def store_ic_weight():
"""
Calculate IC weight and save it to file
"""
dv = DataView()
dv.load_dataview(folder_path=dataview_dir_path)
factorList = ['TO', 'BP', 'REVS20', 'float_mv_factor']
orthFactor_dic = {}
for factor in factorList:
orthFactor_dic[factor] = {}
# add the orthogonalized factor to dataview
for trade_date in dv.dates:
snapshot = dv.get_snapshot(trade_date)
factorPanel = snapshot[factorList]
factorPanel = factorPanel.dropna()
if len(factorPanel) != 0:
orthfactorPanel = Schmidt(factorPanel)
orthfactorPanel.columns = [x + '_adj' for x in factorList]
snapshot = pd.merge(left=snapshot,
right=orthfactorPanel,
left_index=True,
right_index=True,
how='left')
for factor in factorList:
orthFactor_dic[factor][trade_date] = snapshot[factor]
for factor in factorList:
dv.append_df(pd.DataFrame(orthFactor_dic[factor]).T,
field_name=factor + '_adj',
is_quarterly=False)
dv.save_dataview(dataview_dir_path)
factorList_adj = [x + '_adj' for x in factorList]
jutil.save_json(factorList_adj, custom_data_path)
w = get_ic_weight(dv)
store = pd.HDFStore(ic_weight_hd5_path)
store['ic_weight'] = w
store.close()
def test_load():
dv = DataView()
dv.load_dataview(folder_path=daily_path)
assert dv.start_date == 20160601 and set(dv.symbol) == set('000001.SZ,600030.SH,000063.SZ'.split(','))
# test get_snapshot
snap1 = dv.get_snapshot(20170504, symbol='600030.SH,000063.SZ', fields='close,pb')
assert snap1.shape == (2, 2)
assert set(snap1.columns.values) == {'close', 'pb'}
assert set(snap1.index.values) == {'600030.SH', '000063.SZ'}
# test get_ts
ts1 = dv.get_ts('close', symbol='600030.SH,000063.SZ', start_date=20170101, end_date=20170302)
assert ts1.shape == (38, 2)
assert set(ts1.columns.values) == {'600030.SH', '000063.SZ'}
assert ts1.index.values[-1] == 20170302
def store_ic_weight():
"""
Calculate IC weight and save it to file
"""
dv = DataView()
dv.load_dataview(folder_path=dataview_dir_path)
factorList = ['TO', 'BP', 'REVS20', 'float_mv_factor']
orthFactor_dic = {}
for factor in factorList:
orthFactor_dic[factor] = {}
# add the orthogonalized factor to dataview
for trade_date in dv.dates:
snapshot = dv.get_snapshot(trade_date)
factorPanel = snapshot[factorList]
factorPanel = factorPanel.dropna()
if len(factorPanel) != 0:
orthfactorPanel = Schmidt(factorPanel)
orthfactorPanel.columns = [x + '_adj' for x in factorList]
snapshot = pd.merge(left=snapshot, right=orthfactorPanel,
left_index=True, right_index=True, how='left')
for factor in factorList:
orthFactor_dic[factor][trade_date] = snapshot[factor]
for factor in factorList:
dv.append_df(pd.DataFrame(orthFactor_dic[factor]).T, field_name=factor + '_adj', is_quarterly=False)
dv.save_dataview(dataview_dir_path)
factorList_adj = [x + '_adj' for x in factorList]
jutil.save_json(factorList_adj, custom_data_path)
w = get_ic_weight(dv)
store = pd.HDFStore(ic_weight_hd5_path)
store['ic_weight'] = w
store.close()
def test_load():
dv = DataView()
dv.load_dataview(folder_path=daily_path)
assert dv.start_date == 20160601 and set(dv.symbol) == set(
'000001.SZ,600030.SH,000063.SZ'.split(','))
# test get_snapshot
snap1 = dv.get_snapshot(20170504,
symbol='600030.SH,000063.SZ',
fields='close,pb')
assert snap1.shape == (2, 2)
assert set(snap1.columns.values) == {'close', 'pb'}
assert set(snap1.index.values) == {'600030.SH', '000063.SZ'}
# test get_ts
ts1 = dv.get_ts('close',
symbol='600030.SH,000063.SZ',
start_date=20170101,
end_date=20170302)
assert ts1.shape == (38, 2)
assert set(ts1.columns.values) == {'600030.SH', '000063.SZ'}
assert ts1.index.values[-1] == 20170302
#1_读取本地数据
import jaqs_fxdayu
jaqs_fxdayu.patch_all() #新加
from jaqs.data import DataView
from jaqs.data import RemoteDataService
import os
import numpy as np
import warnings
warnings.filterwarnings("ignore")
dv = DataView()
dataview_folder = 'G:/data/hs300' #文件地址
dv.load_dataview(dataview_folder) #读取dataview_folder
#2_读取索引为股票代号的数据 get_snapshot
print(dv.get_snapshot(20170504, symbol='600036.SH,600050.SH', fields=''))
print(
dv.get_snapshot(20170504, symbol='600036.SH,600050.SH',
fields='close_adj'))
#20170504表示时间,symbol='600036.SH,600050.SH'表示股票(可以添加),fields=''表示因子,若不输则会返回全部的因子
#3_读取时间序列数据 get_ts
data1 = dv.get_ts('pb') #返回的是一个DataFrame格式的数据(包含沪深300全部),pb表示平均市净率
print(dv.get_ts('pb').head())
#4_添加自定义算法数据 add_formul
roe_pb = dv.add_formula('roe_pb', 'roe/pb', is_quarterly=False, add_data=True)
#'roe_pb'表示算法的新名称,'roe/pb'为公式,is_quarterly=False代表是否为季度数据
print(dv.get_ts('roe_pb').head()) #这里用get_ts的方法输入新的名称即可
#5_从数据服务添加新数据至本地
dataview_props = { # Start and end date of back-test
'start_date': start_data,
'end_date': cur_data,
# Investment universe and performance benchmark
'universe': UNIVERSE,
'benchmark': '000300.SH',
# Data fields that we need
'fields': 'open,close,high,low',
# freq = 1 means we use daily data. Please do not change this.
'freq': 1
}
ds = RemoteDataService()
ds.init_from_config(data_config)
dv = DataView()
dv.init_from_config(dataview_props, ds)
dv.prepare_data()
# dv.add_formula('tingpai', 'trade_status != \"停牌\"', is_quarterly=False)
dv.add_formula('is_yang',
'(close > open) && ((close - open) / (high -low) >= 0.4)',
is_quarterly=False)
dv.add_formula('three_yang',
'is_yang && Delay(is_yang, 1) && Delay(is_yang, 2)',
is_quarterly=False)
df = dv.get_snapshot(cur_data)
# df[df['three_yang'] == 1.0]
print(df[df['three_yang'] == 1.0])
