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
class SimpleBookToPrice(CustomFactor):
# pb = price to book, but we return the reciprocal
fund_pd = Fundamental().pb
fund_pd.window_safe = True
inputs = [fund_pd]
window_length = 1
def compute(self, today, assets, out, pb):
out[:] = 1 / pb
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 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 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