def __make_factors(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'),
)
last_price = USEquityPricing.close.latest >= 1.0 # 大于1元
universe = universe & last_price
# market cap and book-to-price data gets fed in here
outstanding = Fundamental().outstanding
outstanding.window_safe = True
market_cap = Latest([outstanding])
book_to_price = SimpleBookToPrice()
momentum = SimpleMomentum(mask=universe)
alpha5 = Alpha5(mask=universe)
alpha8 = Alpha8(mask=universe)
alpha9 = Alpha9(mask=universe)
all_factors = {
'market_cap': market_cap.downsample('month_start'),
'book_to_price': book_to_price.downsample('month_start'),
'momentum': momentum,
'alpha5': alpha5,
'alpha8': alpha8,
'alpha9': alpha9,
}
return all_factors, universe
def make_pipeline(asset_finder):
h2o = USEquityPricing.high.latest / USEquityPricing.open.latest
l2o = USEquityPricing.low.latest / USEquityPricing.open.latest
c2o = USEquityPricing.close.latest / USEquityPricing.open.latest
h2c = USEquityPricing.high.latest / USEquityPricing.close.latest
l2c = USEquityPricing.low.latest / USEquityPricing.close.latest
h2l = USEquityPricing.high.latest / USEquityPricing.low.latest
vol = USEquityPricing.volume.latest
outstanding = Fundamental(asset_finder).outstanding
outstanding.window_safe = True
turnover_rate = vol / Latest([outstanding])
returns = Returns(inputs=[USEquityPricing.close], window_length=5) # 预测一周数据
pipe_columns = {
'h2o': h2o.log1p().zscore(),
'l2o': l2o.log1p().zscore(),
'c2o': c2o.log1p().zscore(),
'h2c': h2c.log1p().zscore(),
'l2c': l2c.log1p().zscore(),
'h2l': h2l.log1p().zscore(),
'vol': vol.zscore(),
'turnover_rate': turnover_rate.log1p().zscore(),
'return': returns.log1p(),
}
# pipe_screen = (low_returns | high_returns)
pipe = Pipeline(columns=pipe_columns)
return pipe