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fund_report
Commit b192a760 authored by 李宗熹's avatar 李宗熹
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修改self.portfolio

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app/service/portfolio_diagnose.py
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# -*- coding: UTF-8 -*-
"""
@author: Zongxi.Li
@file:portfolio_diagnose.py
@time:2020/12/07
"""
from app.utils.fund_rank import *
from app.utils.risk_parity import *
from app.pypfopt import risk_models
......@@ -52,23 +58,6 @@ def replace_fund(manager, substrategy, fund_rank):
return df['fund_id'].values[0]
def get_tamp_fund():
"""获取探普产品池净值表
Returns:
"""
with TAMP_SQL(tamp_fund_engine) as tamp_fund:
tamp_fund_session = tamp_fund.session
sql = "SELECT id FROM tamp_fund_info WHERE id LIKE 'HF%'"
cur = tamp_fund_session.execute(sql)
data = cur.fetchall()
# df = pd.read_sql(sql, con)
df = pd.DataFrame(list(data), columns=['fund_id'])
# df.rename({'id': 'fund_id'}, axis=1, inplace=True)
return df
def search_rank(fund_rank, fund, metric):
"""查找基金在基金排名表中的指标
......@@ -157,13 +146,17 @@ def choose_bad_evaluation(evaluation):
def get_fund_rank():
with TAMP_SQL(tamp_fund_engine) as tamp_product:
tamp_product_session = tamp_product.session
"""获取基金指标排名
:return: 基金指标排名表
"""
with TAMP_SQL(tamp_fund_engine) as tamp_fund:
tamp_fund_session = tamp_fund.session
sql = "SELECT * FROM fund_rank"
# df = pd.read_sql(sql, con)
# df = pd.read_csv('fund_rank.csv', encoding='gbk')
cur = tamp_product_session.execute(sql)
cur = tamp_fund_session.execute(sql)
data = cur.fetchall()
df = pd.DataFrame(list(data), columns=['index', 'fund_id', 'range_return', 'annual_return', 'max_drawdown',
'sharp_ratio', 'volatility', 'sortino_ratio', 'downside_risk',
......@@ -173,18 +166,20 @@ def get_fund_rank():
return df
def get_index_daily(index_id):
"""获取指数数据
def get_index_daily(index_id, start_date):
"""获取指数日更数据
Args:
index_id: 指数ID
start_date: 数据开始时间
Returns:与组合净值形式相同的表
"""
with TAMP_SQL(tamp_fund_engine) as tamp_product:
tamp_product_session = tamp_product.session
sql = "SELECT ts_code, trade_date, close FROM index_daily WHERE ts_code='{}'".format(index_id)
sql = "SELECT ts_code, trade_date, close FROM index_daily " \
"WHERE ts_code='{}' AND trade_date>'{}'".format(index_id, start_date)
# df = pd.read_sql(sql, con).dropna(how='any')
cur = tamp_product_session.execute(sql)
data = cur.fetchall()
......@@ -198,6 +193,49 @@ def get_index_daily(index_id):
return df
def get_index_monthly(index_id, start_date):
"""获取指数月度数据
Args:
index_id: 指数ID
start_date: 数据开始时间
Returns:与组合净值形式相同的表
"""
with TAMP_SQL(tamp_fund_engine) as tamp_fund:
tamp_fund_session = tamp_fund.session
sql = "SELECT ts_code, trade_date, pct_chg FROM index_monthly " \
"WHERE ts_code='{}' AND trade_date>'{}'".format(index_id, start_date)
# df = pd.read_sql(sql, con).dropna(how='any')
cur = tamp_fund_session.execute(sql)
data = cur.fetchall()
df = pd.DataFrame(list(data), columns=['fund_id', 'end_date', 'pct_chg'])
df['end_date'] = pd.to_datetime(df['end_date'])
df.set_index('end_date', drop=True, inplace=True)
df.sort_index(inplace=True, ascending=True)
df = rename_col(df, index_id)
return df
def get_tamp_fund():
"""获取探普产品池净值表
Returns:
"""
with TAMP_SQL(tamp_fund_engine) as tamp_fund:
