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alpha-mind
Commit 0b987f3e authored by Dr.李's avatar Dr.李
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update store and analysis

parent 6eb178dc
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Showing with 42 additions and 38 deletions
alphamind/analysis/factoranalysis.py
View file @ 0b987f3e
......@@ -162,7 +162,8 @@ def factor_analysis(factors: pd.DataFrame,
detail_analysis=True,
benchmark: Optional[np.ndarray]=None,
risk_exp: Optional[np.ndarray]=None,
is_tradable: Optional[np.ndarray]=None) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]:
is_tradable: Optional[np.ndarray]=None,
method='risk_neutral') -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]:
data_pack = FDataPack(raw_factors=factors.values,
d1returns=d1returns,
......@@ -172,7 +173,7 @@ def factor_analysis(factors: pd.DataFrame,
er = data_pack.factor_processing([winsorize_normal, standardize]) @ factor_weights
if benchmark is not None and risk_exp is not None:
if benchmark is not None and risk_exp is not None and method == 'risk_neutral':
# using linear programming portfolio builder
benchmark = benchmark.flatten()
lbound = 0.
......
alphamind/data/store.py
View file @ 0b987f3e
......@@ -12,12 +12,12 @@ import numpy as np
import pandas as pd
db_settings = {
'uqer':
'alpha':
{
'user': 'sa',
'password': 'We051253524522',
'host': 'rm-bp1psdz5615icqc0yo.mysql.rds.aliyuncs.com',
'db': 'uqer',
'user': 'licheng',
'password': 'A12345678!',
'host': '10.63.6.220',
'db': 'alpha',
'charset': 'utf8'
}
}
......@@ -31,7 +31,8 @@ risk_styles = ['BETA',
'BTOP',
'LEVERAGE',
'LIQUIDTY',
'SIZENL']
'SIZENL',
'COUNTRY']
industry_styles = [
'Bank',
......@@ -82,8 +83,9 @@ def fetch_codes(codes: Union[str, Iterable[int]], start_date, end_date, engine):
return code_table, code_str
def industry_mapping(industry_arr, industry_dummies):
return [industry_arr[row][0] for row in industry_dummies]
def industry_mapping(industry_arr, industry_codes, industry_dummies):
return [industry_arr[row][0] for row in industry_dummies], \
[industry_codes[row][0] for row in industry_dummies],
def fetch_data(factors: Iterable[str],
......@@ -92,10 +94,10 @@ def fetch_data(factors: Iterable[str],
codes: Union[str, Iterable[int]] = None,
benchmark: int = None,
risk_model: str = 'day') -> dict:
engine = sa.create_engine('mysql+mysqldb://{user}:{password}@{host}/{db}?charset={charset}'
.format(**db_settings['uqer']))
engine = sa.create_engine('mssql+pymssql://{user}:{password}@{host}/{db}?charset={charset}'
.format(**db_settings['alpha']))
factor_str = ','.join('factors.' + f for f in factors)
factor_str = ','.join('uqer.' + f for f in factors)
code_table, code_str = fetch_codes(codes, start_date, end_date, engine)
total_risk_factors = risk_styles + industry_styles
......@@ -104,30 +106,30 @@ def fetch_data(factors: Iterable[str],
special_risk_table = 'specific_risk_' + risk_model
if code_str:
sql = "select factors.Date, factors.Code, {0}, {3}, market.isOpen, daily_return.d1, {5}.SRISK" \
" from (factors INNER JOIN" \
" risk_exposure on factors.Date = risk_exposure.Date and factors.Code = risk_exposure.Code)" \
" INNER JOIN market on factors.Date = market.Date and factors.Code = market.Code" \
" INNER JOIN daily_return on factors.Date = daily_return.Date and factors.Code = daily_return.Code" \
" INNER JOIN {5} on factors.Date = {5}.Date and factors.Code = {5}.Code" \
" where factors.Date >= '{1}' and factors.Date <= '{2}' and factors.Code in ({4})".format(factor_str,
start_date,
end_date,
risk_str,
code_str,
special_risk_table)
sql = "select uqer.Date, uqer.Code, {0}, {3}, market.isOpen, daily_return.d1, {5}.SRISK" \
" from (uqer INNER JOIN" \
" risk_exposure on uqer.Date = risk_exposure.Date and uqer.Code = risk_exposure.Code)" \
" INNER JOIN market on uqer.Date = market.Date and uqer.Code = market.Code" \
" INNER JOIN daily_return on uqer.Date = daily_return.Date and uqer.Code = daily_return.Code" \
" INNER JOIN {5} on uqer.Date = {5}.Date and uqer.Code = {5}.Code" \
" where uqer.Date >= '{1}' and uqer.Date <= '{2}' and uqer.Code in ({4})".format(factor_str,
start_date,
end_date,
risk_str,
code_str,
special_risk_table)
else:
sql = "select factors.Date, factors.Code, {0}, {3}, market.isOpen, daily_return.d1, {4}.SRISK" \
" from (factors INNER JOIN" \
" risk_exposure on factors.Date = risk_exposure.Date and factors.Code = risk_exposure.Code)" \
" INNER JOIN market on factors.Date = market.Date and factors.Code = market.Code" \
" INNER JOIN daily_return on factors.Date = daily_return.Date and factors.Code = daily_return.Code" \
" INNER JOIN {4} on factors.Date = {4}.Date and factors.Code = {4}.Code" \
" where factors.Date >= '{1}' and factors.Date <= '{2}'".format(factor_str,
start_date,
end_date,
risk_str,
special_risk_table)
sql = "select uqer.Date, uqer.Code, {0}, {3}, market.isOpen, daily_return.d1, {4}.SRISK" \
" from (uqer INNER JOIN" \
" risk_exposure on uqer.Date = risk_exposure.Date and uqer.Code = risk_exposure.Code)" \
" INNER JOIN market on uqer.Date = market.Date and uqer.Code = market.Code" \
" INNER JOIN daily_return on uqer.Date = daily_return.Date and uqer.Code = daily_return.Code" \
" INNER JOIN {4} on uqer.Date = {4}.Date and uqer.Code = {4}.Code" \
" where uqer.Date >= '{1}' and uqer.Date <= '{2}'".format(factor_str,
start_date,
end_date,
risk_str,
special_risk_table)
factor_data = pd.read_sql(sql, engine)
......@@ -147,7 +149,7 @@ def fetch_data(factors: Iterable[str],
total_data = {'factor': factor_data, 'risk_cov': risk_cov_data}
if benchmark:
sql = "select Date, Code, weight from index_components " \
sql = "select Date, Code, weight / 100. as weight from index_components " \
"where Date >= '{0}' and Date <= '{1}' and indexCode = {2}".format(start_date,
end_date,
benchmark)
......@@ -156,9 +158,10 @@ def fetch_data(factors: Iterable[str],
total_data['benchmark'] = benchmark_data
industry_arr = np.array(industry_styles)
industry_codes = np.arange(len(industry_styles), dtype=int)
industry_dummies = factor_data[industry_styles].values.astype(bool)
factor_data['industry'] = industry_mapping(industry_arr, industry_dummies)
factor_data['industry'], factor_data['industry_code'] = industry_mapping(industry_arr, industry_codes, industry_dummies)
return total_data
......
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