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fund_report
Commit 03fa8113 authored by 赵杰's avatar 赵杰
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内外部订单隔离计算分红订单收益与成本

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app/service/result_service_v2.py
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......@@ -14,6 +14,7 @@ from app.service.data_service_v2_1 import UserCustomerDataAdaptor
from app.utils.fund_rank import get_frequency
from app.service.portfolio_diagnose import cal_correlation, get_fund_name, get_frequency
from app.utils.week_evaluation import *
from app.service.substrategy_dict import get_substrategy_name
class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
......@@ -65,7 +66,6 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
resample_df.set_index("index_date", inplace=True)
resample_df = resample_df[resample_df.index <= self.end_date]
# 收益分解df
contribution_decomposition_df = contribution_decomposition.fillna(0)*100
p_plot_data = []
......@@ -79,7 +79,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
"cumulative": cumulative_data}
# 总成本
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()), 0)
total_cost = round(float(round(float(cur_folio_result_cnav_data[[i + "_amount" for i in fund_id_list]].tail(1).sum().sum()), 0)), 0)
folio_report_data["total_cost"] = total_cost
# 累积盈利
......@@ -109,8 +109,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
folio_report_data["sharpe"] = float(sharpe) if not math.isnan(sharpe) else 0.0
# 期末资产
ending_assets = cumulative_profit + total_cost
folio_report_data["ending_assets"] = float(ending_assets)
ending_assets = round(float(cur_folio_result_cnav_data[[i + "_net_amount" for i in fund_id_list]].tail(1).sum().sum()), 0)
folio_report_data["ending_assets"] = ending_assets
# 本月收益
cur_month_profit_df = profit_df.loc[self.month_start_date:self.end_date+datetime.timedelta(days=1), fund_id_list_earn]
......@@ -250,7 +250,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
resample_df = resample_df[resample_df.index <= self.end_date]
# 总成本
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()), 0)
total_cost = round(float(cur_folio_result_cnav_data[[i + "_amount" for i in fund_id_list]].tail(1).sum().sum()), 0)
report_data["total_cost"] = total_cost
# 累积盈利
......@@ -277,8 +277,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
# report_data["sharpe"] = float(sharpe)
# 期末资产
ending_assets = cumulative_profit + total_cost
report_data["ending_assets"] = float(ending_assets)
ending_assets = round(float(cur_folio_result_cnav_data[[i + "_net_amount" for i in fund_id_list]].tail(1).sum().sum()), 0)
report_data["ending_assets"] = ending_assets
# 本月收益
cur_month_profit_df = profit_df.loc[self.month_start_date:self.end_date + datetime.timedelta(days=1),
......@@ -377,8 +377,6 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
index_result = self.signal_fund_profit_result(index_df[index_df.index >= pd.to_datetime(first_trade_date)], "index")
report_data["index_result"] = index_result
# self.__month_return(cur_folio_result_cnav_data, fund_id_list)
self.total_result_data = report_data
return report_data
......@@ -389,12 +387,14 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
freq_max = p_order_df["freq"].max()
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
# for index, row in p_order_df.iterrows():
for hold_fund_id in p_order_df["fund_id"].unique():
order = p_order_df[(p_order_df["fund_id"] == hold_fund_id)]
row = order[order["order_type"] == 1].iloc[0]
cur_fund_id = str(row["fund_id"])
cur_fund_performance = self.all_fund_performance[cur_fund_id]
if len(cur_fund_performance) <=0:
if len(cur_fund_performance) <= 0:
fund_basic_info = {"fund_name": row["fund_name"], "confirm_nav": round(row["nav"], 4)}
fund_basic_info["cur_nav"] = round(float(self.fund_nav_total[cur_fund_id].dropna().values[-1]), 4)
fund_basic_info["cur_cnav"] = round(float(self.fund_cnav_total[cur_fund_id].dropna().values[-1]), 4)
......@@ -408,8 +408,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
# 申购以来
confirm_date = pd.to_datetime(row["confirm_share_date"])
