Merge remote-tracking branch 'origin/dev' into dev

.idea/fund_report.iml

@@ -4,6 +4,8 @@
     <content url="file://$MODULE_DIR$" />
     <orderEntry type="jdk" jdkName="Python 3.7" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
+    <orderEntry type="module" module-name="tamp_course_order" />
+    <orderEntry type="module" module-name="tamp_app_scan" />
   </component>
   <component name="TestRunnerService">
     <option name="PROJECT_TEST_RUNNER" value="Unittests" />

.idea/modules.xml

@@ -3,6 +3,8 @@
   <component name="ProjectModuleManager">
     <modules>
       <module fileurl="file://$PROJECT_DIR$/.idea/fund_report.iml" filepath="$PROJECT_DIR$/.idea/fund_report.iml" />
+      <module fileurl="file://$PROJECT_DIR$/../tamp_app_scan/.idea/tamp_app_scan.iml" filepath="$PROJECT_DIR$/../tamp_app_scan/.idea/tamp_app_scan.iml" />
+      <module fileurl="file://$PROJECT_DIR$/../tamp_course_order/.idea/tamp_course_order.iml" filepath="$PROJECT_DIR$/../tamp_course_order/.idea/tamp_course_order.iml" />
     </modules>
   </component>
 </project>
\ No newline at end of file

.idea/vcs.xml

 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+    <mapping directory="" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$/../tamp_app_scan" vcs="Git" />
   </component>
 </project>
\ No newline at end of file

app/service/data_service.py

@@ -190,7 +190,7 @@ class UserCustomerDataAdaptor:
                 confirm_date_nav_data = p_nav_df[p_nav_df.index == confirm_share_date][cur_fund_id].tail(1)
                 confirm_date_cnav_data = p_cnav_df[p_cnav_df.index == confirm_share_date][cur_fund_id].tail(1)
                 diff_nav = Decimal(row["nav"]) - Decimal(confirm_date_nav_data.values[0])
-                cur_cnav = confirm_date_cnav_data.values[0] + diff_nav
+                cur_cnav = Decimal(confirm_date_cnav_data.values[0]) + diff_nav
                 cnav_df.loc[confirm_share_date, cur_fund_id] = cur_cnav
 
         cnav_df = cnav_df.dropna(axis=0, how="all").fillna(method='ffill')
@@ -285,7 +285,7 @@ class UserCustomerDataAdaptor:
                 confirm_date_nav_data = p_nav_df[p_nav_df.index == confirm_share_date][cur_fund_id].tail(1)
                 confirm_date_cnav_data = p_cnav_df[p_cnav_df.index == confirm_share_date][cur_fund_id].tail(1)
                 diff_nav = Decimal(row["nav"]) - Decimal(confirm_date_nav_data.values[0])
-                cur_cnav = confirm_date_cnav_data.values[0] + diff_nav
+                cur_cnav = Decimal(confirm_date_cnav_data.values[0]) + diff_nav
                 cnav_df.loc[confirm_share_date, cur_fund_id] = cur_cnav
 
         cnav_df = cnav_df.dropna(axis=0, how="all").fillna(method='ffill')

app/service/portfolio_diagnose.py

@@ -707,9 +707,12 @@ class PortfolioDiagnose(object):
         # 覆盖的基金名称
         strategy_name_evaluate = "".join(uniqe_strategy_name)[:-1]
 
