self.user_customer.ifa_real_name
         # self.pdf_name = self.ifa_name + "_" + self.customer_name + "_" + '.pdf'
@@ -50,7 +51,9 @@ class DataIntegrate:
     # 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():
-            portfolio_diagnose = self.get_portfolio_diagnose(group_result["fund_id_list"], invest_amount=group_result["total_cost"])
+            portfolio_diagnose = self.get_portfolio_diagnose(group_result["fund_id_list"],
+                                                             invest_amount=group_result["total_cost"],
+                                                             weight_dict=group_result["weight_result"])
             cur_group_portfolio_result = {
                 'new_correlation': [],
                 'propose_fund_data_list': [],
@@ -84,17 +87,23 @@ class DataIntegrate:
 
             self.all_folio_result[group_name] = cur_group_portfolio_result
 
-    def get_portfolio_diagnose(self, portfolio, client_type=1, invest_amount=10000000):
+    def get_portfolio_diagnose(self, portfolio, client_type=1, invest_amount=10000000, weight_dict={}):
         if invest_amount < 10000000:
             invest_amount = 10000000
         folio_fund_dict = {}
+        folio_fund_list = []
+        folio_fund_weight_list = []
         for fd in portfolio:
+            folio_fund_list.append(fd)
+            folio_fund_weight_list.append(weight_dict[fd])
             folio_fund_dict[fd] = self.user_customer.all_fund_type_dict[fd]
+
         portfolio_diagnose = PortfolioDiagnose(client_type=client_type, portfolio=folio_fund_dict,
                                                invest_amount=float(invest_amount),
                                                start_date=self.user_customer.start_date)
         if self.type == 2:
-            portfolio_diagnose.optimize()
+            portfolio_diagnose.proposal_customize(folio_fund_list, folio_fund_weight_list, folio_fund_dict)
+            # portfolio_diagnose.optimize()
         return portfolio_diagnose
 
     # 全部数据综述结果
@@ -166,8 +175,8 @@ class DataIntegrate:
         """新收益比较，调仓建议"""
         cur_group_portfolio_result["suggestions_result"], cur_group_portfolio_result["suggestions_result_asset"], \
         cur_group_portfolio_result["return_compare_data"], \
-        cur_group_portfolio_result["indicator_compare"], cur_group_portfolio_result["new_group_evaluation"] = portfolio_diagnose.new_evaluation(folio, self.d,
-                                                                                                   self.user_customer)
+        cur_group_portfolio_result["indicator_compare"], cur_group_portfolio_result["new_group_evaluation"] = \
+            portfolio_diagnose.new_evaluation(folio, self.d, self.user_customer, self.user_customer.all_fund_type_dict)
 
         cur_group_portfolio_result["return_compare_pic"] = draw_combination_chart(cur_group_portfolio_result["return_compare_data"]["xlabels"],
                                                                                   cur_group_portfolio_result["return_compare_data"]["new_combination"],
@@ -237,6 +246,7 @@ class DataIntegrate:
                 # 全局数据
                 'customer_name': self.customer_name,
                 'year_month': datetime.now().strftime("%Y-%m-%d"),
+                'end_date': self.user_customer.end_date.strftime("%Y-%m-%d"),
                 'valueSex': self.user_customer.valueSex,
                 'month': self.user_customer.month_start_date.strftime("%m"),
                 'start_date': self.user_customer.start_date.strftime("%Y-%m-%d"),
@@ -271,7 +281,8 @@ class DataIntegrate:
             data = {
                 # 全局数据
                 'customer_name': self.customer_name,
-                'year_month': self.user_customer.end_date.strftime("%Y-%m-%d"),
+                'year_month': datetime.now().strftime("%Y-%m-%d"),
+                'end_date': self.user_customer.end_date.strftime("%Y-%m-%d"),
                 'valueSex': self.user_customer.valueSex,
                 'month': self.user_customer.month_start_date.strftime("%m"),
                 'start_date': self.user_customer.start_date.strftime("%Y-%m-%d"),
@@ -312,9 +323,9 @@ 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/pdf/monthReport.html"
-        # with open(save_file, 'w', encoding="utf-8") as f:
-        #     f.write(monthReport_html)
+        save_file = "app/pdf/monthReport.html"
+        with open(save_file, 'w', encoding="utf-8") as f:
+            f.write(monthReport_html)
 
         # save_file = "app/html/v2/monthReportV2.html"
         # with open(save_file, 'w', encoding="utf-8") as f: