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Commit b2420a78 authored by 李宗熹's avatar 李宗熹
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产品净值获取方式修改

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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
......@@ -107,8 +113,7 @@ def choose_good_evaluation(evaluation):
if v1[0] > 1:
del evaluation[1]
# if v2[0] > 1:
if v2:
if v2[0] > 1:
del evaluation[2]
if v3[0] > 1:
del evaluation[3]
......@@ -128,8 +133,7 @@ def choose_bad_evaluation(evaluation):
if v1[0] < 2:
del evaluation[1]
# if v2[0] < 2:
if v2:
if v2[0] < 2:
del evaluation[2]
if v3[0] < 2:
del evaluation[3]
......@@ -140,13 +144,17 @@ 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',
......@@ -156,24 +164,26 @@ 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()
df = pd.DataFrame(list(data), columns=['ts_code', 'trade_date', ' close'])
df.rename({'ts_code': 'fund_id', 'trade_date': 'end_date', 'close': 'adj_nav'}, axis=1, inplace=True)
df = pd.DataFrame(list(data), columns=['fund_id', 'end_date', 'adj_nav'])
# 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)
df.sort_index(inplace=True, ascending=True)
......@@ -181,20 +191,86 @@ 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_product:
tamp_product_session = tamp_product.session
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_product_session.execute(sql)
cur = tamp_fund_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)
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
Args:
fund[str]:基金ID
start_date[date]:起始日期
rollback[bool]:当起始日期不在净值公布日历中，是否往前取最近的净值公布日
public[bool]:是否为公募
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_product_engine) as tamp_product:
tamp_product_session = tamp_product.session
if invest_type == "private":
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')
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 df2['adj_nav'].count() == 0:
# logging.log(logging.ERROR, "CAN NOT FIND {}".format(fund))
# return None
df['end_date'] = pd.to_datetime(df['end_date'])
if rollback and df['end_date'].min() < start_date < df['end_date'].max():
while start_date not in list(df['end_date']):
start_date -= datetime.timedelta(days=1)
df = df[df['end_date'] >= start_date]
df.drop_duplicates(subset='end_date', inplace=True, keep='first')
df.set_index('end_date', inplace=True)
df.sort_index(inplace=True, ascending=True)
return df
......@@ -237,11 +313,11 @@ 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
......@@ -254,14 +330,17 @@ 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
......@@ -303,56 +382,57 @@ class PortfolioDiagnose(object):
"""
# 获取原始投资组合的第一支基金的净值表
prod = get_nav(self.portfolio[0], self.start_date, invest_type=self.invest_type)
prod = get_tamp_nav(self.portfolio[0], self.start_date, invest_type=self.invest_type)
fund_info = get_fund_info(self.end_date, invest_type=self.invest_type)
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'] == self.portfolio[0]]
result = fund_info[fund_info['fund_id'] == 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:
# 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[self.portfolio[0]] = replaced_fund
self.replace_pair[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(self.portfolio[0])
self.no_data_fund.append(portfolio[0])
self.portfolio.pop(0)
prod = get_nav(self.portfolio[0], self.start_date, invest_type=self.invest_type)
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, self.portfolio[0])
prod = rename_col(prod, portfolio[0])
# 循环拼接基金净值表构建组合
for idx in range(len(self.portfolio) - 1):
prod1 = get_nav(self.portfolio[idx + 1], self.start_date, invest_type=self.invest_type)
for idx in range(len(portfolio) - 1):
prod1 = get_tamp_nav(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'] == self.portfolio[idx + 1]]