tamp_fund_session = tamp_fund.session
sql = "SELECT id FROM tamp_fund_info WHERE id LIKE 'HF%'"
cur = tamp_fund_session.execute(sql)
data = cur.fetchall()
# df = pd.read_sql(sql, con)
df = pd.DataFrame(list(data), columns=['fund_id'])
# df.rename({'id': 'fund_id'}, axis=1, inplace=True)
return df
def get_tamp_nav(fund, start_date, rollback=False, invest_type='public'):
"""获取基金ID为fund, 起始日期为start_date, 终止日期为当前日期的基金净值表
......@@ -277,11 +315,11 @@ def get_radar_data(fund):
def get_fund_name(fund):
with TAMP_SQL(tamp_fund_engine) as tamp_product:
tamp_product_session = tamp_product.session
with TAMP_SQL(tamp_fund_engine) as tamp_fund:
tamp_fund_session = tamp_fund.session
sql = "SELECT fund_short_name FROM fund_info WHERE id='{}'".format(fund)
# df = pd.read_sql(sql, con)
cur = tamp_product_session.execute(sql)
cur = tamp_fund_session.execute(sql)
data = cur.fetchall()
df = pd.DataFrame(list(data), columns=['fund_short_name'])
return df
......@@ -294,14 +332,17 @@ tamp_fund = get_tamp_fund()
class PortfolioDiagnose(object):
def __init__(self, client_type, portfolio, invest_amount, expect_return=None,
expect_drawdown=None, index_id='000905.SH', invest_type='private', start_date=None, end_date=None):
def __init__(self, client_type, portfolio, invest_amount, expect_return=0.2,
expect_drawdown=0.1, index_id='000905.SH', invest_type='private', start_date=None, end_date=None):
"""基金诊断
Args:
client_type: 客户类型：1:保守型, 2:稳健型, 3:平衡型, 4:成长型, 5：进取型
portfolio: 投资组合：[基金1, 基金2, 基金3...]
invest_amount: 投资金额：10000000元
expect_return: 期望收益
expect_drawdown: 期望回撤
index_id： 指数ID
invest_type: 投资类型：public, private, ...
start_date: 诊断所需净值的开始日期
end_date: 诊断所需净值的结束日期
......@@ -348,51 +389,52 @@ class PortfolioDiagnose(object):
while prod is None:
# 获取的净值表为空时首先考虑基金净值数据不足半年，查找同一基金经理下的相同二级策略的基金ID作替换
result = fund_info[fund_info['fund_id'] == portfolio[0]]
result = fund_info[fund_info['fund_id'] == self.portfolio[0]]
manager = str(result['manager'].values)
strategy = result['substrategy'].values
replaced_fund = replace_fund(manager, strategy, fund_rank)
if replaced_fund is not None:
if replaced_fund:
# 替换基金数据非空则记录替换的基金对
prod = get_nav(replaced_fund, self.start_date, invest_type=self.invest_type)
self.replace_pair[portfolio[0]] = replaced_fund
self.replace_pair[self.portfolio[0]] = replaced_fund
else:
# 替换基金数据为空则记录当前基金为找不到数据的基金， 继续尝试获取下一个基金ID的净值表
self.no_data_fund.append(portfolio[0])
self.no_data_fund.append(self.portfolio[0])
self.portfolio.pop(0)
prod = get_tamp_nav(self.portfolio[0], self.start_date, invest_type=self.invest_type)
# 记录基金的公布频率
self.freq_list.append(get_frequency(prod))
prod = rename_col(prod, portfolio[0])
prod = rename_col(prod, self.portfolio[0])
# 循环拼接基金净值表构建组合
for idx in range(len(portfolio) - 1):
prod1 = get_tamp_nav(portfolio[idx + 1], self.start_date, invest_type=self.invest_type)
for idx in range(len(self.portfolio) - 1):
prod1 = get_tamp_nav(self.portfolio[idx + 1], self.start_date, invest_type=self.invest_type)
if prod1 is None or prod1.index[-1] - prod1.index[0] < 0.6 * (self.end_date - self.start_date):
result = fund_info[fund_info['fund_id'] == portfolio[idx + 1]]
result = fund_info[fund_info['fund_id'] == self.portfolio[idx + 1]]
if result['fund_manager_id'].count() != 0:
manager = str(result['fund_manager_id'].values)
substrategy = result['substrategy'].values[0]
replaced_fund = replace_fund(manager, substrategy, fund_rank)
else:
self.no_data_fund.append(portfolio[idx + 1])
self.no_data_fund.append(self.portfolio[idx + 1])
continue
if replaced_fund is not None:
if replaced_fund:
prod1 = get_nav(replaced_fund, self.start_date, invest_type=self.invest_type)
self.replace_pair[portfolio[idx + 1]] = replaced_fund
self.replace_pair[self.portfolio[idx + 1]] = replaced_fund
self.freq_list.append(get_frequency(prod1))
prod1 = rename_col(prod1, replaced_fund)
else:
self.no_data_fund.append(portfolio[idx + 1])
self.no_data_fund.append(self.portfolio[idx + 1])
continue
else:
self.freq_list.append(get_frequency(prod1))
prod1 = rename_col(prod1, portfolio[idx + 1])
prod1 = rename_col(prod1, self.portfolio[idx + 1])
# 取prod表和prod1表的并集
prod = pd.merge(prod, prod1, on=['end_date'], how='outer')
......@@ -476,6 +518,7 @@ class PortfolioDiagnose(object):
prod = pd.merge(prod, proposal_nav, how='outer', on='end_date').astype(float)
prod.sort_index(inplace=True)
prod.ffill(inplace=True)
prod.bfill(inplace=True)
prod = resample(prod, get_trade_cal(), min(self.freq_list))
self.new_correlation = cal_correlation(prod)
......@@ -529,14 +572,31 @@ class PortfolioDiagnose(object):
for fund in self.propose_portfolio.columns:
propose_risk_mapper[fund] = str(get_risk_level(search_rank(fund_rank, fund, metric='substrategy')))
# risk_upper = {"H": 0.0}
# risk_lower = {"L": 0.6, "M": 0.4}
w_low = 1e6 / self.invest_amount
ef = EfficientFrontier(mu, S, expected_drawdown=dd)
# ef.add_sector_constraints(propose_risk_mapper, risk_lower, risk_upper)
# weights = ef.nonconvex_objective(deviation_risk_parity, ef.cov_matrix)
ef.efficient_return(0.3)
if self.client_type == 1:
risk_upper = {"H": 1.0}
risk_lower = {"L": 0.0}
elif self.client_type == 2:
risk_upper = {"H": 1.0}
risk_lower = {"L": 0.0}
elif self.client_type == 3:
risk_upper = {"H": 1.0}
risk_lower = {"L": 0.0}
elif self.client_type == 4:
risk_upper = {"H": 1.0}
risk_lower = {"L": 0.0}
elif self.client_type == 5:
risk_upper = {"H": 1.0}
risk_lower = {"L": 0.0}
else:
risk_upper = {"H": 1.0}
risk_lower = {"L": 0.0}
raise ValueError
w_low = 1000000 / self.invest_amount
# ef = EfficientFrontier(mu, S, weight_bounds=[w_low, 1], expected_drawdown=dd)
ef = EfficientFrontier(mu, S, weight_bounds=[0, 1], expected_drawdown=dd)
ef.add_sector_constraints(propose_risk_mapper, risk_lower, risk_upper)
ef.efficient_return(target_return=self.expect_return)
clean_weights = ef.clean_weights()
# ef.portfolio_performance(verbose=True)
self.new_weights = np.array(list(clean_weights.values()))
......@@ -564,7 +624,7 @@ class PortfolioDiagnose(object):
# self.proposal_weights = calcu_w(w_origin, S, risk_target)
def return_compare(self):
index_data = get_index_daily(self.index_id)
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
# origin_fund_return = origin_portfolio.iloc[:, :] / origin_portfolio.iloc[0, :] - 1
......@@ -610,7 +670,7 @@ class PortfolioDiagnose(object):
# 正收益基金数量
group_hold_data = pd.DataFrame(group_result[group_name]["group_hoding_info"])
profit_positive_num = group_hold_data[group_hold_data["profit"]>0]["profit"].count()
