def test_universe_equal(self):
universe1 = Universe('custom', ['zz500'])
universe2 = Universe('custom', ['zz500'])
self.assertEqual(universe1, universe2)
universe1 = Universe('custom', ['zz500'])
universe2 = Universe('custom', ['zz800'])
self.assertNotEqual(universe1, universe2)
filter_cond = LAST('x') > 1.
universe1 = Universe('custom', ['zz500'], filter_cond=filter_cond)
universe2 = Universe('custom', ['zz500'], filter_cond=filter_cond)
self.assertEqual(universe1, universe2)
universe1 = Universe('custom', ['zz500'], filter_cond=LAST('x') > 1.)
universe2 = Universe('custom', ['zz500'], filter_cond=LAST('x') > 2.)
self.assertNotEqual(universe1, universe2)
def __init__(self):
self.closes = CLOSE()
self.mul = LAST('multiplier')
self.preMul = LAST('multiplier').shift(1)
self.instrumentID = LAST('instrumentID')
self.preInstrumentID = LAST('instrumentID').shift(1)
self.preCloses = CLOSE().shift(1)
self.openes = OPEN()
self.lowes = LOW()
self.highes = HIGH()
# 计算指数的备用指标
self.closeDLastClose = self.closes / self.preCloses - 1.
self.closeDOpen = self.closes / self.openes - 1.
self.closeDLow = self.closes / self.lowes - 1.
self.highDClose = 1. - self.highes / self.closes
# 定义指数的权重
self.indexWeights = pd.Series([1., -2., 1.], index=['000016.zicn', '000300.zicn', '000905.zicn'])
def __init__(self):
self.closes = CLOSE()
self.mul = LAST('multiplier')
self.preMul = LAST('multiplier').shift(1)
self.preCloses = CLOSE().shift(1)
self.openes = OPEN()
self.lowes = LOW()
self.highes = HIGH()
# 计算指数的备用指标
self.closeDLastClose = self.closes / self.preCloses - 1.
self.closeDOpen = self.closes / self.openes - 1.
self.closeDLow = self.closes / self.lowes - 1.
self.highDClose = 1. - self.highes / self.closes
self.ih_amount = 28
self.if_amount = 42
self.ic_amount = 12
# 定义指数的权重
names = ['000016.zicn', '000300.zicn', '000905.zicn']
self.indexWeights = SeriesValues(np.array([1., -2., 1.]),
index=dict(zip(names, range(len(names)))))
def test_universe_persistence(self):
universe = Universe('custom', ['zz500'])
univ_desc = universe.save()
loaded_universe = Universe.load(univ_desc)
self.assertEqual(universe.name, loaded_universe.name)
self.assertListEqual(universe.base_universe,
loaded_universe.base_universe)
universe = Universe('custom', ['zz500'], filter_cond=LAST('x') > 1.)
univ_desc = universe.save()
loaded_universe = Universe.load(univ_desc)
self.assertEqual(universe.name, loaded_universe.name)
self.assertListEqual(universe.base_universe,
loaded_universe.base_universe)
self.assertEqual(str(universe.filter_cond),
str(loaded_universe.filter_cond))
freq = '60b'
universe = Universe('custom', ['ashare_ex'])
batch = 1
neutralized_risk = industry_styles
risk_model = 'short'
pre_process = [winsorize_normal, standardize]
post_process = [standardize]
warm_start = 3
data_source = None
horizon = map_freq(freq)
engine = SqlEngine(data_source)
fit_intercept = True
kernal_feature = 'roe_q'
regress_features = {kernal_feature: LAST(kernal_feature),
kernal_feature + '_l1': SHIFT(kernal_feature, 1),
kernal_feature + '_l2': SHIFT(kernal_feature, 2),
kernal_feature + '_l3': SHIFT(kernal_feature, 3)
}
const_features = {kernal_feature: LAST(kernal_feature)}
fit_target = [kernal_feature]
data_meta = DataMeta(freq=freq,
universe=universe,
batch=batch,
neutralized_risk=neutralized_risk,
risk_model=risk_model,
pre_process=pre_process,
post_process=post_process,
warm_start=warm_start,
from matplotlib.pylab import mpl
plt.style.use('seaborn-whitegrid')
mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
start_date = '2017-01-01'
end_date = '2018-05-17'
freq = '10b'
neutralized_risk = None
universe = Universe('zz800')
dask_client = Client('10.63.6.176:8786')
alpha_factors = {
'f1':
CSQuantiles(LAST('ILLIQUIDITY') * LAST('NegMktValue'),
groups='sw1_adj'),
'f2':
CSQuantiles('con_pe', groups='sw1_adj')
}
weights = {'f1': 1., 'f2': 0.}
# alpha_model = XGBTrainer(objective='reg:linear',
# booster='gbtree',
# n_estimators=300,
# eval_sample=0.25,
# features=alpha_factors)
alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)
plt.style.use('seaborn-whitegrid')
mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
"""
Back test parameter settings
"""
benchmark_code = 905
universe = Universe('zz800') + Universe('cyb')
start_date = '2011-01-01'
end_date = '2011-05-04'
freq = '10b'
neutralized_risk = None
alpha_factors = {'ep_q_cs': CSQuantiles(LAST('ep_q'), groups='sw1_adj')}
weights = dict(ep_q_cs=1.)
alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)
data_meta = DataMeta(freq=freq,
universe=universe,
batch=1,
neutralized_risk=None,
pre_process=None,
post_process=None,
data_source=os.environ['DB_URI'])
strategy = Strategy(alpha_model,
data_meta,