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fund_report
Commit b2db96ec authored by 赵杰's avatar 赵杰
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持仓报告修复数据

parent b2bd9f89
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Showing with 76 additions and 14 deletions
app/service/data_service_v2.py
View file @ b2db96ec
......@@ -106,11 +106,32 @@ class UserCustomerDataAdaptor:
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()))
self.start_date = user_customer_order_df["confirm_share_date"].min()
self.customer_real_name = user_customer_order_df["customer_name"].values[0]
self.ifa_real_name = user_customer_order_df["username"].values[0]
user_customer_order_df = user_customer_order_df[user_customer_order_df["confirm_share_date"] <= self.end_date]
user_customer_order_df["confirm_amount"] = user_customer_order_df["confirm_amount"] - user_customer_order_df["subscription_fee"]
user_customer_order_df.index = pd.Series(range(len(user_customer_order_df)))
for index, row in user_customer_order_df.iterrows():
if row["order_type"] == 2:
need_less_share = row["confirm_share"]
for index_ori, row_ori in user_customer_order_df.iterrows():
if index_ori >= index:
break
if row["fund_id"] == row_ori["fund_id"] and row_ori["order_type"] == 1:
ori_share = row_ori["confirm_share"]
if need_less_share >= ori_share:
need_less_share -= ori_share
user_customer_order_df.loc[index_ori, "confirm_share"] = 0
else:
need_less_share = 0
user_customer_order_df.loc[index_ori, "confirm_share"] = row_ori[
"confirm_share"] - need_less_share
if need_less_share <= 0:
break
user_customer_order_df = user_customer_order_df[(user_customer_order_df["order_type"] == 1)&(user_customer_order_df["confirm_share"] > 0)]
user_customer_order_df.index = pd.Series(range(len(user_customer_order_df)))
self.start_date = user_customer_order_df["confirm_share_date"].min()
return user_customer_order_df
# 获取客户持有的基金净值数据
......@@ -163,6 +184,8 @@ class UserCustomerDataAdaptor:
data = cur.fetchall()
cur_fund_performance_df = pd.DataFrame(list(data),
columns=['price_date', 'ret_1w', 'ret_cum_1m', 'ret_cum_6m', 'ret_cum_1y', 'ret_cum_ytd', 'ret_cum_incep'])
cur_fund_performance_df = cur_fund_performance_df[
cur_fund_performance_df["price_date"] <= self.end_date.date()]
self.all_fund_performance[cur_fund_id] = cur_fund_performance_df
cur_fund_nav_df["price_date"] = pd.to_datetime(cur_fund_nav_df["price_date"])
......
app/service/data_service_v2_1.py
View file @ b2db96ec
......@@ -105,11 +105,33 @@ class UserCustomerDataAdaptor:
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()
self.start_date = user_customer_order_df["confirm_share_date"].min()
user_customer_order_df["confirm_share_date"] = user_customer_order_df["confirm_share_date"].apply(lambda x: pd.to_datetime(x.date()))
self.customer_real_name = user_customer_order_df["customer_name"].values[0]
self.ifa_real_name = user_customer_order_df["username"].values[0]
user_customer_order_df = user_customer_order_df[user_customer_order_df["confirm_share_date"] <= self.end_date]
user_customer_order_df["confirm_amount"] = user_customer_order_df["confirm_amount"] - user_customer_order_df["subscription_fee"]
user_customer_order_df.index = pd.Series(range(len(user_customer_order_df)))
for index, row in user_customer_order_df.iterrows():
if row["order_type"] == 2:
need_less_share = row["confirm_share"]
for index_ori, row_ori in user_customer_order_df.iterrows():
if index_ori >= index:
break
if row["fund_id"] == row_ori["fund_id"] and row_ori["order_type"] == 1:
ori_share = row_ori["confirm_share"]
if need_less_share >= ori_share:
need_less_share -= ori_share
user_customer_order_df.loc[index_ori, "confirm_share"] = 0
else:
need_less_share = 0
user_customer_order_df.loc[index_ori, "confirm_share"] = row_ori[
"confirm_share"] - need_less_share
if need_less_share <= 0:
break
user_customer_order_df = user_customer_order_df[(user_customer_order_df["order_type"] == 1)&(user_customer_order_df["confirm_share"] > 0)]
user_customer_order_df.index = pd.Series(range(len(user_customer_order_df)))
self.start_date = user_customer_order_df["confirm_share_date"].min()
return user_customer_order_df
# 获取客户持有的基金净值数据
......@@ -162,6 +184,8 @@ class UserCustomerDataAdaptor:
data = cur.fetchall()
cur_fund_performance_df = pd.DataFrame(list(data),
columns=['price_date', 'ret_1w', 'ret_cum_1m', 'ret_cum_6m', 'ret_cum_1y', 'ret_cum_ytd', 'ret_cum_incep'])
cur_fund_performance_df = cur_fund_performance_df[
cur_fund_performance_df["price_date"] <= self.end_date.date()]
self.all_fund_performance[cur_fund_id] = cur_fund_performance_df
cur_fund_nav_df["price_date"] = pd.to_datetime(cur_fund_nav_df["price_date"])
......
