def pipeline_columns_and_mask(self):
universe = make_china_equity_universe(
target_size=3000,
mask=default_china_equity_universe_mask([risk_benchmark]),
max_group_weight=0.01,
smoothing_func=lambda f: f.downsample('month_start'),
)
private_universe = private_universe_mask(
self.portfolio.index) # 把当前组合的stock 包含在universe中
last_price = USEquityPricing.close.latest >= 1.0 # 大于1元
universe = (universe & last_price) & ~private_universe
# print "universe:",universe
# Instantiate ranked factors
returns = Returns(inputs=[USEquityPricing.close],
mask=universe,
window_length=2)
risk_beta = 0.66 * RollingLinearRegressionOfReturns(
target=symbol(risk_benchmark), # sid(8554),
returns_length=5,
regression_length=21,
# mask=long_short_screen
mask=(universe),
).beta + 0.33 * 1.0
returns.window_safe = True
risk_beta.window_safe = True
predict = RNNPredict(universe, trigger_date=self.predict_time) # 进行回顾
universe = predict.top(20)
weights = Markowitz(inputs=[returns, risk_beta],
window_length=4,
mask=universe,
trigger_date=self.predict_time) #进行回顾
columns = {
'predict': predict,
'weights': weights,
}
return columns, universe
def pipeline_columns_and_mask(self):
'''
universe = make_china_equity_universe(
target_size=3000,
mask=default_china_equity_universe_mask([RISK_BENCHMARK]),
max_group_weight=0.01,
smoothing_func=lambda f: f.downsample('month_start'),
)
private_universe = private_universe_mask(self.portfolio.index) # 把当前组合的stock 包含在universe中
'''
last_price = USEquityPricing.close.latest >= 1.0 # 大于1元
sector = get_sector()
sector_filter = sector != 0.0
universe = last_price & sector_filter
# print "universe:",universe
# Instantiate ranked factors
returns = Returns(inputs=[USEquityPricing.close],
mask=universe,
window_length=2)
risk_beta = 0.66 * RollingLinearRegressionOfReturns(
target=symbol(RISK_BENCHMARK),
returns_length=5,
regression_length=21,
# mask=long_short_screen
mask=(universe),
).beta + 0.33 * 1.0
returns.window_safe = True
risk_beta.window_safe = True
predict = RNNPredict(universe,
source='predict.csv',
trigger_date=self.predict_time) # 进行回顾
columns = {
'predict': predict,
'market_beta': risk_beta,
'sector': sector,
}
return columns, universe
