def make_pipeline(asset_finder):
private_universe = private_universe_mask( hs300.tolist(),asset_finder=asset_finder)
#print private_universe_mask(['000001','000002','000005'],asset_finder=asset_finder)
######################################################################################################
returns = Returns(inputs=[USEquityPricing.close], window_length=5) # 预测一周数据
######################################################################################################
ep = 1/Fundamental(mask = private_universe,asset_finder=asset_finder).pe
bp = 1/Fundamental(mask = private_universe,asset_finder=asset_finder).pb
bvps = Fundamental(mask = private_universe,asset_finder=asset_finder).bvps
market = Fundamental(mask = private_universe,asset_finder=asset_finder).outstanding
rev20 = Returns(inputs=[USEquityPricing.close], window_length=20,mask = private_universe)
vol20 = AverageDollarVolume(window_length=20,mask = private_universe)
illiq = ILLIQ(window_length=22,mask = private_universe)
rsi = RSI(window_length=22,mask = private_universe)
mom = Momentum(window_length=252,mask = private_universe)
sector = get_sector(asset_finder=asset_finder,mask=private_universe)
ONEHOTCLASS,sector_indict_keys = get_sector_by_onehot(asset_finder=asset_finder,mask=private_universe)
pipe_columns = {
'ep':ep.zscore(groupby=sector).downsample('month_start'),
'bp':bp.zscore(groupby=sector).downsample('month_start'),
'bvps':bvps.zscore(groupby=sector).downsample('month_start'),
'market_cap': market.zscore(groupby=sector).downsample('month_start'),
'vol20':vol20.zscore(groupby=sector),
'rev20':rev20.zscore(groupby=sector),
'ILLIQ':illiq.zscore(groupby=sector,mask=illiq.percentile_between(1, 99)),
'mom' :mom.zscore(groupby=sector,mask=mom.percentile_between(1, 99)),
'rsi' :rsi.zscore(groupby=sector,mask=rsi.percentile_between(1, 99)),
#'sector':sector,
#'returns':returns.quantiles(100),
'returns': returns.zscore(),
}
# pipe_screen = (low_returns | high_returns)
pipe = Pipeline(columns=pipe_columns,
screen=private_universe,
)
i = 0
for c in ONEHOTCLASS:
pipe.add(c,sector_indict_keys[i])
i +=1
return pipe
def pipeline_columns_and_mask(self):
universe = make_china_equity_universe(
target_size=2000,
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
hurst = HurstExp(window_length=int(252 * 0.25),
mask=universe) #判断动量或反转特性指标
sector = get_sector()
#combined_rank = (
# hurst.rank(mask=universe)
#)
pct_slope = Slope(window_length=21, mask=(universe)) #量和价格加速度
risk_beta = 0.66 * RollingLinearRegressionOfReturns(
target=symbol(risk_benchmark),
returns_length=5,
regression_length=21,
mask=(universe),
).beta + 0.33 * 1.0
returns = Returns(inputs=[USEquityPricing.close],
mask=universe,
window_length=2)
#returns.window_safe = True
#risk_beta.window_safe = True
#m = Markowitz(inputs=[returns,risk_beta],window_length=6,mask=universe)
columns = {
'hurst': hurst.downsample('week_start'),
'price_pct_slope': pct_slope.pslope,
'volume_pct_slope': pct_slope.vslope,
'sector': sector.downsample('month_start'),
'market_beta': risk_beta,
'returns': returns,
}
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
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 __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'),
)
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
# 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, algo_mode):
private_universe = private_universe_mask(hs300.tolist(),
asset_finder=asset_finder)
#private_universe = private_universe_mask( ['000005'],asset_finder=asset_finder)
###private_universe = private_universe_mask( ['000002','000005'],asset_finder=asset_finder)
#private_universe = private_universe_mask( ['000001','000002','000005'],asset_finder=asset_finder)
#private_universe = private_universe_mask( ['000001'],asset_finder=asset_finder)
#print private_universe_mask(['000001','000002','000005'],asset_finder=asset_finder)
######################################################################################################
returns = Returns(inputs=[USEquityPricing.close],
window_length=5,
mask=private_universe) # 预测一周数据
######################################################################################################
pe = Fundamental(mask=private_universe, asset_finder=asset_finder).pe
pb = Fundamental(mask=private_universe, asset_finder=asset_finder).pb
bvps = Fundamental(mask=private_universe, asset_finder=asset_finder).bvps
market = Fundamental(mask=private_universe,
asset_finder=asset_finder).outstanding
totals = Fundamental(mask=private_universe,
asset_finder=asset_finder).totals
totalAssets = Fundamental(mask=private_universe,
asset_finder=asset_finder).totalAssets
