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fund_report
Commit 39a9a9d0 authored by 李宗熹's avatar 李宗熹
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评价修改

parent fc60482a
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Showing with 203 additions and 113 deletions
app/api/engine.py
View file @ 39a9a9d0
......@@ -57,6 +57,18 @@ tamp_user_engine = create_engine(
),
echo=True
)
tamp_fund_engine = create_engine(
'mysql+pymysql://{user}:{password}@{host}:{port}/{db}?charset={charset}'.format(
db=config[env]['MySQL']['tamp_fund_db'],
host=config[env]['MySQL']['host'],
port=config[env]['MySQL']['port'],
user=config[env]['MySQL']['user'],
password=config[env]['MySQL']['password'],
charset="utf8"
),
echo=True
)
# tamp_product_session = scoped_session(sessionmaker(bind=tamp_product_engine))()
# tamp_order_session = scoped_session(sessionmaker(bind=tamp_order_engine))()
# tamp_user_session = scoped_session(sessionmaker(bind=tamp_user_engine))()
......
app/config/config.yaml
View file @ 39a9a9d0
......@@ -43,6 +43,7 @@ prod:
tamp_product_db: tamp_product
tamp_order_db: tamp_order
tamp_user_db: tamp_user
tamp_fund_db: tamp_fund
host: tamper.mysql.polardb.rds.aliyuncs.com
port: 3306
user: tamp_admin
......
app/service/portfolio_diagnose.py
View file @ 39a9a9d0
# -*- coding: UTF-8 -*-
# """
# @author: Zongxi.Li
# @file:portfolio_copy.py
# @time:2020/12/03
# """
from app.utils.fund_rank import *
from app.utils.risk_parity import *
from app.pypfopt import risk_models
from app.pypfopt import expected_returns
from app.pypfopt import EfficientFrontier
from app.api.engine import tamp_user_engine, tamp_product_engine, TAMP_SQL
from app.api.engine import tamp_product_engine, tamp_fund_engine, TAMP_SQL
def cal_correlation(prod):
......@@ -15,10 +21,10 @@ def cal_correlation(prod):
Returns:屏蔽基金与自身相关性的相关矩阵，因为基金与自身相关性为1，妨碍后续高相关性基金筛选的判断
"""
prod_return = prod.iloc[:, :].apply(lambda x: simple_return(x))
prod_return = prod.iloc[:, :].apply(lambda x: simple_return(x).astype(float))
correlation = prod_return.corr()
correlation = correlation.round(2)
return correlation.mask(np.eye(correlation.shape[0], dtype=np.bool))
return correlation.mask(np.eye(correlation.shape[0], dtype=np.bool_))
def rename_col(df, fund_id):
......@@ -66,7 +72,7 @@ def search_rank(fund_rank, fund, metric):
return fund_rank[fund_rank['fund_id'] == fund][metric].values[0]
def translate_single(content, evaluation):
def translate_single(content, content_id, evaluation):
'''
content = [["优秀","良好","一般"],
["优秀","良好","合格","较差"],
......@@ -74,7 +80,20 @@ def translate_single(content, evaluation):
["高","一般","较低"]]
evaluation = [0,1,1,2]
'''
return tuple([content[i][v] if type(v) == int else v for i, v in enumerate(evaluation)])
ret = []
for i, v in enumerate(evaluation):
if isinstance(v, str):
ret.append(v)
continue
elif content[content_id][i][v] in ["优秀", "良好", "高", "高于", "较好"]:
ret.append("""<span class="self_description_red">{}</span>""".format(content[content_id][i][v]))
continue
elif content_id == 4 and v == 0:
ret.append("""<span class="self_description_red">{}</span>""".format(content[content_id][i][v]))
continue
else:
ret.append("""<span class="self_description_green">{}</span>""".format(content[content_id][i][v]))
return tuple(ret)
def choose_good_evaluation(evaluation):
......@@ -127,9 +146,19 @@ def choose_bad_evaluation(evaluation):
def get_fund_rank():
with TAMP_SQL(tamp_fund_engine) as tamp_product:
tamp_product_session = tamp_product.session
sql = "SELECT * FROM fund_rank"
df = pd.read_sql(sql, con)
