main
jack
2 months ago
parent
06c5886998
commit
6572c1061a
@ -0,0 +1,137 @@ |
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rank(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),returns,0),60)) |
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rank(group_neutralize(ts_mean(if_else(returns < ts_quantile(returns,45,gaussian),returns,0),45),sector)) |
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zscore(ts_mean(if_else(returns < ts_quantile(returns,90,cauchy),returns,0),90)) |
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normalize(group_zscore(ts_mean(if_else(returns < ts_quantile(returns,30,uniform),returns,0),30),industry)) |
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rank(ts_mean(if_else(subtract(returns,ts_quantile(returns,60)) < 0,returns,0),60)) |
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group_rank(ts_mean(if_else(returns < ts_quantile(returns,75,gaussian),returns,0),75),subindustry) |
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zscore(group_neutralize(ts_mean(if_else(returns < ts_quantile(returns,50,cauchy),returns,0),50),country)) |
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rank(abs(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),returns,0),60))) |
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normalize(ts_mean(if_else(returns < ts_quantile(returns,80,gaussian),signed_power(returns,2),0),80)) |
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rank(group_zscore(abs(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),returns,0),60)),sector)) |
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zscore(ts_mean(if_else(returns < ts_quantile(returns,40,uniform),power(abs(returns),1.5),0),40)) |
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rank(group_rank(ts_mean(if_else(returns < ts_quantile(returns,60,gaussian),signed_power(returns,3),0),60),industry)) |
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normalize(ts_mean(if_else(returns < ts_quantile(returns,70,cauchy),log(abs(returns)+1),0),70)) |
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rank(group_neutralize(abs(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),returns,0),60)),subindustry)) |
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zscore(ts_mean(if_else(returns < ts_quantile(returns,55,gaussian),sqrt(abs(returns)),0),55)) |
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rank(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),inverse(abs(returns)+1),0),60)) |
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group_zscore(rank(ts_mean(if_else(returns < ts_quantile(returns,65,uniform),returns,0),65)),sector) |
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normalize(group_rank(abs(ts_mean(if_else(returns < ts_quantile(returns,70,gaussian),returns,0),70)),country)) |
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rank(zscore(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),signed_power(returns,1.2),0),60))) |
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group_neutralize(rank(ts_mean(if_else(returns < ts_quantile(returns,50,uniform),returns,0),50)),industry) |
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rank(ts_mean(if_else(returns < ts_quantile(returns,85,gaussian),multiply(returns,100),0),85)) |
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zscore(group_rank(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),divide(returns,100),0),60),subindustry)) |
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normalize(abs(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),add(returns,1),0),60))) |
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rank(ts_mean(if_else(returns < ts_quantile(returns,95,gaussian),subtract(0,returns),0),95)) |
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group_zscore(zscore(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),returns,0),60)),sector) |
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rank(ts_mean(if_else(returns < ts_quantile(returns,35,uniform),sign(returns)*power(abs(returns),2),0),35)) |
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normalize(group_neutralize(ts_mean(if_else(returns < ts_quantile(returns,60,gaussian),sqrt(abs(returns))*sign(returns),0),60),industry)) |
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zscore(abs(ts_mean(if_else(returns < ts_quantile(returns,45,cauchy),signed_power(returns,0.5),0),45))) |
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rank(group_rank(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),log(abs(returns)+2),0),60),subindustry)) |
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group_neutralize(zscore(ts_mean(if_else(returns < ts_quantile(returns,75,gaussian),inverse(abs(returns)+2),0),75)),country) |
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rank(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),max(returns,-0.5),0),60)) |
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zscore(normalize(ts_mean(if_else(returns < ts_quantile(returns,50,uniform),min(returns,-0.1),0),50))) |
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rank(group_zscore(ts_mean(if_else(returns < ts_quantile(returns,60,gaussian),ts_zscore(returns,20),0),60)),sector) |
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normalize(rank(abs(ts_mean(if_else(returns < ts_quantile(returns,80,cauchy),ts_std_dev(returns,10),0),80)))) |
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rank(group_neutralize(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),ts_delta(returns,5),0),60),industry)) |
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zscore(ts_mean(if_else(returns < ts_quantile(returns,65,gaussian),ts_av_diff(returns,15),0),65)) |
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rank(group_rank(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),ts_rank(returns,10),0),60),subindustry)) |
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normalize(zscore(abs(ts_mean(if_else(returns < ts_quantile(returns,70,uniform),returns,0),70)))) |
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group_zscore(rank(ts_mean(if_else(returns < ts_quantile(returns,60,gaussian),signed_power(returns,2.5),0),60)),country) |
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rank(ts_mean(if_else(returns < ts_quantile(returns,50,cauchy),power(abs(returns),0.8),0),50)) |
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zscore(group_neutralize(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),sqrt(abs(returns))*signed_power(returns,1),0),60),sector)) |