result = fund_info[fund_info['fund_id'] == 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(self.portfolio[idx + 1])
self.no_data_fund.append(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[self.portfolio[idx + 1]] = replaced_fund
self.replace_pair[portfolio[idx + 1]] = replaced_fund
self.freq_list.append(get_frequency(prod1))
prod1 = rename_col(prod1, replaced_fund)
else:
self.no_data_fund.append(self.portfolio[idx + 1])
self.no_data_fund.append(portfolio[idx + 1])
continue
else:
self.freq_list.append(get_frequency(prod1))
prod1 = rename_col(prod1, self.portfolio[idx + 1])
prod1 = rename_col(prod1, portfolio[idx + 1])
# 取prod表和prod1表的并集
prod = pd.merge(prod, prod1, on=['end_date'], how='outer')
......@@ -423,8 +503,7 @@ class PortfolioDiagnose(object):
# if proposal_z_score > 80 and proposal_strategy in add_strategy:
if proposal_z_score > 60:
proposal_nav = get_nav(proposal, self.start_date, invest_type=self.invest_type)
proposal_nav = get_tamp_nav(proposal, self.start_date, invest_type=self.invest_type)
# 忽略净值周期大于周更的产品
if get_frequency(proposal_nav) <= 52:
continue
......@@ -436,6 +515,7 @@ 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)
......@@ -470,37 +550,55 @@ class PortfolioDiagnose(object):
end3 = time.time()
print("遍历产品池获取候选推荐时间：", end3 - end2)
# propose_portfolio.to_csv('test_portfolio.csv', encoding='gbk')
mu = expected_returns.mean_historical_return(self.propose_portfolio, frequency=min(self.freq_list))
S = risk_models.sample_cov(self.propose_portfolio, frequency=min(self.freq_list))
dd = expected_returns.drawdown_from_prices(self.propose_portfolio)
# if self.client_type == 1:
# proposal_risk = [[x, get_risk_level(search_rank(fund_rank, x, metric='substrategy'))] for x in
# propose_portfolio.columns]
# self.proposal_fund = list(filter(lambda x: x[1] != 'H', proposal_risk))
# drop_fund_list = list(filter(lambda x: x[1] = 'H', proposal_risk))
# proposal_portfolio = list((set(self.portfolio) - set(self.no_data_fund) - set(self.replace_pair.keys())) | \
# (set(self.proposal_fund) | set(self.replace_pair.values())))
# propose_portfolio.drop()
# proposal_risk = [[x, get_risk_level(search_rank(fund_rank, x, metric='substrategy'))] for x in
# self.propose_portfolio.columns]
# self.proposal_fund = list(filter(lambda x: x[1] != 'H', proposal_risk))
# drop_fund_list = list(filter(lambda x: x[1] == 'H', proposal_risk))
# drop_fund_list = [x[0] for x in drop_fund_list]
# self.propose_portfolio.drop(drop_fund_list, axis=1, inplace=True)
# mu = expected_returns.mean_historical_return(self.propose_portfolio, frequency=min(self.freq_list))
# S = risk_models.sample_cov(self.propose_portfolio, frequency=min(self.freq_list))
# dd = expected_returns.drawdown_from_prices(self.propose_portfolio)
propose_risk_mapper = dict()
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": 0.0}
risk_lower = {"L": 0.0}
elif self.client_type == 2:
risk_upper = {"H": 0.0}
risk_lower = {"L": 0.0}
elif self.client_type == 3:
risk_upper = {"H": 0.0}
risk_lower = {"L": 0.0}
elif self.client_type == 4:
risk_upper = {"H": 0.0}
risk_lower = {"L": 0.0}
elif self.client_type == 5:
risk_upper = {"H": 0.0}
risk_lower = {"L": 0.0}
else:
risk_upper = {"H": 0.0}
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, target_drawdown=self.expect_drawdown)
clean_weights = ef.clean_weights()
# ef.portfolio_performance(verbose=True)
self.new_weights = np.array(list(clean_weights.values()))
self.new_weights[self.new_weights < w_low] = 0
print(clean_weights)
print(self.new_weights)
end4 = time.time()
print("模型计算一次时间：", end4 - end3)
# S = np.asmatrix(S)
......@@ -524,7 +622,7 @@ 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
......@@ -570,7 +668,7 @@ 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:
......@@ -639,7 +737,6 @@ class PortfolioDiagnose(object):
return ret
def new_evaluation(self, group_name, group_result, data_adaptor):