-        if len(uniqe_strategy) / float(len(strategy_list)) > 0.6:
-            strategy_distribution_evaluate = "策略上有一定分散"
-        else:
+        try:
+            if len(uniqe_strategy) / float(len(strategy_list)) > 0.6:
+                strategy_distribution_evaluate = "策略上有一定分散"
+            else:
+                strategy_distribution_evaluate = "策略分散程度不高"
+        except ZeroDivisionError:
             strategy_distribution_evaluate = "策略分散程度不高"
         # 相关性
         if len(self.abandon_fund_corr) > 0:
@@ -749,7 +752,73 @@ class PortfolioDiagnose(object):
         ret = []
         for k, v in data.items():
             ret.append(sentence[k].format(*data[k]).replace("，；", "；"))
-        return ret
+
+        # 旧组合累积收益df
+        group_result_data = group_result[group_name]
+        hold_info = group_result_data["group_hoding_info"]
+        hold_info_df = pd.DataFrame(hold_info)
+        group_order_df = data_adaptor.user_customer_order_df[
+            data_adaptor.user_customer_order_df["folio_name"] == group_name]
+        group_order_start_date = pd.to_datetime(group_order_df["confirm_share_date"].min())
+
+        freq_max = group_order_df["freq"].max()
+        n_freq = freq_days(int(freq_max))
+
+        old_return_df = group_result_data["return_df"]
+        old_return_df["cum_return_ratio"] = old_return_df["cum_return_ratio"] - 1
+
+        # 原组合总市值, 区间收益, 年化收益,	波动率,	最大回撤, 夏普比率
+        total_asset = round(hold_info_df["market_values"].sum(), 2)
+        old_return = group_result_data["cumulative_return"]
+        old_return_ratio_year = group_result_data["return_ratio_year"]
+        old_volatility = group_result_data["volatility"]
+        old_max_drawdown = group_result_data["max_drawdown"]
+        old_sharpe = group_result_data["sharpe"]
+
+        # 指数收益
+        index_data = get_index_daily(self.index_id, self.start_date)
+        index_data = pd.merge(index_data, self.propose_portfolio, how='inner', left_index=True, right_index=True)
+        index_return = index_data.iloc[:, :] / index_data.iloc[0, :] - 1
+
+        # 指数收益
+        index_return = index_return[index_return.index >= group_order_start_date]
+        start_index_return = index_return[" close"].values[0]
+        index_return["new_index_return"] = (index_return[" close"] - start_index_return) / (1 + start_index_return)
+        index_return_ratio = index_return["new_index_return"].values[-1]
+        index_return_ratio_year = annual_return(index_return["new_index_return"].values[-1],
+                                                index_return["new_index_return"], n_freq)
+        index_volatility = volatility(index_return["new_index_return"] + 1, n_freq)
+        index_drawdown = max_drawdown(index_return["new_index_return"] + 1)
+        index_sim = simple_return(index_return["new_index_return"]+1)
+        index_exc = excess_return(index_sim, BANK_RATE, n_freq)
+        index_sharpe = sharpe_ratio(index_exc, index_sim, n_freq)
+
+        # 收益对比数据
+        return_compare_df = pd.merge(index_return[["new_index_return"]], old_return_df[["cum_return_ratio"]],
+                                     right_index=True,
+                                     left_index=True)
+        return_compare_df["date"] = return_compare_df.index
+        return_compare_df["date"] = return_compare_df["date"].apply(lambda x: x.strftime("%Y-%m-%d"))
+        return_compare_df.iloc[1:-1, :]["date"] = ""
+        old_return_compare_result = {
+
+            "index": {"name": "中证500", "data": return_compare_df["new_index_return"].values*100},
+            "origin_combination": {"name": "原组合", "data": return_compare_df["cum_return_ratio"].values*100},
+            "xlabels": return_compare_df["date"].values
+        }
+        # 指标对比
+        old_indicator = {"group_name": "现有持仓组合", "return_ratio": round((old_return - 1) * 100, 2),
+                         "return_ratio_year": round(old_return_ratio_year * 100, 2),
+                         "volatility": round(old_volatility * 100, 2),
+                         "max_drawdown": round(old_max_drawdown[0] * 100, 2), "sharpe": round(old_sharpe, 2)}
+
+        index_indicator = {"group_name": "中证500", "return_ratio": round(index_return_ratio * 100, 2),
+                           "return_ratio_year": round(index_return_ratio_year * 100, 2),
+                           "volatility": round(index_volatility * 100, 2),
+                           "max_drawdown": round(index_drawdown[0] * 100, 2), "sharpe": round(index_sharpe, 2)}
+        old_indicator_compare = [old_indicator, index_indicator]
+
+        return ret, old_return_compare_result, old_indicator_compare
 
     def new_evaluation(self, group_name, group_result, data_adaptor):
         try:
@@ -855,7 +924,7 @@ class PortfolioDiagnose(object):
             index_return_ratio_year = annual_return(index_return["new_index_return"].values[-1], index_return["new_index_return"], n_freq)
             index_volatility = volatility(index_return["new_index_return"]+1, n_freq)
             index_drawdown = max_drawdown(index_return["new_index_return"]+1)
-            index_sim = simple_return(propose_fund_return_limit_data["new_return"]+1)
+            index_sim = simple_return(index_return["new_index_return"]+1)
             index_exc = excess_return(index_sim, BANK_RATE, n_freq)
             index_sharpe = sharpe_ratio(index_exc, index_sim, n_freq)
 