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rank(normalize(ts_mean(if_else(returns < ts_quantile(returns,90,gaussian),log(abs(returns)+3),0),90))) |
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group_rank(zscore(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),inverse(abs(returns)+3),0),60)),industry) |
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normalize(abs(group_zscore(ts_mean(if_else(returns < ts_quantile(returns,65,uniform),returns,0),65),subindustry))) |
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rank(ts_mean(if_else(returns < ts_quantile(returns,60,gaussian),multiply(signed_power(returns,2),0.5),0),60)) |
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zscore(rank(ts_mean(if_else(returns < ts_quantile(returns,70,cauchy),divide(returns,abs(returns)+1),0),70))) |
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group_neutralize(normalize(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),add(returns,2),0),60)),country) |
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rank(abs(zscore(ts_mean(if_else(returns < ts_quantile(returns,80,gaussian),subtract(0,abs(returns)),0),80)))) |
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group_zscore(rank(abs(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),returns,0),60))),sector) |
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ts_rank(ts_sum(multiply(returns, returns < ts_quantile(returns, 60, "gaussian")), 60), 60) |
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ts_rank(ts_mean(if_else(returns < ts_quantile(returns, 60, "uniform"), returns, 0), 60), 60) |
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rank(subtract(ts_mean(returns, 60), ts_mean(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60))) |
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rank(ts_sum(if_else(returns < ts_delay(ts_quantile(returns, 60, "gaussian"), 1), returns, 0), 60)) |
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zscore(ts_sum(multiply(returns, returns < ts_mean(returns, 60)), 60)) |
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ts_rank(ts_sum(if_else(returns < ts_quantile(returns, 60, "cauchy"), returns, 0), 60), 120) |
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rank(ts_mean(if_else(returns < ts_delay(ts_quantile(returns, 60, "uniform"), 5), returns, 0), 60)) |
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ts_rank(ts_sum(multiply(returns, returns < ts_quantile(returns, 120, "gaussian")), 120), 60) |
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rank(subtract(ts_sum(returns, 60), ts_sum(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60))) |
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zscore(ts_mean(if_else(returns < ts_delay(ts_mean(returns, 60), 1), returns, 0), 60)) |
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ts_rank(ts_sum(multiply(returns, returns < ts_quantile(returns, 60, "gaussian")), 30), 60) |
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rank(ts_mean(if_else(returns < ts_delay(ts_quantile(returns, 30, "uniform"), 2), returns, 0), 60)) |
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ts_rank(ts_sum(if_else(returns < ts_quantile(returns, 90, "cauchy"), returns, 0), 90), 60) |
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zscore(ts_sum(multiply(returns, returns < ts_quantile(returns, 60, "gaussian")), 60)) |
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rank(subtract(ts_mean(returns, 90), ts_mean(if_else(returns < ts_quantile(returns, 90, "gaussian"), returns, 0), 90))) |
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ts_rank(ts_mean(multiply(returns, returns < ts_delay(ts_quantile(returns, 60, "uniform"), 3)), 60), 60) |
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rank(ts_sum(if_else(returns < ts_quantile(returns, 60, "cauchy"), returns, 0), 30)) |
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zscore(ts_mean(if_else(returns < ts_delay(ts_mean(returns, 30), 5), returns, 0), 60)) |
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ts_rank(ts_sum(multiply(returns, returns < ts_quantile(returns, 120, "gaussian")), 60), 120) |
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rank(subtract(ts_sum(returns, 30), ts_sum(if_else(returns < ts_quantile(returns, 30, "gaussian"), returns, 0), 30))) |
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@ -0,0 +1 @@ |
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-rank(ts_std_dev(if_else(ts_rank(returns,60)<0.05,returns,NaN),60)) |
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@ -0,0 +1,19 @@ |
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if_else(and(rank(ts_mean(daily_volume_percent_shares_out,60))<0.1,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
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if_else(and(rank(ts_mean(divide(volume,public_float_shares),50))<0.05,ts_arg_max(ts_sum(group_mean(returns,1,market),5)<-0.03,200)<=15),ts_sum(returns,15),0) |
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if_else(and(rank(ts_decay_linear(divide(volume,cap),70))<0.1,ts_arg_max(ts_sum(vec_avg(returns),10)<-0.07,300)<=25),ts_sum(returns,25),0) |
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if_else(and(rank(ts_mean(daily_volume_to_shares_outstanding,60))<0.15,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
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if_else(and(rank(ts_mean(divide(volume,fnd17_float),40))<0.1,ts_arg_max(ts_sum(group_mean(returns,cap,market),5)<-0.04,150)<=30),ts_sum(returns,30),0) |
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if_else(and(rank(ts_product(daily_volume_percent_shares_out,60))<0.1,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
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if_else(and(rank(ts_scale(daily_volume_percent_shares_out,60))<0.1,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
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if_else(and(rank(ts_zscore(daily_volume_percent_shares_out,60))<0.1,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
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if_else(and(rank(ts_av_diff(daily_volume_percent_shares_out,60))<0.1,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
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if_else(and(rank(ts_delta(daily_volume_percent_shares_out,60))<0.1,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
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@ -0,0 +1 @@ |