app/service/result_service_v2.py
View file @ b2db96ec
......@@ -63,8 +63,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
"cumulative": cumulative_data}
# 总成本
total_cost = float(cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_amount"].sum() - \
cur_folio_order_data[cur_folio_order_data["order_type"] == 2]["confirm_amount"].sum())
total_cost = round(float((cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_share"]*cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["nav"]).sum()), 2)
folio_report_data["total_cost"] = total_cost
# 累积盈利
......@@ -104,7 +103,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
# 本月累积收益率
last_profit_ratio = return_ratio_df.loc[:self.month_start_date, "cum_return_ratio"].values
cur_profit_ratio = return_ratio_df.loc[self.month_start_date:, "cum_return_ratio"].values
cur_profit_ratio = return_ratio_df.loc[self.month_start_date - datetime.timedelta(days=1):, "cum_return_ratio"].values
if len(last_profit_ratio) <= 0:
cur_month_profit_ratio = cur_profit_ratio[-1] - 1
else:
......@@ -215,8 +214,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
resample_df = resample_df[resample_df.index <= self.end_date]
# 总成本
total_cost = float(cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_amount"].sum() - \
cur_folio_order_data[cur_folio_order_data["order_type"] == 2]["confirm_amount"].sum())
total_cost = round(float((cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_share"]*cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["nav"]).sum()), 2)
report_data["total_cost"] = total_cost
# 累积盈利
......@@ -350,6 +348,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
n_freq = freq_days(int(freq_max))
resample_df = resample(p_result_cnav_data, self.trade_cal_date, freq_max)
for index, row in p_order_df.iterrows():
if row['order_type'] == 2 or row["confirm_share"] <= 0:
continue
cur_fund_id = str(row["fund_id"])
cur_fund_performance = self.all_fund_performance[cur_fund_id]
if len(cur_fund_performance) <=0:
......@@ -410,24 +410,37 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
fund_hoding_info["confirm_date"] = row["confirm_share_date"].strftime("%Y-%m-%d")
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)
temp_market_values = float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav) + float(row["confirm_amount"])
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 row['confirm_share_date'] > self.month_start_date:
cal_month_start_date = row['confirm_share_date']
last_month_cnav_serise = p_result_cnav_data[p_result_cnav_data.index == pd.to_datetime(cal_month_start_date)][
row["fund_id"]].dropna()
else:
cal_month_start_date = self.month_start_date - datetime.timedelta(days=1)
last_month_cnav_serise = p_result_cnav_data[p_result_cnav_data.index<pd.to_datetime(cal_month_start_date)][row["fund_id"]].dropna()
if len(last_month_cnav_serise) == 0:
fund_hoding_info["profit"] = round(float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav) / 10000, 2)
temp_profit = float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav)
temp_profit_ratio = (fund_basic_info["cur_cnav"] - confirm_cnav)/confirm_cnav
else:
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)
temp_profit = float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - last_month_cnav)
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(float(fund_hoding_info["profit"]) / float(fund_hoding_info["market_values"])*100, 2)
# 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_ratio * 100, 2)
# 累积收益
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"] = "%.2f" % round((fund_basic_info["cur_cnav"] - confirm_cnav)/confirm_cnav*100, 2)
cum_profit_ratio_temp = (fund_basic_info["cur_cnav"] - confirm_cnav) / confirm_cnav
# 累积年化收益率
cur_resample_df = resample_df[[row["fund_id"]]].dropna()
return_ratio_year = annual_return(float(fund_hoding_info["cum_profit_ratio"])/100.0, cur_resample_df, n_freq)
return_ratio_year = annual_return(float(cum_profit_ratio_temp), 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
View file @ b2db96ec
......@@ -1046,7 +1046,7 @@
{%endif%}
</td>
<td>
{%if (now_month_income| float) >=0%}
{%if (now_month_income_temp| float) >=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>
......@@ -1060,7 +1060,7 @@
{%endif%}
</td>
<td>
{%if (now_year_income|float)>=0%}
{%if (now_year_income_temp|float)>=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>
......@@ -1113,7 +1113,7 @@
</td>
<td>
{%if (total_profit|float)>=0%}
{%if (total_profit_temp|float)>=0%}
累计盈利：<span class="red">{{total_profit}}元</span>
{%else%}
累计盈利：<span class="green">{{total_profit}}元</span>
......
app/utils/jinjia2html_v2.py
View file @ b2db96ec
......@@ -264,6 +264,8 @@ class DataIntegrate:
'now_withdrawal': self.now_withdrawal, 'index_withdrawal': self.index_withdrawal, 'expected_withdrawal': 20,
'now_year_income': '{:,}'.format(self.now_year_income), 'now_month_income': '{:,}'.format(self.now_month_income),
'final_balance': '{:,}'.format(self.final_balance), 'total_profit': '{:,}'.format(self.total_profit),
'total_profit_temp': self.total_profit,
'now_year_income_temp': self.now_year_income, 'now_month_income_temp': self.now_month_income,
'monthly_return_performance_pic': self.monthly_return_performance_pic,
'month_rise': self.month_rise, 'year_totoal_rate_of_return': self.year_totoal_rate_of_return,
......@@ -329,6 +331,6 @@ class DataIntegrate:
if __name__ == '__main__':
start = time.time()
dt = DataIntegrate(ifa_id='USER_INFO15916072577875', customer_id='USER_INFO15917853924996')
dt = DataIntegrate(ifa_id='212498904817250304', customer_id='CUSTOMER16086497662396')
dt.render_data()
print('耗时{}秒'.format(round(time.time()-start, 2)))
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