@@ -868,9 +937,9 @@ class PortfolioDiagnose(object):
             return_compare_df["date"] = return_compare_df["date"].apply(lambda x: x.strftime("%Y-%m-%d"))
             return_compare_df.iloc[1:-1,:]["date"] = ""
             return_compare_result = {
-                "new_combination": {"name": "新组合", "data": return_compare_df["new_return"].values},
-                "index": {"name": "中证500", "data": return_compare_df["new_index_return"].values},
-                "origin_combination": {"name": "原组合", "data": return_compare_df["cum_return_ratio"].values},
+                "new_combination": {"name": "新组合", "data": return_compare_df["new_return"].values*100},
+                "index": {"name": "中证500", "data": return_compare_df["new_index_return"].values*100},
+                "origin_combination": {"name": "原组合", "data": return_compare_df["cum_return_ratio"].values*100},
                 "xlabels": return_compare_df["date"].values
             }
 

app/service/result_service_v2.py

@@ -369,8 +369,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             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["weight"] = round(float(row["confirm_amount"]) / total_market_values * 100, 2)      # 月末占比
             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
             # 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()

app/templates/v2/monthReportV2.html

@@ -886,7 +886,11 @@
     }
 
     .financial_show .financial_show_right .financial_team .financial_team_img {
-      width: 100%;
+      /*width: 100%;*/
+      /*height: 100%;*/
+      /*-o-object-fit: cover;*/
+      /*object-fit: cover;*/
+      width: auto;
       height: 100%;
       -o-object-fit: cover;
       object-fit: cover;
@@ -1363,20 +1367,20 @@
               <tr>
 
               <tr class="yellow">
-                <td>{{indicator_compare[1]["group_name"]}}</td>
-                <td>{{indicator_compare[1]["return_ratio"]}}</td>
-                <td>{{indicator_compare[1]["return_ratio_year"]}}</td>
-                <td>{{indicator_compare[1]["volatility"]}}</td>
-                <td>{{indicator_compare[1]["max_drawdown"]}}</td>
-                <td>{{indicator_compare[1]["sharpe"]}}</td>
+                <td>{{old_indicator_compare[0]["group_name"]}}</td>
+                <td>{{old_indicator_compare[0]["return_ratio"]}}</td>
+                <td>{{old_indicator_compare[0]["return_ratio_year"]}}</td>
+                <td>{{old_indicator_compare[0]["volatility"]}}</td>
+                <td>{{old_indicator_compare[0]["max_drawdown"]}}</td>
+                <td>{{old_indicator_compare[0]["sharpe"]}}</td>
               </tr>
               <tr >
-                <td>{{indicator_compare[2]["group_name"]}}</td>
-                <td>{{indicator_compare[2]["return_ratio"]}}</td>
-                <td>{{indicator_compare[2]["return_ratio_year"]}}</td>
-                <td>{{indicator_compare[2]["volatility"]}}</td>
-                <td>{{indicator_compare[2]["max_drawdown"]}}</td>
-                <td>{{indicator_compare[2]["sharpe"]}}</td>
+                <td>{{old_indicator_compare[1]["group_name"]}}</td>
+                <td>{{old_indicator_compare[1]["return_ratio"]}}</td>
+                <td>{{old_indicator_compare[1]["return_ratio_year"]}}</td>
+                <td>{{old_indicator_compare[1]["volatility"]}}</td>
+                <td>{{old_indicator_compare[1]["max_drawdown"]}}</td>
+                <td>{{old_indicator_compare[1]["sharpe"]}}</td>
               </tr>
 
           </table>
@@ -1559,12 +1563,12 @@
                 <th>基金简称</th>
                 <th>买入时间</th>
                 <th>存续年数</th>
-                <th>投资本金 (万元)</th>
-                <th>当月收益 (万元)</th>
+                <th>投资本金 (万)</th>
+                <th>当月收益 (万)</th>
                 <th>当月收益率 (%)</th>
-                <th>月末市值 (万元)</th>
+                <th>月末市值 (万)</th>
                 <th>月末占比 (%)</th>
-                <th>累计收益 (万元)</th>
+                <th>累计收益 (万)</th>
                 <th>累计收益率 (%)</th>
                 <th>年化收益率 (%)</th>
               </tr>