# df = pd.read_sql(sql, con)
# df = pd.read_csv('fund_rank.csv', encoding='gbk')
cur = tamp_product_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',
'substrategy', 'manager', 'annual_return_rank', 'downside_risk_rank',
'max_drawdown_rank', 'sharp_ratio_rank', 'z_score'])
df.drop('index', axis=1, inplace=True)
return df
......@@ -142,8 +171,14 @@ def get_index_daily(index_id):
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)
df = pd.read_sql(sql, con).dropna(how='any')
# df = pd.read_sql(sql, con).dropna(how='any')
cur = tamp_product_session.execute(sql)
data = cur.fetchall()
df = pd.DataFrame(list(data), columns=['ts_code', 'trade_code', ' close'])
df.rename({'ts_code': 'fund_id', 'trade_date': 'end_date', 'close': 'adj_nav'}, axis=1, inplace=True)
df['end_date'] = pd.to_datetime(df['end_date'])
df.set_index('end_date', drop=True, inplace=True)
......@@ -158,8 +193,13 @@ def get_tamp_fund():
Returns:
"""
with TAMP_SQL(tamp_fund_engine) as tamp_product:
tamp_product_session = tamp_product.session
sql = "SELECT id FROM tamp_fund_info WHERE id LIKE 'HF%'"
df = pd.read_sql(sql, con)
cur = tamp_product_session.execute(sql)
data = cur.fetchall()
# df = pd.read_sql(sql, con)
df = pd.DataFrame(list(data), columns=['id'])
df.rename({'id': 'fund_id'}, axis=1, inplace=True)
return df
......@@ -203,8 +243,13 @@ 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
sql = "SELECT fund_short_name FROM fund_info WHERE id='{}'".format(fund)
df = pd.read_sql(sql, con)
# df = pd.read_sql(sql, con)
cur = tamp_product_session.execute(sql)
data = cur.fetchall()
df = pd.DataFrame(list(data), columns=['fund_short_name'])
return df
......@@ -336,12 +381,12 @@ class PortfolioDiagnose(object):
# 建议替换得分为60或与其他基金相关度大于0.8的基金
if z_score < 60:
self.abandon_fund_score.append(fund)
prod = prod.drop(fund, axis=1)
continue
if np.any(self.old_correlation[fund] > 0.8):
elif np.any(self.old_correlation[fund] > 0.8):
self.abandon_fund_corr.append(fund)
prod = prod.drop(fund, axis=1)
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])
......@@ -390,7 +435,7 @@ class PortfolioDiagnose(object):
proposal_nav = rename_col(proposal_nav, proposal)
# 按最大周期进行重采样，计算新建组合的相关性
prod = pd.merge(prod, proposal_nav, how='outer', on='end_date')
prod = pd.merge(prod, proposal_nav, how='outer', on='end_date').astype(float)
prod.sort_index(inplace=True)
prod.ffill(inplace=True)
prod = resample(prod, get_trade_cal(), min(self.freq_list))
......@@ -442,7 +487,6 @@ class PortfolioDiagnose(object):
# (set(self.proposal_fund) | set(self.replace_pair.values())))
# propose_portfolio.drop()
propose_risk_mapper = dict()
for fund in propose_portfolio.columns:
propose_risk_mapper[fund] = str(get_risk_level(search_rank(fund_rank, fund, metric='substrategy')))
......@@ -526,13 +570,14 @@ class PortfolioDiagnose(object):
num = len(fund_rank_re)
fund_id_rank_list = list(fund_rank_re["fund_id"])
for f_id in fund_id_rank_list:
name = data_adaptor.user_customer_order_df[data_adaptor.user_customer_order_df["fund_id"] == f_id]["fund_name"].values[0]
name = data_adaptor.user_customer_order_df[data_adaptor.user_customer_order_df["fund_id"] == f_id][
"fund_name"].values[0]
return_rank_evaluate = return_rank_evaluate + name + "、"
return_rank_evaluate = return_rank_evaluate[:-1] +"等" + str(num) + "只产品稳健，对组合的收益率贡献明显，"
return_rank_evaluate = return_rank_evaluate[:-1] + "等" + str(num) + "只产品稳健，对组合的收益率贡献明显，"
# 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:
......@@ -548,21 +593,22 @@ class PortfolioDiagnose(object):
else:
no_data_fund_evaluate = "；"
group_order_df = data_adaptor.user_customer_order_df[data_adaptor.user_customer_order_df["folio_name"] == group_name]
group_order_df = data_adaptor.user_customer_order_df[
data_adaptor.user_customer_order_df["folio_name"] == group_name]
strategy_list = group_order_df["substrategy"]
uniqe_strategy = list(strategy_list.unique())
uniqe_strategy_name = [dict_substrategy[int(x)] + "、" for x in uniqe_strategy]
# 覆盖的基金名称
strategy_name_evaluate = "".join(uniqe_strategy_name)[:-1]
if len(uniqe_strategy)/float(len(strategy_list)) > 0.6:
if len(uniqe_strategy) / float(len(strategy_list)) > 0.6:
strategy_distribution_evaluate = "策略上有一定分散"
else:
strategy_distribution_evaluate = "策略分散程度不高"
# 相关性
if len(self.abandon_fund_corr) > 0:
fund_corr_name = [str(group_order_df[group_order_df["fund_id"] == f_id]["fund_name"].values[0]) + "和" for f_id in self.abandon_fund_corr]
fund_corr_name = [str(group_order_df[group_order_df["fund_id"] == f_id]["fund_name"].values[0]) + "和" for
f_id in self.abandon_fund_corr]
fund_corr_evaluate = "".join(fund_corr_name)[:-1] + "相关性较高，建议调整组合配比；"
else:
fund_corr_evaluate = "；"
......@@ -605,7 +651,7 @@ class PortfolioDiagnose(object):
hold_info = group_result_data["group_hoding_info"]
# 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(pd.DataFrame(hold_info)["market_values"].sum(),2)
total_asset = round(pd.DataFrame(hold_info)["market_values"].sum(), 2)
old_return = group_result_data["cumulative_return"]
old_return_ratio_year = group_result_data["return_ratio_year"]
old_volatility = group_result_data["volatility"]
......@@ -625,9 +671,6 @@ class PortfolioDiagnose(object):
propose_fund_df = product_df[product_df["fund_id"].isin(propose_fund_id_list)]
propose_fund_id_list_name = [] # 基金名称，策略分级
# hold_fund = set(self.portfolio) - set(self.abandon_fund)
# abandon_fund = self.abandon_fund
# proposal_fund = self.proposal_fund
......@@ -733,10 +776,13 @@ class PortfolioDiagnose(object):
else:
evaluation = choose_bad_evaluation(data)
ret = ""
ret = []
i = 1
for k, v in evaluation.items():
# print(translate_single(content[k], v))
ret = ret + sentence[k] % translate_single(content[k], v)
print(k, v)
single_sentence = str(i) + "." + sentence[k] % translate_single(content, k, v)
ret.append(single_sentence)
i += 1
fund_name = get_fund_name(fund_id).values[0][0]
return {'name': fund_name, 'data': ret}
......@@ -774,11 +820,11 @@ class PortfolioDiagnose(object):
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())
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())
app/utils/fund_rank.py
View file @ 39a9a9d0
# import pymysql
from sqlalchemy import create_engine
db = create_engine(
'mysql+pymysql://tamp_fund:@imeng408@tamper.mysql.polardb.rds.aliyuncs.com:3306/tamp_fund?charset=utf8mb4',
pool_size=50,
pool_recycle=3600,
pool_pre_ping=True)
con = db.connect()
import logging
# db = create_engine(
# 'mysql+pymysql://tamp_fund:@imeng408@tamper.mysql.polardb.rds.aliyuncs.com:3306/tamp_fund?charset=utf8mb4',
# pool_size=50,
# pool_recycle=3600,
# pool_pre_ping=True)
# con = db.connect()
import logging
logging.basicConfig(level=logging.INFO)
from app.api.engine import tamp_fund_engine, TAMP_SQL
from app.utils.week_evaluation import *
......@@ -35,15 +36,22 @@ def get_nav(fund, start_date, rollback=False, invest_type='public'):
Returns:df[DataFrame]: 索引为净值公布日， 列为复权净值的净值表; 查询失败则返回None
"""
with TAMP_SQL(tamp_fund_engine) as tamp_product:
tamp_product_session = tamp_product.session
if invest_type == 'public':