fixedAssets = Fundamental(mask=private_universe,
asset_finder=asset_finder).fixedAssets
esp = Fundamental(mask=private_universe, asset_finder=asset_finder).esp
rev = Fundamental(mask=private_universe, asset_finder=asset_finder).rev
profit = Fundamental(mask=private_universe,
asset_finder=asset_finder).profit
gpr = Fundamental(mask=private_universe, asset_finder=asset_finder).gpr
npr = Fundamental(mask=private_universe, asset_finder=asset_finder).npr
rev10 = Returns(inputs=[USEquityPricing.close],
window_length=10,
mask=private_universe)
vol10 = AverageDollarVolume(window_length=20, mask=private_universe)
rev20 = Returns(inputs=[USEquityPricing.close],
window_length=20,
mask=private_universe)
vol20 = AverageDollarVolume(window_length=20, mask=private_universe)
rev30 = Returns(inputs=[USEquityPricing.close],
window_length=30,
mask=private_universe)
vol30 = AverageDollarVolume(window_length=20, mask=private_universe)
illiq22 = ILLIQ(window_length=22, mask=private_universe)
illiq5 = ILLIQ(window_length=5, mask=private_universe)
rsi5 = RSI(window_length=5, mask=private_universe)
rsi22 = RSI(window_length=22, mask=private_universe)
mom5 = Momentum(window_length=5, mask=private_universe)
mom22 = Momentum(window_length=22, mask=private_universe)
sector = get_sector(asset_finder=asset_finder, mask=private_universe)
ONEHOTCLASS, sector_indict_keys = get_sector_by_onehot(
asset_finder=asset_finder, mask=private_universe)
pipe_columns = {
'pe': pe.zscore(groupby=sector).downsample('month_start'),
'pb': pb.zscore(groupby=sector).downsample('month_start'),
'bvps': bvps.zscore(groupby=sector).downsample('month_start'),
'market_cap': market.zscore(groupby=sector).downsample('month_start'),
'totals': totals.zscore(groupby=sector).downsample('month_start'),
'totalAssets':
totalAssets.zscore(groupby=sector).downsample('month_start'),
'fixedAssets':
fixedAssets.zscore(groupby=sector).downsample('month_start'),
'esp': esp.zscore(groupby=sector).downsample('month_start'),
'rev': rev.zscore(groupby=sector).downsample('month_start'),
'profit': profit.zscore(groupby=sector).downsample('month_start'),
'gpr': gpr.zscore(groupby=sector).downsample('month_start'),
'npr': npr.zscore(groupby=sector).downsample('month_start'),
'vol10': vol10.zscore(groupby=sector).downsample('week_start'),
'rev10': rev10.zscore(groupby=sector).downsample('week_start'),
'vol20': vol20.zscore(groupby=sector).downsample('week_start'),
'rev20': rev20.zscore(groupby=sector).downsample('week_start'),
'vol30': vol30.zscore(groupby=sector).downsample('week_start'),
'rev30': rev30.zscore(groupby=sector).downsample('week_start'),
'ILLIQ5': illiq5.zscore(groupby=sector).downsample('week_start'),
'ILLIQ22': illiq22.zscore(groupby=sector).downsample('week_start'),
'mom5': mom5.zscore(groupby=sector).downsample('week_start'),
'mom22': mom22.zscore(groupby=sector).downsample('week_start'),
'rsi5': rsi5.zscore(groupby=sector).downsample('week_start'),
'rsi22': rsi22.zscore(groupby=sector).downsample('week_start'),
}
from collections import OrderedDict
factors_pipe = OrderedDict()
factors_pipe['Returns'] = returns
factors_pipe['Returns'].window_safe = True
idx = 0
sort_keys = sorted(pipe_columns)
for key in sort_keys:
#print(key)
factors_pipe[key] = pipe_columns[key]
factors_pipe[key].window_safe = True
idx += 1
if idx == 100:
break
#for name, f in pipe_columns.items():
# f.window_safe = True
# factors_pipe[name] = f
# print (name,f)
# idx += 1
# if idx == 1:
# break
i = 0
for c in ONEHOTCLASS:
c.window_safe = True
factors_pipe[sector_indict_keys[i]] = c
#print (c,sector_indict_keys[i])
i += 1
predict = BasicFactorRegress(inputs=factors_pipe.values(),
window_length=252,
mask=private_universe,
n_fwd_days=5,
algo_mode=algo_mode,
cross=False)
predict_rank = predict.rank(mask=private_universe)
longs = predict_rank.top(NUM_LONG_POSITIONS)
shorts = predict_rank.bottom(NUM_SHORT_POSITIONS)
long_short_screen = (longs | shorts)
#TODO sector onehot
pipe_final_columns = {
'Predict Factor': predict.downsample('week_start'),
'longs': longs.downsample('week_start'),
'shorts': shorts.downsample('week_start'),
'predict_rank': predict_rank.downsample('week_start'),
}
pipe = Pipeline(
columns=pipe_final_columns,
screen=long_short_screen,
)
return pipe
def make_pipeline(asset_finder):
hs300 = ts.get_hs300s()['code']
private_universe = private_universe_mask(hs300.tolist(),
asset_finder=asset_finder)
#private_universe = private_universe_mask(['000001','000002','000005'],asset_finder=asset_finder)
######################################################################################################