group_result_data = group_result[group_name]
hold_info = group_result_data["group_hoding_info"]
hold_info_df = pd.DataFrame(hold_info)
......@@ -647,7 +744,7 @@ 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"]
......@@ -670,9 +767,11 @@ class PortfolioDiagnose(object):
# 基金名称，策略分级
propose_fund_id_name_list = [propose_fund_df[propose_fund_df["fund_id"] == fund_id]["fund_name"].values[0] for
fund_id in propose_fund_id_list]
propose_fund_id_strategy_name_list = [dict_substrategy[int(propose_fund_df[propose_fund_df["fund_id"] == fund_id]["substrategy"].values[0])] for
fund_id in propose_fund_id_list]
propose_fund_asset = [round(self.new_weights[i] * total_asset, 2) for i in range(len(propose_fund_id_name_list))]
propose_fund_id_strategy_name_list = [
dict_substrategy[int(propose_fund_df[propose_fund_df["fund_id"] == fund_id]["substrategy"].values[0])] for
fund_id in propose_fund_id_list]
propose_fund_asset = [round(self.new_weights[i] * total_asset, 2) for i in
range(len(propose_fund_id_name_list))]
propose_info = {propose_fund_id_strategy_name_list[i]:
{"fund_name": propose_fund_id_name_list[i],
......@@ -714,63 +813,72 @@ class PortfolioDiagnose(object):
# 新组合累积收益df
propose_fund_return_limit_data = propose_fund_return[propose_fund_return.index >= group_order_start_date]
start_return = propose_fund_return_limit_data['return'].values[0]
propose_fund_return_limit_data["new_return"] = (propose_fund_return_limit_data["return"] - start_return)/(1+start_return)
propose_fund_return_limit_data["new_return"] = (propose_fund_return_limit_data["return"] - start_return) / (
1 + start_return)
# 新组合累积收益
new_return_ratio = propose_fund_return_limit_data["new_return"].values[-1]
# 新组合区间年化收益率
freq_max = group_order_df["freq"].max()
n_freq = freq_days(int(freq_max))
new_return_ratio_year = annual_return(propose_fund_return_limit_data["new_return"].values[-1], propose_fund_return_limit_data, n_freq)
new_return_ratio_year = annual_return(propose_fund_return_limit_data["new_return"].values[-1],
propose_fund_return_limit_data, n_freq)
# 新组合波动率
new_volatility = volatility(propose_fund_return_limit_data["new_return"]+1, n_freq)
new_volatility = volatility(propose_fund_return_limit_data["new_return"] + 1, n_freq)
# 新组合最大回撤
new_drawdown = max_drawdown(propose_fund_return_limit_data["new_return"]+1)
new_drawdown = max_drawdown(propose_fund_return_limit_data["new_return"] + 1)
# 新组合夏普比率
sim = simple_return(propose_fund_return_limit_data["new_return"]+1)
sim = simple_return(propose_fund_return_limit_data["new_return"] + 1)
exc = excess_return(sim, BANK_RATE, n_freq)
new_sharpe = sharpe_ratio(exc, sim, n_freq)
# 指数收益
index_return = index_return[index_return.index >= group_order_start_date]
start_index_return = index_return[" close"].values[0]
index_return["new_index_return"] = (index_return[" close"] - start_index_return) / (1 + start_index_return)
start_index_return = index_return["adj_nav"].values[0]
index_return["new_index_return"] = (index_return["adj_nav"] - start_index_return) / (1 + start_index_return)
index_return_ratio = index_return["new_index_return"].values[-1]
index_return_ratio_year = annual_return(index_return["new_index_return"].values[-1], index_return["new_index_return"], n_freq)
index_volatility = volatility(index_return["new_index_return"]+1, n_freq)
index_drawdown = max_drawdown(index_return["new_index_return"]+1)
index_sim = simple_return(propose_fund_return_limit_data["new_return"]+1)
index_return_ratio_year = annual_return(index_return["new_index_return"].values[-1],
index_return["new_index_return"], n_freq)
index_volatility = volatility(index_return["new_index_return"] + 1, n_freq)
index_drawdown = max_drawdown(index_return["new_index_return"] + 1)
index_sim = simple_return(propose_fund_return_limit_data["new_return"] + 1)
