def save_dataview():
data_config = {
"remote.data.address":
"tcp://data.tushare.org:8910",
"remote.data.username":
"******",
"remote.data.password":
"******" #QuantOs API令牌
}
ds = RemoteDataService()
ds.init_from_config(data_config)
dv = DataView()
props = {
'start_date': 20140101,
'end_date': 20180101,
'universe': '000300.SH', #'universe': '000300.SH'表示标的股票池取的是沪深300
'fields':
"pb,pe,ps,float_mv,sw1,roe", #'fields'表示取的数据,pb,pe,ps分别表示市净率,市盈率,市销率;float_mv代表流通市值,sw1表示申万1级行业分类的数据
'freq': 1
}
dv.init_from_config(props, ds)
dv.prepare_data()
dv.save_dataview(dataview_folder) # 保存数据文件到指定路径，方便下次直接加载
def save_dataview():
data_config = {
"remote.data.address":
"tcp://data.quantOS.org:8910",
"remote.data.username":
"******",
"remote.data.password":
"******"
}
ds = RemoteDataService()
ds.init_from_config(data_config)
dv = DataView()
props = {
'start_date': 20140101,
'end_date': 20180101,
'universe': '000300.SH',
'fields': "volume,pb,pe,ps,roe,float_mv,sw1",
'freq': 1,
'timeout': 180
}
dv.init_from_config(props, ds)
dv.prepare_data()
dv.save_dataview(dataview_folder) # 保存数据文件到指定路径，方便下次直接加载
def test_write():
ds = RemoteDataService()
ds.init_from_config(data_config)
dv = DataView()
secs = '600030.SH,000063.SZ,000001.SZ'
props = {
'start_date': 20160601,
'end_date': 20170601,
'symbol': secs,
'fields': 'open,close,high,low,volume,pb,net_assets,pcf_ncf',
'freq': 1
}
dv.init_from_config(props, data_api=ds)
dv.prepare_data()
assert dv.data_d.shape == (281, 48)
assert dv.dates.shape == (281, )
# TODO
"""
PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block1_values] [items->[('000001.SZ', 'int_income'), ('000001.SZ', 'less_handling_chrg_comm_exp'), ('000001.SZ', 'net_int_income'), ('000001.SZ', 'oper_exp'), ('000001.SZ', 'symbol'), ('000063.SZ', 'int_income'), ('000063.SZ', 'less_handling_chrg_comm_exp'), ('000063.SZ', 'net_int_income'), ('000063.SZ', 'oper_exp'), ('000063.SZ', 'symbol'), ('600030.SH', 'int_income'), ('600030.SH', 'less_handling_chrg_comm_exp'), ('600030.SH', 'net_int_income'), ('600030.SH', 'oper_exp'), ('600030.SH', 'symbol')]]
"""
dv.save_dataview(folder_path=daily_path)
def test_q():
ds = RemoteDataService()
ds.init_from_config(data_config)
dv = DataView()
secs = '600030.SH,000063.SZ,000001.SZ'
props = {
'start_date':
20160609,
'end_date':
20170601,
'symbol':
secs,
'fields':
('open,close,' + 'pb,net_assets,' + 'total_oper_rev,oper_exp,' +
'cash_paid_invest,' + 'capital_stk,' + 'roe'),
'freq':
1
}
dv.init_from_config(props, data_api=ds)
dv.prepare_data()
dv.save_dataview(folder_path=quarterly_path)
print(group2_code.tail()
) #value="industry1_code"表示以industry2_name为分类标准,不过返回的code类型(类别代号)
group3 = dp.daily_sec_industry(api,
symbols,
start,
end,
source='zz',
value="industry1_name")
print(group3.tail()) #source='zz'表示以中证为分类标准,只是分类标准不同而已
group3_code = dp.daily_sec_industry(api,
