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Commit 73060184 authored by 赵杰's avatar 赵杰
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新版本推荐基金，支持ifa导入基金

parent 0fecd195
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Showing with 245 additions and 192 deletions
app/service/data_service_v2_1.py
View file @ 73060184
......@@ -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/portfolio_diagnose.py
View file @ 73060184
This diff is collapsed.
app/service/result_service_v2.py
View file @ 73060184
......@@ -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
......@@ -134,6 +139,9 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
cur_profit_ratio = return_ratio_df.loc[cur_year_date:, "cum_return_ratio"].values
if len(last_profit_ratio) <= 0:
cur_year_profit_ratio = cur_profit_ratio[-1] - 1
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]
folio_report_data["cur_year_profit_ratio"] = float(cur_year_profit_ratio)
......@@ -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
......@@ -288,6 +301,12 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
cur_profit_ratio = return_ratio_df.loc[cur_year_date:, "cum_return_ratio"].values
if len(last_profit_ratio) <= 0:
cur_year_profit_ratio = cur_profit_ratio[-1] - 1
else:
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)
......@@ -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
View file @ 73060184
......@@ -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)
......
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