index_exc = excess_return(index_sim, BANK_RATE, n_freq)
index_sharpe = sharpe_ratio(index_exc, index_sim, n_freq)
# 收益对比数据
return_compare_df = pd.merge(index_return[["new_index_return"]], old_return_df[["cum_return_ratio"]], right_index=True,
left_index=True)
return_compare_df = pd.merge(index_return[["new_index_return"]], old_return_df[["cum_return_ratio"]],
right_index=True,
left_index=True)
return_compare_df = pd.merge(return_compare_df, propose_fund_return_limit_data["new_return"], right_index=True,
left_index=True)
left_index=True)
return_compare_df["date"] = return_compare_df.index
return_compare_df["date"] = return_compare_df["date"].apply(lambda x: x.strftime("%Y-%m-%d"))
return_compare_df.iloc[1:-1,:]["date"] = ""
return_compare_df.iloc[1:-1, :]["date"] = ""
return_compare_result = {
"new_combination": {"name": "新组合", "data": return_compare_df["new_return"].values*100},
"index": {"name": "中证500", "data": return_compare_df["new_index_return"].values*100},
"origin_combination": {"name": "原组合", "data": return_compare_df["cum_return_ratio"].values*100},
"new_combination": {"name": "新组合", "data": return_compare_df["new_return"].values},
"index": {"name": "中证500", "data": return_compare_df["new_index_return"].values},
"origin_combination": {"name": "原组合", "data": return_compare_df["cum_return_ratio"].values},
"xlabels": return_compare_df["date"].values
}
# 指标对比
old_indicator = {"group_name": "现有持仓组合", "return_ratio": round((old_return-1)*100, 2), "return_ratio_year": round(old_return_ratio_year*100,2),
"volatility": round(old_volatility*100, 2), "max_drawdown": round(old_max_drawdown[0]*100, 2), "sharpe": round(old_sharpe, 2)}
new_indicator = {"group_name": "建议优化组合", "return_ratio": round(new_return_ratio*100, 2), "return_ratio_year": round(new_return_ratio_year*100, 2),
"volatility": round(new_volatility*100, 2), "max_drawdown": round(new_drawdown[0]*100, 2), "sharpe": round(new_sharpe, 2)}
index_indicator = {"group_name": "中证500", "return_ratio": round(index_return_ratio*100, 2), "return_ratio_year": round(index_return_ratio_year*100, 2),
"volatility": round(index_volatility*100, 2), "max_drawdown": round(index_drawdown[0]*100, 2), "sharpe": round(index_sharpe, 2)}
old_indicator = {"group_name": "现有持仓组合", "return_ratio": round((old_return - 1) * 100, 2),
"return_ratio_year": round(old_return_ratio_year * 100, 2),
"volatility": round(old_volatility * 100, 2),
"max_drawdown": round(old_max_drawdown[0] * 100, 2), "sharpe": round(old_sharpe, 2)}
new_indicator = {"group_name": "建议优化组合", "return_ratio": round(new_return_ratio * 100, 2),
"return_ratio_year": round(new_return_ratio_year * 100, 2),
"volatility": round(new_volatility * 100, 2), "max_drawdown": round(new_drawdown[0] * 100, 2),
"sharpe": round(new_sharpe, 2)}
index_indicator = {"group_name": "中证500", "return_ratio": round(index_return_ratio * 100, 2),
"return_ratio_year": round(index_return_ratio_year * 100, 2),
"volatility": round(index_volatility * 100, 2),
"max_drawdown": round(index_drawdown[0] * 100, 2), "sharpe": round(index_sharpe, 2)}
indicator_compare = [new_indicator, old_indicator, index_indicator]
# 在保留{}的基础上，建议赎回{}，并增配{}后，整体组合波动率大幅降低，最大回撤从{}降到不足{}，年化收益率提升{}个点
hold_fund = set(self.portfolio) - set(self.abandon_fund_score + self.abandon_fund_corr + self.no_data_fund)
hold_fund_name = [get_fund_name(x).values[0][0] for x in hold_fund]
......@@ -820,6 +928,16 @@ 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,
......@@ -843,7 +961,7 @@ 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]}
......@@ -880,7 +998,7 @@ 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；"}
......@@ -902,6 +1020,14 @@ 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"
......@@ -940,14 +1066,14 @@ 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())
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