def pipeline_columns_and_mask(self):
factors, universe = self.__make_factors()
from collections import OrderedDict
factors_pipe = OrderedDict()
# Create returns over last n days.
factors_pipe['Returns'] = Returns(inputs=[USEquityPricing.close],
mask=universe,
window_length=5)
# Instantiate ranked factors
for name, f in factors.items():
f.window_safe = True
factors_pipe[name] = f.rank(
mask=universe) #rank 使用相对顺序，而不是绝对值，避免自相似性
predict = BasicFactorRegress(inputs=factors_pipe.values(),
window_length=42,
mask=universe) #进行预测，5天后价格
risk_beta = 0.66 * RollingLinearRegressionOfReturns(
target=symbol(risk_benchmark), # sid(8554),
returns_length=6,
regression_length=21,
# mask=long_short_screen
mask=(universe),
).beta + 0.33 * 1.0
sector = get_sector()
columns = {
'market_beta': risk_beta,
'sector': sector,
'predict': predict,
}
return columns, universe
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 universe_filter(smoothing_func=None, asset_finder=None):
"""
Create a Pipeline producing Filters implementing common acceptance criteria.
Returns
-------
zipline.Filter
Filter to control tradeablility
"""
factors = {
'MarketCap': Fundamental(asset_finder=asset_finder).outstanding,
'ADV_adj': ADV_adj(),
'Sector': get_sector(asset_finder=asset_finder),
}
#func = lambda f: f.downsample('month_start')
if smoothing_func != None:
factors = {id: smoothing_func(factors[id]) for id in factors.keys()}
#print factors
factors['MarketCap'] = factors['MarketCap'] > MARKET_CAP_DOWNLIMIT
factors['ADV_adj'] = factors['ADV_adj'] > ADV_ADJ_DOWNLIMIT
factors['Sector'] = factors['Sector'].notnull()
filters = None
for value in factors.values():
if filters == None:
filters = value
else:
filters = (filters & value)
#print filters
#mySymbolsListfiter = default_china_equity_universe_mask()
# Equities with an average daily volume greater than 5000000.
#high_volume = (AverageDollarVolume(window_length=252) > 5000000)
#liquid = ADV_adj().downsample('month_start') > 2500000
#market_cap_filter = MarketCap().downsample('month_start') > market_cap_limit
#universe_filter = (mySymbolsListfiter & market_cap_filter & liquid & high_volume )
#universe_filter = ( maket_cap_filter & liquid )
return filters
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
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
def sector_filter(tradeable_count,
sector_exposure_limit,
smoothing_func=None,
asset_finder=None):
"""
Mask for Pipeline in create_tradeable. Limits each sector so as not to be over-exposed
Parameters
----------
tradeable_count : int
Target number of constituent securities in universe
sector_exposure_limit: float
Target threshold for any particular sector
Returns
-------
zipline.Filter
Filter to control sector exposure
"""
industry_class, _ = get_sector_class()
#print("g_inds",g_inds)
sector_factor = get_sector(industry_class, asset_finder=asset_finder)
# set thresholds
sector_size = len(industry_class)
if sector_exposure_limit < ((1. / sector_size)):
threshold = int(math.ceil((1. / sector_size) * tradeable_count))
elif sector_exposure_limit > 1.:
threshold = tradeable_count
else:
threshold = int(math.ceil(sector_exposure_limit * tradeable_count))
#print ("tradeable_count %s , get_sector_size %s , industry threashold %s:" % (tradeable_count,sector_size,threshold))
filters = None
for industry, ino in industry_class.items():
#print industry,ino,industry_class[industry]
mask = sector_factor.eq(industry_class[industry])
if smoothing_func != None:
value = smoothing_func(AverageDollarVolume(window_length=21)).top(
threshold, mask)
else:
value = AverageDollarVolume(window_length=21).top(threshold, mask)
#print value
#print filters
if filters == None:
filters = value
else:
filters = (filters | value)
'''
transport_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['交通运输'.decode("UTF-8")])) #
instrument_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['仪器仪表'.decode("UTF-8")]))
media_entertainment_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['传媒娱乐'.decode("UTF-8")])) #
water_supply_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['供水供气'.decode("UTF-8")])) #
highway_bridge_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['公路桥梁'.decode("UTF-8")])) #
other_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['其它行业'.decode("UTF-8")]))
animal_husbandry_fishery_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['农林牧渔'.decode("UTF-8")])) #
pesticide_fertilizer_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['农药化肥'.decode("UTF-8")]))