symbols,
start,
end,
source='zz',
value="industry1_code")
print(group3_code.tail()
) #source='zz'表示以中证为分类标准,只是分类标准不同而已,industry1_code返回的是类别代号(code类型)
#3_添加数据保存
dv = DataView()
dataview_folder = 'G:/data/hs300' #档案地址
dv.load_dataview(dataview_folder) #加载档案地址
dv.append_df(group, 'group') #将group列加入dv中,后面的'group'为列名
dv.save_dataview('G:/data/hs300') #保存
#获取数据
print(dv.get_ts('group').tail())
A = dv.get_ts('group') #这样看的更清楚点
add_data=True)
Factor8 = dv.add_formula('Factor8',
'-Ts_Sum(Max(a,0),20)',
is_quarterly=False,
add_data=True)
Factor9 = dv.add_formula('Factor9',
'-ps-pb-0.1*pe',
is_quarterly=False,
add_data=True)
Factor10 = dv.add_formula('Factor10',
'-BullPower*volume',
is_quarterly=False,
add_data=True)
dv.save_dataview(dataview_folder)
alpha_signal = [
'Factor1', 'Factor2', 'Factor3', 'Factor4', 'Factor5', 'Factor6',
'Factor7', 'Factor8', 'Factor9', 'Factor10'
]
for a in alpha_signal:
print((dv.get_ts(a)).shape)
import matplotlib.pyplot as plt
from jaqs_fxdayu.research import SignalDigger
from jaqs_fxdayu.util import dp
def limit_up_down():
# 定义可买卖条件——未停牌、未涨跌停
"eyJhbGciOiJIUzI1NiJ9.eyJjcmVhdGVfdGltZSI6IjE1NjM5NDc0MzYwMzEiLCJpc3MiOiJhdXRoMCIsImlkIjoiMTc2NjUxMzk1OTcifQ.crFpJTlrqcduHE7zEMms2XhAoggttMLlFSkGWAzyNhY"
}
# step 2
ds = RemoteDataService()
ds.init_from_config(data_config)
dv = DataView()
sub_period = [(20190130, 20190630)]
file_name = ['data']
universe = '000300.SH'
# step 3
for i in range(len(sub_period)):
props = {
'start_date': sub_period[i][0],
'end_date': sub_period[i][1],
'universe': universe,
'fields': "pb,pe,oper_exp,sw1",
'report_type': '408003000',
'freq': 1
}
dv.init_from_config(props, ds)
dv.prepare_data()
dv.save_dataview('C:/Users/Lenovo/PycharmProjects/my_factor/' +
file_name[i] + '_' + universe[0:6])
print(sub_period[i], 'finished')
is_quarterly=False,
add_data=True)
can_enter = np.logical_and(dv.get_ts('up_limit') < 1, ~mask_sus) # 未涨停未停牌
can_exit = np.logical_and(dv.get_ts('down_limit') < 1, ~mask_sus) # 未跌停未停牌
return can_enter, can_exit
mask = mask_index_member()
can_enter, can_exit = limit_up_down()
#step2: #用append_df的方法将slope_df添加至本地(dataview数据集里)
print(dv.fields) #查看dv中取得的数据
dv.append_df(field_name="Union", df=Union, is_quarterly=False
) #用append_df方法将"Union"添加至本地(dataview数据集里),其新名称为df=(Union)
dv.append_df(field_name="Intersection", df=Intersection, is_quarterly=False)
dv.save_dataview('G:/data/hs300_2') #保存,若不运行,则下次重新运行不会有Union和Intersection
#step3:新因子分析:
from jaqs_fxdayu.research import SignalDigger
import matplotlib.pyplot as plt
from jaqs_fxdayu.research.signaldigger.analysis import analysis
obj = SignalDigger(output_folder='G:/data', output_format='pdf')
def draw_analysis(
signal="Union",
benchmark_price=None): #同样类似于第二章SignalDigger处理因子的方式,不过这里是定义函数
obj.process_signal_before_analysis(
signal=dv.get_ts(signal),
price=dv.get_ts("close_adj"),