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trade_when(trade_when(0,rank(multiply(add(ts_min(returns,60),kth_element(returns,60,2)),inverse(2)),rate=2)>0.9,-1),rank(multiply(add(ts_min(returns,60),kth_element(returns,60,2)),inverse(2)),rate=2)<0.1,1) |
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@ -0,0 +1,99 @@ |
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reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(returns < -0.01, returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(returns < -0.02, returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(returns < -0.05, returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(returns < subtract(ts_mean(returns, 60), ts_std_dev(returns, 60)), returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(returns < subtract(ts_mean(returns, 60), multiply(ts_std_dev(returns, 60), 1.5)), returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(returns < subtract(ts_mean(returns, 60), multiply(ts_std_dev(returns, 60), 2)), returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 30))) |
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reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 90))) |
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reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 120))) |
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reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 250))) |
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reverse(rank(ts_decay_linear(if_else(returns < 0, returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(abs(returns) > add(ts_mean(abs(returns), 60), ts_std_dev(abs(returns), 60)), returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(returns < 0, signed_power(returns, 2), NaN), 60))) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), quarterly_return_on_equity_percent) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), ttm_return_on_equity_percent) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), anl45_risk_free_rate) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), fnd86_risk_score) |
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scale(winsorize(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), 4)) |
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winsorize(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), 3) |
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scale(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), 1, 1.5, 0.5) |
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reverse(rank(ts_std_dev(if_else(returns < subtract(ts_delay(ts_mean(returns, 60), 1), ts_delay(ts_std_dev(returns, 60), 1)), returns, NaN), 60))) |
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add(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), quarterly_return_on_equity_percent) |
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subtract(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), rank(quarterly_return_on_assets_percent)) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), rank(ttm_return_on_equity_percent)) |
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divide(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), add(ts_mean(abs(returns), 60), 0.001)) |
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signed_power(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), 2) |
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reverse(rank(ts_std_dev(if_else(returns < ts_mean(returns, 20), returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(returns < ts_mean(returns, 120), returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(abs(returns) > 0.05, returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(returns < -0.03, returns, NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(returns < ts_min(returns, 60), returns, NaN), 60))) |
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reverse(rank(ts_delay(ts_std_dev(if_else(returns < 0, returns, NaN), 60), 5))) |
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reverse(rank(ts_decay_linear(ts_std_dev(if_else(returns < 0, returns, NaN), 60), 20))) |
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reverse(rank(ts_std_dev(if_else(returns < 0, ts_delay(returns, 1), NaN), 60))) |
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reverse(rank(ts_std_dev(if_else(ts_delay(returns, 1) < 0, ts_delay(returns, 1), NaN), 60))) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), ts_step(1)) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), quarterly_return_on_equity_percent_3) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), return_on_equity_ratio_3) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), ttm_return_on_equity_percent_2) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), quarterly_return_on_investment_percent) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), quarterly_return_on_assets_percent_2) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), ttm_return_on_average_equity) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), log(add(1, abs(quarterly_return_on_assets_percent)))) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), sqrt(abs(quarterly_return_on_equity_percent))) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), sign(quarterly_return_on_investment)) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), inverse(add(1, abs(ttm_return_on_equity_percent)))) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), max(0.1, abs(quarterly_return_on_assets_percent))) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), min(10, abs(ttm_return_on_equity_percent))) |
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multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), add(1, anl45_risk_free_rate)) |
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@ -0,0 +1,99 @@ |
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trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
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trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(market_capitalization_current, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
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trade_when(and(ts_mean(returns, 5) < -0.05, rank(ts_mean(daily_volume_to_shares_outstanding, 60)) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
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trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(public_float_shares, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