profit_positive_num = group_hold_data[group_hold_data["profit"] > 0]["profit"].count()
if profit_positive_num > 0:
profit_positive_evaluate = str(profit_positive_num) + "只基金取的正收益，"
else:
......@@ -687,7 +747,7 @@ class PortfolioDiagnose(object):
data_adaptor.user_customer_order_df["folio_name"] == group_name]
group_order_start_date = pd.to_datetime(group_order_df["confirm_share_date"].min())
# 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
# 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"]
......@@ -863,6 +923,16 @@ class PortfolioDiagnose(object):
70 <= z_score < 80,
z_score < 70], [0, 1, 2]).item()
index_return_monthly = get_index_monthly(self.index_id, self.start_date)
fund_nav = get_tamp_nav(fund_id, self.start_date, invest_type=self.invest_type)
fund_nav_monthly = fund_nav.groupby([fund_nav.index.year, fund_nav.index.month]).tail(1)
fund_nav_monthly = rename_col(fund_nav_monthly, fund_id)
fund_return_monthly = simple_return(fund_nav_monthly[fund_id].astype(float))
index_return_monthly.index = index_return_monthly.index.strftime('%Y-%m')
fund_return_monthly.index = fund_return_monthly.index.strftime('%Y-%m')
compare = pd.merge(index_return_monthly, fund_return_monthly, how='inner', left_index=True, right_index=True)
fund_win_rate = ((compare[fund_id] - compare['pct_chg']) > 0).sum()
return_rank = search_rank(fund_rank, fund_id, metric='annual_return_rank')
return_level = np.select([return_rank >= 0.8,
0.7 <= return_rank < 0.8,
......@@ -886,7 +956,7 @@ class PortfolioDiagnose(object):
sharp_rank < 0.6], [0, 1, 2]).item()
data = {1: [total_level, return_level, drawdown_level, sharp_level],
2: [return_triple, "12", return_bool],
2: [return_triple, str(fund_win_rate), return_bool],
3: [drawdown_triple, drawdown_triple, format(drawdown_value, '.2%'), drawdown_triple],
4: [return_bool, drawdown_bool, drawdown_bool, return_bool, drawdown_bool]}
......@@ -923,7 +993,7 @@ class PortfolioDiagnose(object):
sentence = {
1: "该基金整体表现%s，收益能力%s，回撤控制能力%s，风险收益比例%s；\n",
2: "在收益方面，该基金年化收益能力%s同类基金平均水平，有%s区间跑赢指数，绝对收益能力%s；\n",
2: "在收益方面，该基金年化收益能力%s同类基金平均水平，有%s个区间跑赢指数，绝对收益能力%s；\n",
3: "在风险方面，该基金抵御风险能力%s，在同类基金中处于%s等水平，最大回撤为%s，%s同类基金平均水平；\n",
4: "该基金收益%s的同时回撤%s，也就是说，该基金在用%s风险换取%s收益，存在%s风险；\n",
5: "基金经理，投资年限%s年，经验丰富；投资能力较强，生涯中共管理过%s只基金，历任的%s只基金平均业绩在同类中处于上游水平，其中%s只排名在前%s；生涯年化回报率%s，同期大盘只有%s；"}
......@@ -945,6 +1015,14 @@ class PortfolioDiagnose(object):
ret.append(single_sentence)
i += 1
fund_name = get_fund_name(fund_id).values[0][0]
if not ret:
try:
default_evaluation = pd.read_csv("evaluation.csv", encoding='utf-8', names=['fund_id', 'eval'])
ret.append('1、' + default_evaluation[default_evaluation['fund_id'] == fund_id]['eval'].values[0])
except Exception:
pass
evaluation_dict = {'name': fund_name, 'data': ret}
if fund_id in self.abandon_fund_score + self.abandon_fund_corr:
evaluation_dict['status'] = "换仓"
......@@ -983,14 +1061,14 @@ class PortfolioDiagnose(object):
return radar_data
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())
print('新组合个基评价：', portfolio_diagnose.propose_fund_evaluation())
\ No newline at end of file
# 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())
# print('新组合个基评价：', portfolio_diagnose.propose_fund_evaluation())
\ No newline at end of file
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