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

# Conflicts:
#	app/service/portfolio_diagnose.py

app/service/data_service.py

@@ -35,10 +35,10 @@ class UserCustomerDataAdaptor:
         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)
         self.end_date = pd.to_datetime(str(p_end_date))
-        # self.end_date = pd.to_datetime("2020-11-23")
+        # self.end_date = pd.to_datetime("2020-12-04")
         p_start_date = datetime.date(year=p_end_date.year, month=p_end_date.month, day=1)
         self.month_start_date = p_start_date
-        # self.month_start_date = pd.to_datetime("2020-11-16")
+        # self.month_start_date = pd.to_datetime("2020-11-01")
         self.user_customer_order_df = self.get_user_customer_order_data()
         self.fund_nav_total, self.fund_cnav_total = self.get_customer_fund_nav_data()
         self.index_df = self.get_customer_index_nav_data()
@@ -68,10 +68,10 @@ class UserCustomerDataAdaptor:
         with TAMP_SQL(tamp_user_engine) as tamp_user, TAMP_SQL(tamp_product_engine) as tamp_product:
             tamp_user_session = tamp_user.session
             tamp_product_session = tamp_product.session
-            sql_user = """select f2.realname,f3.customer_name,fund_id,f1.order_type,f1.pay_date,f1.subscription_fee,f1.confirm_share_date,f1.confirm_share,f1.confirm_amount,f1.nav,f1.folio_name from customer_order f1, user_info f2,customer_info f3   where f2.id=f1.user_id and f3.id=f1.customer_id and f1.user_id='{}' and f1.customer_id='{}'""".format(self.user_id, self.customer_id)
+            sql_user = """select f1.id, f2.realname,f3.customer_name,fund_id,f1.order_type,f1.pay_date,f1.subscription_fee,f1.confirm_share_date,f1.confirm_share,f1.confirm_amount,f1.nav,f1.folio_name from customer_order f1, user_info f2,customer_info f3   where f2.id=f1.user_id and f3.id=f1.customer_id and f1.user_id='{}' and f1.customer_id='{}'""".format(self.user_id, self.customer_id)
             cur = tamp_user_session.execute(sql_user)
             data = cur.fetchall()
-            order_df = pd.DataFrame(list(data), columns=['username', 'customer_name', 'fund_id', 'order_type', 'pay_date',
+            order_df = pd.DataFrame(list(data), columns=['order_id', 'username', 'customer_name', 'fund_id', 'order_type', 'pay_date',
                                                          'subscription_fee', 'confirm_share_date', 'confirm_share',
                                                          'confirm_amount', 'nav', 'folio_name'])
 
@@ -122,7 +122,7 @@ class UserCustomerDataAdaptor:
                 cur_fund_nav_df["price_date"] = pd.to_datetime(cur_fund_nav_df["price_date"])
                 cur_fund_nav_df.drop_duplicates(subset="price_date", keep='first', inplace=True)
                 cur_fund_nav_df.set_index("price_date", inplace=True)
-                cur_fund_nav_df = cur_fund_nav_df[cur_fund_nav_df.index.isin(all_fund_nav.index & cur_fund_nav_df.index)]
+                cur_fund_nav_df = cur_fund_nav_df[cur_fund_nav_df.index.isin(all_fund_nav.index)]
                 all_fund_nav[cur_fund_id] = cur_fund_nav_df["nav"]
                 all_fund_cnav[cur_fund_id] = cur_fund_nav_df["cnav"]
 
@@ -186,6 +186,12 @@ class UserCustomerDataAdaptor:
                     diff_nav = row["nav"] - last_nav_data.values[0]
                     cur_cnav = last_cnav_data.values[0] + diff_nav
                     cnav_df.loc[confirm_share_date, cur_fund_id] = cur_cnav
+            else:
+                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 = row["nav"] - confirm_date_nav_data.values[0]
+                cur_cnav = 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')
         for index, row in p_order_df.iterrows():
@@ -237,6 +243,8 @@ class UserCustomerDataAdaptor:
             cnav_df[p_fund_id_ + "_cum_earn"] = cnav_df[p_fund_id_ + "_earn"].cumsum().fillna(0)
             cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_cum_earn"].apply(lambda x: Decimal(x)) + cnav_df[p_fund_id_ + "_amount"]
             # cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_share"] * cnav_df[p_fund_id_]
+            # cnav_df[p_fund_id_ + "_profit_ratio"] = cnav_df[p_fund_id_ + "_earn"].apply(lambda x: Decimal(x)) / cnav_df[
+            #     p_fund_id_ + "_net_amount"].shift()
         self.group_data[p_folio] = {"result_cnav_data": cnav_df, "order_df": p_order_df}
         return cnav_df
 