sql = "SELECT ts_code, end_date, adj_nav FROM public_fund_nav " \
"WHERE ts_code='{}'".format(fund)
df = pd.read_sql(sql, con).dropna(how='any')
cur = tamp_product_session.execute(sql)
data = cur.fetchall()
df = pd.DataFrame(list(data), columns=['ts_code', 'end_date', 'adj_nav']).dropna(how='any')
df.rename({'ts_code': 'fund_id'}, axis=1, inplace=True)
else:
sql = "SELECT fund_id, price_date, cumulative_nav FROM fund_nav " \
"WHERE fund_id='{}'".format(fund)
df = pd.read_sql(sql, con).dropna(how='any')
# df = pd.read_sql(sql, con).dropna(how='any')
cur = tamp_product_session.execute(sql)
data = cur.fetchall()
df = pd.DataFrame(data, columns=['fund_id', 'price_date', 'cumulative_nav']).dropna(how='any')
df.rename({'price_date': 'end_date', 'cumulative_nav': 'adj_nav'}, axis=1, inplace=True)
if df['adj_nav'].count() == 0:
......@@ -97,8 +105,13 @@ def get_trade_cal():
Returns:df[DataFrame]: 索引为交易日， 列为交易日的上交所交易日历表
"""
with TAMP_SQL(tamp_fund_engine) as tamp_product:
tamp_product_session = tamp_product.session
sql = 'SELECT cal_date FROM stock_trade_cal WHERE is_open=1'
df = pd.read_sql(sql, con)
cur = tamp_product_session.execute(sql)
data = cur.fetchall()
df = pd.DataFrame(list(data), columns=['cal_date']).dropna(how='all')
# df = pd.read_sql(sql, con)
df['end_date'] = pd.to_datetime(df['cal_date'])
df.set_index('end_date', drop=False, inplace=True)
return df
......@@ -113,12 +126,20 @@ def get_manager(invest_type):
Returns:
"""
with TAMP_SQL(tamp_fund_engine) as tamp_product:
tamp_product_session = tamp_product.session
if invest_type == 'public':
sql = 'SELECT ts_code, name FROM public_fund_manager WHERE end_date IS NULL'
df = pd.read_sql(sql, con)
# df = pd.read_sql(sql, con)
cur = tamp_product_session.execute(sql)
data = cur.fetchall()
df = pd.DataFrame(list(data), columns=['ts_code', 'name'])
else:
sql = 'SELECT fund_id, fund_manager_id FROM fund_manager_mapping'
df = pd.read_sql(sql, con)
# df = pd.read_sql(sql, con)
cur = tamp_product_session.execute(sql)
data = cur.fetchall()
df = pd.DataFrame(list(data), columns=['fund_id', 'fund_manager_id'])
return df
......@@ -132,18 +153,28 @@ def get_fund_info(end_date, invest_type):
Returns:
[type]: [description]
"""
with TAMP_SQL(tamp_fund_engine) as tamp_product:
tamp_product_session = tamp_product.session
if invest_type == 'public':
sql = "SELECT ts_code, fund_type, management FROM public_fund_basic " \
"WHERE delist_date IS NULL AND (due_date IS NULL OR due_date>'{}')".format(end_date.strftime('%Y%m%d'))
df = pd.read_sql(sql, con).dropna(how='all')
# df = pd.read_sql(sql, con).dropna(how='all')
cur = tamp_product_session.execute(sql)
data = cur.fetchall()
df = pd.DataFrame(list(data), columns=['ts_code', 'fund_type', 'management'])
manager_info = get_manager(invest_type)
df.rename({'ts_code': 'fund_id'}, axis=1, inplace=True)
df = pd.merge(df, manager_info, how="left", on='fund_id')
else:
sql = "SELECT id, substrategy FROM fund_info WHERE delete_tag=0 " \
"AND substrategy!=-1"
df = pd.read_sql(sql, con).dropna(how='all')
cur = tamp_product_session.execute(sql)
data = cur.fetchall()
df = pd.DataFrame(list(data), columns=['id', 'substrategy'])
# df = pd.read_sql(sql, con).dropna(how='all')
df.rename({'id': 'fund_id'}, axis=1, inplace=True)
manager_info = get_manager(invest_type)
......@@ -301,4 +332,4 @@ if __name__ == '__main__':
# fund_rank.to_csv("fund_rank.csv", encoding='gbk')
# df = pd.read_csv('fund_rank.csv')
# df.to_sql("fund_rank", con, if_exists='replace')
con.close()
# con.close()
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