app/utils/draw.py

@@ -84,7 +84,7 @@ def draw_month_return_chart(xlabels, product_list, cumulative):
     ax2.plot(loc, cumulative['data'], color='#D40000', marker='.', linewidth=3, label=cumulative['name'])
     # 添加数字标签
     for a, b in zip(range(len(xlabels)), cumulative['data']):
-        ax2.text(a+0.2, b + 0.1, '%.2f' % b + '%', ha='center', va='bottom', fontsize=fontsize, color='#D40000')
+        ax2.text(a+0.1, b + 0.1, '%.2f' % b + '%', ha='center', va='bottom', fontsize=fontsize, color='#D40000')
     ax2.legend(loc='upper center', fontsize=fontsize)
 
     # plt.show()
@@ -117,6 +117,7 @@ def draw_contribution_chart(xlabels, product_list, cumulative):
     ax1.set_xticks(loc)
     ax1.set_xticklabels(xlabels)
     ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
+    ax1.grid(axis='y')
     # temp_rate = np.zeros(max_x_count)
     # for i in range(len(product_list)):
     #     temp_rate += product_list[i]['data']
@@ -143,6 +144,9 @@ def draw_contribution_chart(xlabels, product_list, cumulative):
 
     # 画折线图
     ax2.plot(loc, cumulative['data'], color='#B40A15', marker='', linewidth=3, label=cumulative['name'])
+    # 添加数字标签
+    for a, b in zip(range(len(xlabels)), cumulative['data']):
+        ax2.text(a + 0.2, b + 0.1, '%.2f' % b + '%', ha='center', va='bottom', fontsize=fontsize, color='#B40A15')
     ax2.legend(loc='upper left', fontsize=fontsize)
 
     imgdata = BytesIO()

app/utils/jinjia2html.py

@@ -37,20 +37,20 @@ class DataIntegrate:
         self.comments_on_position_portfolio()
         # 贡献分解
         self.contribution_deco()
-        # 个基点评
-        self.single_fund_comment()
-        # 旧相关性
-        self.get_old_correlation()
-        # 新相关性
-        self.get_new_correlation()
-        # 新增基金
-        self.propose_fund()
         # 目标与业绩
         self.objectives_performance(self.group_result)
+        # 个基点评
+        self.single_fund_comment()
         # 旧收益比较
         self.get_old_compare_pic()
-        # 新收益比较
-        self.get_transfer_suggestions()
+        # 旧相关性
+        self.get_old_correlation()
+        # # 新增基金
+        # self.propose_fund()
+        # # 新收益比较
+        # self.get_transfer_suggestions()
+        # # 新相关性
+        # self.get_new_correlation()
         # 渲染模版
         self.render_data()
 
@@ -82,8 +82,8 @@ class DataIntegrate:
         self.monthly_table_return = self.df["month_return_data_dict"]
 
     def comments_on_position_portfolio(self):
-        """旧持仓组合点评."""
-        self.old_evaluation = self.portfolio_diagnose.old_evaluation('default', self.d, self.user_customer)
+        """旧持仓组合点评. 旧贡献分解数据"""
+        self.old_evaluation, self.old_return_compare_data, self.old_indicator_compare = self.portfolio_diagnose.old_evaluation('default', self.d, self.user_customer)
 
     def contribution_deco(self):
         """贡献分解."""
@@ -116,12 +116,14 @@ class DataIntegrate:
 
     def get_old_compare_pic(self):
         """旧收益比较"""
-        self.suggestions_result, self.suggestions_result_asset, self.return_compare_data,\
-        self.indicator_compare, self.new_group_evaluation = self.portfolio_diagnose.new_evaluation("default", self.d, self.user_customer)
-        self.old_return_compare_pic = draw_old_combination_chart(self.return_compare_data["xlabels"], self.return_compare_data["origin_combination"], self.return_compare_data["index"])
+        self.old_return_compare_pic = draw_old_combination_chart(self.old_return_compare_data["xlabels"], self.old_return_compare_data["origin_combination"], self.old_return_compare_data["index"])
 
     def get_transfer_suggestions(self):
         """新收益比较，调仓建议"""
+        self.suggestions_result, self.suggestions_result_asset, self.return_compare_data, \
+        self.indicator_compare, self.new_group_evaluation = self.portfolio_diagnose.new_evaluation("default", self.d,
+                                                                                                   self.user_customer)
+
         self.return_compare_pic = draw_combination_chart(self.return_compare_data["xlabels"], self.return_compare_data["new_combination"],
                                                          self.return_compare_data["origin_combination"], self.return_compare_data["index"])
 