@@ -273,6 +281,12 @@ class UserCustomerDataAdaptor:
                     diff_nav = row["nav"] - last_nav_data.values[0]
                     cur_cnav = last_cnav_data.values[0] + diff_nav
                     cnav_df.loc[confirm_share_date, cur_fund_id] = cur_cnav
+            else:
+                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 = row["nav"] - confirm_date_nav_data.values[0]
+                cur_cnav = 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')
         for index, row in p_order_df.iterrows():
@@ -314,5 +328,8 @@ class UserCustomerDataAdaptor:
             # cnav_df[p_fund_id_ + "_earn"] = cnav_df[p_fund_id_ + "_earn"].apply(lambda x: float(x))
             cnav_df[p_fund_id_ + "_cum_earn"] = cnav_df[p_fund_id_ + "_earn"].cumsum().fillna(0)
             cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_cum_earn"].apply(lambda x: Decimal(x)) + cnav_df[p_fund_id_ + "_amount"]
+
+            # cnav_df[p_fund_id_ + "_profit_ratio"] = cnav_df[p_fund_id_ + "_earn"].apply(lambda x: Decimal(x)) / cnav_df[
+            #     p_fund_id_ + "_net_amount"].shift()
             # cnav_df[p_fund_id_ + "_net_amount"] = cnav_df[p_fund_id_ + "_share"] * cnav_df[p_fund_id_]
         return cnav_df

app/service/portfolio_diagnose.py

@@ -139,7 +139,6 @@ def choose_bad_evaluation(evaluation):
     if v1[0] < 2:
         del evaluation[1]
     if v2[0] < 2:
-        # if v2:
         del evaluation[2]
     if v3[0] < 2:
         del evaluation[3]
@@ -473,7 +472,7 @@ class PortfolioDiagnose(object):
             elif np.any(self.old_correlation[fund] > 0.8):
                 self.abandon_fund_corr.append(fund)
 
-        prod.drop(self.abandon_fund_score + self.abandon_fund_corr, axis=1, inplace=True)
+        prod = prod.drop(self.abandon_fund_score + self.abandon_fund_corr, axis=1)
         self.old_correlation = self.old_correlation.fillna(1).round(2)
         self.old_correlation.columns = self.old_correlation.columns.map(lambda x: get_fund_name(x).values[0][0])
         self.old_correlation.index = self.old_correlation.index.map(lambda x: get_fund_name(x).values[0][0])
@@ -488,6 +487,8 @@ class PortfolioDiagnose(object):
         Returns: 增加建议申购基金的组合净值表
 
         """
+        # 组合内已包含的策略
+        # included_strategy = set()
         # 按每种基金最少投资100w确定组合包含的最大基金数量
         max_len = len(self.portfolio) - len(prod.columns)
 
@@ -778,11 +779,9 @@ class PortfolioDiagnose(object):
         # 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[int(propose_fund_df[propose_fund_df["fund_id"] == fund_id]["substrategy"].values[0])] for
+        propose_fund_id_strategy_name_list = [dict_substrategy[int(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_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],
@@ -824,25 +823,23 @@ class PortfolioDiagnose(object):
         # 新组合累积收益df
         propose_fund_return_limit_data = propose_fund_return[propose_fund_return.index >= group_order_start_date]
         start_return = propose_fund_return_limit_data['return'].values[0]
-        propose_fund_return_limit_data["new_return"] = (propose_fund_return_limit_data["return"] - start_return) / (
-                    1 + start_return)
+        propose_fund_return_limit_data["new_return"] = (propose_fund_return_limit_data["return"] - start_return)/(1+start_return)
 
         # 新组合累积收益
         new_return_ratio = propose_fund_return_limit_data["new_return"].values[-1]
-
         # 新组合区间年化收益率
         freq_max = group_order_df["freq"].max()
         n_freq = freq_days(int(freq_max))
-        new_return_ratio_year = annual_return(new_return_ratio, propose_fund_return_limit_data, n_freq)
+        new_return_ratio_year = annual_return(propose_fund_return_limit_data["new_return"].values[-1], propose_fund_return_limit_data, n_freq)
 
         # 新组合波动率
-        new_volatility = volatility(propose_fund_return_limit_data["new_return"] + 1, n_freq)
+        new_volatility = volatility(propose_fund_return_limit_data["new_return"]+1, n_freq)
 
         # 新组合最大回撤
-        new_drawdown = max_drawdown(propose_fund_return_limit_data["new_return"] + 1)
+        new_drawdown = max_drawdown(propose_fund_return_limit_data["new_return"]+1)
 
         # 新组合夏普比率
-        sim = simple_return(propose_fund_return_limit_data["new_return"] + 1)
+        sim = simple_return(propose_fund_return_limit_data["new_return"]+1)
         exc = excess_return(sim, BANK_RATE, n_freq)
         new_sharpe = sharpe_ratio(exc, sim, n_freq)
 