chemical_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['化工行业'.decode("UTF-8")])) #
chemical_fiber_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['化纤行业'.decode("UTF-8")]))
medical_device_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['医疗器械'.decode("UTF-8")]))
printing_packaging_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['印刷包装'.decode("UTF-8")]))
power_plant_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['发电设备'.decode("UTF-8")]))
business_department_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['商业百货'.decode("UTF-8")])) #
plastics_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['塑料制品'.decode("UTF-8")]))
furniture_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['家具行业'.decode("UTF-8")])) #
appliance_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['家电行业'.decode("UTF-8")])) #
building_materials_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['建筑建材'.decode("UTF-8")])) #
development_zone_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['开发区'.decode("UTF-8")]))
real_estate_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['房地产'.decode("UTF-8")])) #
motorcycle_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['摩托车'.decode("UTF-8")]))
nonferrous_metals_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['有色金属'.decode("UTF-8")])) #
clothing_footwear_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['服装鞋类'.decode("UTF-8")])) #
machinery_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['机械行业'.decode("UTF-8")])) #
time_shares_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['次新股'.decode("UTF-8")]))
cement_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['水泥行业'.decode("UTF-8")]))
automobile_manufacturing_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['汽车制造'.decode("UTF-8")])) #
coal_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['煤炭行业'.decode("UTF-8")])) #
material_trade_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['物资外贸'.decode("UTF-8")]))
environmental_protection_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['环保行业'.decode("UTF-8")])) #
glass_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['玻璃行业'.decode("UTF-8")]))
biopharmaceuticals_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['生物制药'.decode("UTF-8")])) #
power_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['电力行业'.decode("UTF-8")]))
electronic_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['电器行业'.decode("UTF-8")]))
electronic_information_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['电子信息'.decode("UTF-8")])) #
electronic_device_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['电子器件'.decode("UTF-8")]))
oil_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['石油行业'.decode("UTF-8")]))
textile_machinery_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['纺织机械'.decode("UTF-8")]))
textile_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['纺织行业'.decode("UTF-8")]))
integrated_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['综合行业'.decode("UTF-8")]))
ship_manufacturing_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['船舶制造'.decode("UTF-8")]))
paper_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['造纸行业'.decode("UTF-8")]))
hotel_tour_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['酒店旅游'.decode("UTF-8")])) #
wine_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['酿酒行业'.decode("UTF-8")]))
financial_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['金融行业'.decode("UTF-8")])) #
steel_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['钢铁行业'.decode("UTF-8")])) #
ceramics_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['陶瓷行业'.decode("UTF-8")]))
aircraft_manufacturing_trim = AverageDollarVolume(window_length=21).top(threshold, mask=sector_factor.eq(industry_class['飞机制造'.decode("UTF-8")]))
food_industry_trim = AverageDollarVolume(window_length=21).downsample('month_start').top(threshold, mask=sector_factor.eq(industry_class['食品行业'.decode("UTF-8")])) #
return transport_trim|media_entertainment_trim|\
chemical_trim|medical_device_trim|\
power_plant_trim|business_department_trim|appliance_trim|\
building_materials_trim|real_estate_trim|nonferrous_metals_trim|\
clothing_footwear_trim|machinery_trim|automobile_manufacturing_trim|\
coal_trim|environmental_protection_trim|biopharmaceuticals_trim|\
power_trim|electronic_information_trim|electronic_device_trim|\
oil_trim|textile_trim|hotel_tour_trim|\
wine_trim|financial_trim|steel_trim|\
aircraft_manufacturing_trim|food_industry_trim
'''
return filters