@@ -199,9 +201,9 @@ class DataIntegrate:
                 # 个基点评
                 'box5': 'block',
                 # 优化组合建议
-                'box6': 'block',
+                'box6': None,
                 # 新增基金
-                'box7': 'block',
+                'box7': None,
                 # 结尾
                 'box8': 'block',
                 'cover_back': template_folder + '/v2/img/cover-back.png',
@@ -242,17 +244,25 @@ class DataIntegrate:
                 'group_hoding_info': self.group_hoding_info,
                 'group_hoding_info_total': self.group_hoding_info_total,
                 'old_evaluation': self.old_evaluation,
+                'old_indicator_compare': self.old_indicator_compare,
                 'contribution_decomposition': self.contribution_decomposition,
                 'single_fund_data_list': self.single_fund_data_list,
                 'old_correlation': self.old_correlation,
-                'new_correlation': self.new_correlation,
-                'propose_fund_data_list': self.propose_fund_data_list,
-                'suggestions_result': self.suggestions_result,
-                'suggestions_result_asset': self.suggestions_result_asset,
                 'old_return_compare_pic': self.old_return_compare_pic,
-                'return_compare_pic': self.return_compare_pic,
-                'indicator_compare': self.indicator_compare,
-                'new_group_evaluation': self.new_group_evaluation
+                # 'new_correlation': self.new_correlation,
+                # 'propose_fund_data_list': self.propose_fund_data_list,
+                # 'suggestions_result': self.suggestions_result,
+                # 'suggestions_result_asset': self.suggestions_result_asset,
+                # 'return_compare_pic': self.return_compare_pic,
+                # 'indicator_compare': self.indicator_compare,
+                # 'new_group_evaluation': self.new_group_evaluation
+                'new_correlation': [],
+                'propose_fund_data_list': [],
+                'suggestions_result': {},
+                'suggestions_result_asset': {},
+                'return_compare_pic': [],
+                'indicator_compare': [],
+                'new_group_evaluation': []
 
                 }
         # 开始渲染html模板

app/utils/timed_task.py

@@ -8,7 +8,7 @@
 
 from app.api.engine import tamp_user_engine, tamp_order_engine, TAMP_SQL
 from sqlalchemy.ext.declarative import declarative_base
-from sqlalchemy import Column, String, Integer
+from sqlalchemy import Column, String, Integer, and_
 from app.utils.jinjia2html import DataIntegrate
 
 Base = declarative_base()
@@ -36,26 +36,26 @@ class Customer(Base):
 
 
 def call_month_report(ifauser_id, ifauser_name, customer_id, customer_name):
-    print('开始制作IFA: {}, 客户: {}的基金月报'.format(ifauser_name, customer_name))
-    try:
-        DataIntegrate(ifauser_id, customer_id, '{}_{}_.pdf'.format(ifauser_name, customer_name))
-    except:
-        print("发生异常，下一个走起！")
+    # print('开始制作IFA: {}, 客户: {}的基金月报'.format(ifauser_name, customer_name))
+    # try:
+    DataIntegrate(ifauser_id, customer_id, '{}_{}_.pdf'.format(ifauser_name, customer_name))
+    # except IndexError:
+    #     print("发生异常，下一个走起！")
 
 
 
 with TAMP_SQL(tamp_user_engine) as tamp_user, TAMP_SQL(tamp_order_engine) as tamp_order:
     tamp_user_session = tamp_user.session
     # 获取所有 ifauser
-    ifausers = tamp_user_session.query(IfaUser).all()
+    ifausers = tamp_user_session.query(IfaUser).filter(IfaUser.id.in_(['USER_INFO15916072577875'])).all()
     
     tamp_order_session = tamp_order.session
     # for ifauser in ifausers:
     for ifauser in ifausers:
         # 获取某个 ifa 的所有 customer
         customers = tamp_order_session.query(Ifa_Customer).\
-                        filter_by(ifa_id = ifauser.id).all()
-                        
+                        filter(and_(Ifa_Customer.ifa_id == ifauser.id, Ifa_Customer.customer_id.in_(['6716613804966817792']))).all()
+
         # 循环这个 ifa 的所有 customer
         for customer in customers:
             customer_full = tamp_order_session.query(Customer).\