@@ -851,23 +848,21 @@ class PortfolioDiagnose(object):
         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(propose_fund_return_limit_data["new_return"] + 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(propose_fund_return_limit_data["new_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,
+        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 = pd.merge(return_compare_df, propose_fund_return_limit_data["new_return"], 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"] = ""
+        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},
@@ -876,20 +871,15 @@ class PortfolioDiagnose(object):
         }
 
         # 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)}
-        new_indicator = {"group_name": "建议优化组合", "return_ratio": round(new_return_ratio * 100, 2),
-                         "return_ratio_year": round(new_return_ratio_year * 100, 2),
-                         "volatility": round(new_volatility * 100, 2), "max_drawdown": round(new_drawdown[0] * 100, 2),
-                         "sharpe": round(new_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 = {"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)}
+        new_indicator = {"group_name": "建议优化组合", "return_ratio": round(new_return_ratio*100, 2), "return_ratio_year": round(new_return_ratio_year*100, 2),
+                         "volatility": round(new_volatility*100, 2), "max_drawdown": round(new_drawdown[0]*100, 2), "sharpe": round(new_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)}
         indicator_compare = [new_indicator, old_indicator, index_indicator]
 
+
         # 在保留{}的基础上，建议赎回{}，并增配{}后，整体组合波动率大幅降低，最大回撤从{}降到不足{}，年化收益率提升{}个点
         hold_fund = set(self.portfolio) - set(self.abandon_fund_score + self.abandon_fund_corr + self.no_data_fund)
         hold_fund_name = [get_fund_name(x).values[0][0] for x in hold_fund]
@@ -1076,13 +1066,14 @@ class PortfolioDiagnose(object):
             radar_data.append(get_radar_data(fund))
         return radar_data
 
-
-# test_portfolio = ['HF00002JJ2', 'HF00005DBQ', 'HF0000681Q', 'HF00006693', 'HF00006AZF', 'HF00006BGS']
-# portfolio_diagnose = PortfolioDiagnose(client_type=3, portfolio=test_portfolio, invest_amount=10000000)
+#
+# portfolio = ['HF00002JJ2', 'HF00005DBQ', 'HF0000681Q', 'HF00006693', 'HF00006AZF', 'HF00006BGS']
+# portfolio_diagnose = PortfolioDiagnose(client_type=1, portfolio=portfolio, invest_amount=10000000)
 # portfolio_diagnose.optimize()
 # if __name__ == '__main__':
 #     print(portfolio_diagnose.single_fund_radar())
 #     print(portfolio_diagnose.propose_fund_radar())
+#     print(portfolio_diagnose.old_portfolio_evaluation())
 #     print('旧组合相关性：', portfolio_diagnose.old_correlation)
 #     print('新组合相关性：', portfolio_diagnose.new_correlation)
 #     print('旧组合个基评价：', portfolio_diagnose.old_portfolio_evaluation())

app/service/result_service_v2.py

@@ -402,11 +402,21 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         # nav_net_amount_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
         nav_net_amount_df["sum_net_amount"] = nav_net_amount_df[fund_id_list_amount].sum(axis=1).apply(lambda x: Decimal.from_float(x))
         for amount_name in fund_id_list:
+            price = nav_net_amount_df[amount_name].dropna()
+            profit = price.diff().fillna(Decimal(0))
+            profit_ratio_new = profit / price.shift(1)
+            profit_ratio_old = nav_net_amount_df[amount_name+"_profit_ratio"]
+            nan_index = profit_ratio_new[pd.isna(profit_ratio_new)].index
+            profit_ratio_new[nan_index] = profit_ratio_old[nan_index]
+            nav_net_amount_df[amount_name + "_profit_ratio"] = profit_ratio_new
             nav_net_amount_df[amount_name+"_amount_ratio"] = nav_net_amount_df[amount_name+"_net_amount"]/(nav_net_amount_df["sum_net_amount"])
-            nav_net_amount_df[amount_name+"_profit_ratio_weight"] = nav_net_amount_df[amount_name+"_amount_ratio"].shift(1) * nav_net_amount_df[amount_name+"_profit_ratio"]
+
             fund_profit_ratio = nav_net_amount_df[amount_name + "_profit_ratio"].dropna() + 1
-            nav_net_amount_df[amount_name + "_profit_cum_ratio_weight"] = (fund_profit_ratio.cumprod()-1)*nav_net_amount_df[amount_name+"_amount_ratio"].shift(1)
-            # enter_date = nav_net_amount_df[amount_name+"_profit_ratio"].dropna()
+            amount_ratio_shift = nav_net_amount_df[amount_name + "_amount_ratio"].shift(1)
+            num_va = len(amount_ratio_shift[amount_ratio_shift.values==0])
+            amount_ratio_shift.iloc[num_va] = amount_ratio_shift.values[num_va+1]
+            nav_net_amount_df[amount_name + "_profit_ratio_weight"] =  amount_ratio_shift * nav_net_amount_df[amount_name + "_profit_ratio"]
+            nav_net_amount_df[amount_name + "_profit_cum_ratio_weight"] = (fund_profit_ratio.cumprod()-1)*amount_ratio_shift # enter_date = nav_net_amount_df[amount_name+"_profit_ratio"].dropna()
 