returns = Returns(inputs=[USEquityPricing.close],
window_length=5,
mask=private_universe) # 预测一周数据
######################################################################################################
ep = 1 / Fundamental(mask=private_universe, asset_finder=asset_finder).pe
bp = 1 / Fundamental(mask=private_universe, asset_finder=asset_finder).pb
bvps = Fundamental(mask=private_universe, asset_finder=asset_finder).bvps
market = Fundamental(mask=private_universe,
asset_finder=asset_finder).outstanding
totals = Fundamental(mask=private_universe,
asset_finder=asset_finder).totals
totalAssets = Fundamental(mask=private_universe,
asset_finder=asset_finder).totalAssets
fixedAssets = Fundamental(mask=private_universe,
asset_finder=asset_finder).fixedAssets
esp = Fundamental(mask=private_universe, asset_finder=asset_finder).esp
rev = Fundamental(mask=private_universe, asset_finder=asset_finder).rev
profit = Fundamental(mask=private_universe,
asset_finder=asset_finder).profit
gpr = Fundamental(mask=private_universe, asset_finder=asset_finder).gpr
npr = Fundamental(mask=private_universe, asset_finder=asset_finder).npr
rev10 = Returns(inputs=[USEquityPricing.close],
window_length=10,
mask=private_universe)
vol10 = AverageDollarVolume(window_length=20, mask=private_universe)
rev20 = Returns(inputs=[USEquityPricing.close],
window_length=20,
mask=private_universe)
vol20 = AverageDollarVolume(window_length=20, mask=private_universe)
rev30 = Returns(inputs=[USEquityPricing.close],
window_length=30,
mask=private_universe)
vol30 = AverageDollarVolume(window_length=20, mask=private_universe)
illiq22 = ILLIQ(window_length=22, mask=private_universe)
illiq5 = ILLIQ(window_length=5, mask=private_universe)
rsi5 = RSI(window_length=5, mask=private_universe)
rsi22 = RSI(window_length=22, mask=private_universe)
mom5 = Momentum(window_length=5, mask=private_universe)
mom22 = Momentum(window_length=22, mask=private_universe)
sector = get_sector(asset_finder=asset_finder, mask=private_universe)
ONEHOTCLASS, sector_indict_keys = get_sector_by_onehot(
asset_finder=asset_finder, mask=private_universe)
pipe_columns = {
'ep': ep.zscore(groupby=sector).downsample('month_start'),
'bp': bp.zscore(groupby=sector).downsample('month_start'),
'bvps': bvps.zscore(groupby=sector).downsample('month_start'),
'market_cap': market.zscore(groupby=sector).downsample('month_start'),
'totals': totals.zscore(groupby=sector).downsample('month_start'),
'totalAssets':
totalAssets.zscore(groupby=sector).downsample('month_start'),
'fixedAssets':
fixedAssets.zscore(groupby=sector).downsample('month_start'),
'esp': esp.zscore(groupby=sector).downsample('month_start'),
'rev': rev.zscore(groupby=sector).downsample('month_start'),
'profit': profit.zscore(groupby=sector).downsample('month_start'),
'gpr': gpr.zscore(groupby=sector).downsample('month_start'),
'npr': npr.zscore(groupby=sector).downsample('month_start'),
'vol10': vol10.zscore(groupby=sector).downsample('week_start'),
'rev10': rev10.zscore(groupby=sector).downsample('week_start'),
'vol20': vol20.zscore(groupby=sector).downsample('week_start'),
'rev20': rev20.zscore(groupby=sector).downsample('week_start'),
'vol30': vol30.zscore(groupby=sector).downsample('week_start'),
'rev30': rev30.zscore(groupby=sector).downsample('week_start'),
'ILLIQ5': illiq5.zscore(groupby=sector).downsample('week_start'),
'ILLIQ22': illiq22.zscore(groupby=sector).downsample('week_start'),
'mom5': mom5.zscore(groupby=sector).downsample('week_start'),
'mom22': mom22.zscore(groupby=sector).downsample('week_start'),
'rsi5': rsi5.zscore(groupby=sector).downsample('week_start'),
'rsi22': rsi22.zscore(groupby=sector).downsample('week_start'),
#'rsi22': rsi22.zscore(groupby=sector, mask=rsi22.percentile_between(1, 99)),
#######################################################################################################################
# 'ILLIQ5-2' : illiq5.zscore(groupby=quantiles([illiq5],bins = 10,mask = private_universe)).downsample('week_start'),
# 'ILLIQ22-2': illiq22.zscore(groupby=quantiles([illiq22],bins = 10,mask = private_universe)).downsample('week_start'),
# 'ILLIQ5-2': illiq5.zscore(groupby=market.quantiles(bins=10, mask=private_universe)).downsample('week_start'),
# 'ILLIQ22-2': illiq22.zscore(groupby=market.quantiles(bins=10, mask=private_universe)).downsample('week_start'),
#############################################################################################################################
'returns': returns.downsample('week_start'),
}
pipe = Pipeline(
columns=pipe_columns,
screen=private_universe,
)
i = 0
for c in ONEHOTCLASS:
pipe.add(c, sector_indict_keys[i])
i += 1
return pipe