confirm_cnav = float(p_result_cnav_data.loc[confirm_date, cur_fund_id])
fund_basic_info["ret_after_confirm"] = round(
(fund_basic_info["cur_cnav"] - confirm_cnav) / confirm_cnav * 100, 2)
fund_basic_info["ret_after_confirm"] = str(round(
(fund_basic_info["cur_cnav"] - confirm_cnav) / confirm_cnav * 100, 2)) + "%"
# 分红
distribution_df = self.all_fund_distribution[cur_fund_id]
if distribution_df.empty:
......@@ -448,23 +448,19 @@ 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"])]
fund_strategy_name = get_substrategy_name(row["substrategy"])
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")
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
# fund_hoding_info["cost"] = "%.2f" % round(float(row["confirm_share"]*row["nav"])/10000, 2)
temp_market_values = float(row["confirm_share"]) * (
fund_basic_info["cur_cnav"] - confirm_cnav + float(row["nav"]))
# 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(p_result_cnav_data[cur_fund_id + "_net_amount"].values[-1])
fund_hoding_info["market_values"] = round(temp_market_values / 10000.0, 2)
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_share"] * row["nav"]) / 10000, 2) # 投资本金
temp_cost = float(p_result_cnav_data[cur_fund_id + "_amount"].values[-1])
fund_hoding_info["cost"] = "%.2f" % round( temp_cost / 10000, 2) # 投资本金
# 当月收益
if row['confirm_share_date'] > self.month_start_date:
......@@ -472,28 +468,29 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
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)
cal_month_start_date = self.month_start_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()
cal_month_cnav_result = p_result_cnav_data[p_result_cnav_data.index >= pd.to_datetime(cal_month_start_date)]
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
temp_profit = 0
fund_hoding_info["profit"] = temp_profit
temp_profit_ratio = 0
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)
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(temp_profit / temp_market_values*100, 2)
fund_hoding_info["month_return_ratio"] = "%.2f" % round(temp_profit_ratio * 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)
cum_profit = float(p_result_cnav_data[cur_fund_id + "_earn"].sum())
fund_hoding_info["cum_profit"] = "%.2f" % round(cum_profit / 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
fund_hoding_info["cum_profit_ratio"] = "%.2f" % round(cum_profit / temp_cost *100, 2)
cum_profit_ratio_temp = cum_profit / temp_cost
# 累积年化收益率
cur_resample_df = resample_df[[row["fund_id"]]].dropna()
cur_resample_df = resample_df[[row["fund_id"]]]
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)
......@@ -504,28 +501,28 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
fund_id_list_amount = [i + "_net_amount" for i in fund_id_list]
fund_id_list_profit_ratio = [i + "_profit_ratio" for i in fund_id_list]
nav_net_amount_df = p_combina_df[fund_id_list + fund_id_list_amount+fund_id_list_profit_ratio].copy()
# 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
# 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"])
fund_profit_ratio = nav_net_amount_df[amount_name + "_profit_ratio"].dropna() + 1
amount_ratio_shift = nav_net_amount_df[amount_name + "_amount_ratio"].shift(1)
num_va = len(amount_ratio_shift[amount_ratio_shift.values==0])
num_va = len(amount_ratio_shift[amount_ratio_shift.values == 0])
if num_va+1 >= len(amount_ratio_shift):
amount_ratio_shift.iloc[num_va] = 0
else:
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_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]
......@@ -544,7 +541,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
cum_return_ratio_df.index = return_ratio.index
# 单个基金累计收益分解df
weight_name_list = [i + "_profit_cum_ratio_weight" for i in fund_id_list]
weight_name_list = [i + "_profit_cum_ratio_weight" for i in fund_id_list]
signal_fund_cum_weight = nav_net_amount_df[weight_name_list]
re_name = {x: x.replace("_profit_cum_ratio_weight", "") for x in weight_name_list}
signal_fund_cum_weight.rename(columns=re_name, inplace=True)
......
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