         fund_id_list_profit_ratio_weight = [i + "_profit_ratio_weight" for i in fund_id_list]
         nav_profit_ratio_weight = nav_net_amount_df[fund_id_list_profit_ratio_weight].copy().fillna(method='ffill')

app/templates/v2/monthReportV2.html

@@ -1037,27 +1037,55 @@
           <table class="tss1_table">
             <tr>
               <td>
+                {%if month_rise>=0%}
                 本月涨幅：<span class="red">{{month_rise}}%</span>
+                {%else%}
+                本月涨幅：<span class="green">{{month_rise}}%</span>
+                {%endif%}
               </td>
               <td>
+                {%if now_month_income>=0%}
+                本月收益：<span class="red">{{now_month_income}}元</span>
+                {%else%}
                 本月收益：<span class="green">{{now_month_income}}元</span>
+                {%endif%}
               </td>
               <td>
+                {%if year_totoal_rate_of_return>=0%}
                 今年累计收益率：<span class="red">{{year_totoal_rate_of_return}}%</span>
+                {%else%}
+                今年累计收益率：<span class="green">{{year_totoal_rate_of_return}}%</span>
+                {%endif%}
               </td>
               <td>
+                {%if now_year_income>=0%}
+                今年累计收益：<span class="red">{{now_year_income}}元</span>
+                {%else%}
                 今年累计收益：<span class="green">{{now_year_income}}元</span>
+                {%endif%}
               </td>
             </tr>
             <tr>
               <td>
+                {%if totoal_rate_of_return>=0%}
                 成立以来累计收益率：<span class="red">{{totoal_rate_of_return}}%</span>
+                {%else%}
+                成立以来累计收益率：<span class="green">{{totoal_rate_of_return}}%</span>
+                {%endif%}
               </td>
               <td>
+                {%if now_annualised_return>=0%}
+                年化收益率：<span class="red">{{now_annualised_return}}%</span>
+                {%else%}
                 年化收益率：<span class="green">{{now_annualised_return}}%</span>
+                {%endif%}
               </td>
               <td>
+                {%if now_withdrawal>=0%}
                 最大回撤：<span class="red">{{now_withdrawal}}%</span>
+                {%else%}
+                最大回撤：<span class="green">{{now_withdrawal}}%</span>
+                {%endif%}
               </td>
               <td>
               </td>
@@ -1076,10 +1104,20 @@
                 投资成本：<span class="red">{{now_allocation_amount}}元</span>
               </td>
               <td>
+
+                {%if final_balance>=now_allocation_amount%}
+                期末资产：<span class="red">{{final_balance}}元</span>
+                {%else%}
                 期末资产：<span class="green">{{final_balance}}元</span>
+                {%endif%}
               </td>
               <td>
+
+                {%if total_profit>=0%}
                 累计盈利：<span class="red">{{total_profit}}元</span>
+                {%else%}
+                累计盈利：<span class="green">{{total_profit}}元</span>
+                {%endif%}
               </td>
               <td>
               </td>
@@ -1296,7 +1334,7 @@
               <th>目标最大回撤(%)</th>
             </tr>
             <tr>
-              <td>2000</td>
+              <td>{{cost_of_investment}}</td>
               <td>5%-10%</td>
               <td>5%-10%</td>
             </tr>
@@ -1513,7 +1551,7 @@
             <table border="1">
               <tr>
                 <th colspan="5">投资本金</th>
-                <th colspan="4">10月业绩</th>
+                <th colspan="4">{{month}}月业绩</th>
                 <th colspan="3">累计业绩</th>
               </tr>
               <tr>

app/utils/jinjia2html.py

@@ -211,6 +211,7 @@ class DataIntegrate:
                 'customer_name': self.customer_name,
                 'customer_gender': '女',
                 'year_month': self.user_customer.month_start_date.strftime("%Y-%m"),
+                'month': self.user_customer.month_start_date.strftime("%m"),
                 'start_date': self.user_customer.start_date.strftime("%Y-%m-%d"),
                 'ifa_company': '飞度工作室',
                 'title': '10月综述',
@@ -273,5 +274,5 @@ class DataIntegrate:
 
 if __name__ == '__main__':
     start = time.time()
-    DataIntegrate()
+    DataIntegrate(ifa_id='USER_INFO15916072577875', customer_id='6716613804966817792')
     print('耗时{}秒'.format(round(time.time()-start, 2)))