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trade_when(and(ts_mean(returns, 7) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
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trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -15), 15)), NaN) |
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trade_when(and(ts_zscore(returns, 5) < -2, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
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trade_when(and(ts_std_dev(returns, 5) > ts_mean(ts_std_dev(returns, 60), 60), rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
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trade_when(and(rank(ts_mean(returns, 5)) < 0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
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trade_when(and(ts_mean(returns, 5) < -0.05, rank(ts_decay_linear(divide(volume, cap), 60)) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
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trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(pv37_volume_global, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
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trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(fnd17_mktcap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
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|
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trade_when(and(ts_mean(returns, 5) < -0.05, rank(ts_mean(daily_volume_percent_shares_out, 60)) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(winsorize(divide(ts_mean(volume, 60), ts_mean(cap, 60)), std=3)) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(scale(divide(ts_mean(volume, 60), ts_mean(cap, 60)))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 10) < -0.07, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -10), 10)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -30), 30)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_corr(ts_delay(returns, -20), returns, 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_decay_linear(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, quantile(divide(ts_mean(volume, 60), ts_mean(cap, 60)), driver=uniform) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), group_neutralize(rank(ts_sum(ts_delay(returns, -20), 20)), market), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, zscore(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < -1.5), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(and(ts_mean(returns, 5) < -0.05, ts_std_dev(returns, 5) > ts_mean(ts_std_dev(returns, 60), 60)), rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(volume, 5) > ts_mean(volume, 60) * 2, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(fnd17_float, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(market_capitalization_value_5, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 30), ts_mean(cap, 30))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 90), ts_mean(cap, 90))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(signed_power(divide(ts_mean(volume, 60), ts_mean(cap, 60)), 0.5)) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(log(divide(ts_mean(volume, 60), ts_mean(cap, 60)))) < -1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(inverse(divide(ts_mean(volume, 60), ts_mean(cap, 60)))) > 0.9), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(divide(ts_sum(ts_delay(returns, -20), 20), ts_std_dev(returns, 20))), NaN) |
||||
|
||||
trade_when(and(ts_delta(ts_mean(returns, 5), 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_arg_min(ts_mean(returns, 5), 10) == 0, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_product(add(1, ts_delay(returns, -20)), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(scale_down(divide(ts_mean(volume, 60), ts_mean(cap, 60)))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, bucket(rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))), range="0,0.1,1") == 0), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, group_rank(divide(ts_mean(volume, 60), ts_mean(cap, 60)), market) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), hump(rank(ts_sum(ts_delay(returns, -20), 20)), 0.02), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), jump_decay(rank(ts_sum(ts_delay(returns, -20), 20)), 20), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), ts_target_tvr_decay(rank(ts_sum(ts_delay(returns, -20), 20))), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_corr(ts_delay(returns, -20), volume, 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_covariance(ts_delay(returns, -20), returns, 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_regression(ts_delay(returns, -20), returns, 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_av_diff(ts_sum(ts_delay(returns, -20), 20), 60)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), rank(ts_backfill(ts_sum(ts_delay(returns, -20), 20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), ts_quantile(ts_sum(ts_delay(returns, -20), 20), 20), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(pv37_volume_global, 60), ts_mean(market_capitalization_current, 60))) < 0.1), rank(ts_sum(ts_delay(returns, -20), 20)), NaN) |
||||
|
||||
trade_when(and(ts_mean(returns, 5) < -0.05, rank(divide(ts_mean(volume, 60), ts_mean(cap, 60))) < 0.1), scale(rank(ts_sum(ts_delay(returns, -20), 20))), NaN) |
||||
@ -0,0 +1,99 @@ |
||||
rank(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), rate=0) |
||||
|
||||
rank(ts_mean(if_else(ts_rank(returns, 30) < 0.05, returns, NaN), 30), rate=0) |
||||
|
||||
rank(ts_mean(if_else(ts_rank(returns, 120) < 0.05, returns, NaN), 120), rate=0) |
||||
|
||||
rank(ts_mean(if_else(ts_rank(returns, 60) < 0.01, returns, NaN), 60), rate=0) |
||||
|
||||
rank(ts_mean(if_else(ts_rank(returns, 60) < 0.10, returns, NaN), 60), rate=0) |
||||
|
||||
rank(ts_min(returns, 60), rate=0) |
||||
|
||||
rank(divide(ts_sum(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), ts_sum(if_else(ts_rank(returns, 60) < 0.05, 1, NaN), 60)), rate=0) |
||||
|
||||
rank(ts_std_dev(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), rate=0) |
||||
|
||||
rank(multiply(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), ts_std_dev(returns, 60)), rate=0) |
||||
|
||||
rank(divide(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), ts_std_dev(returns, 60)), rate=0) |
||||
|
||||
rank(ts_mean(if_else(ts_rank(returns, 60) < 0.05, ts_decay_linear(returns, 60), NaN), 60), rate=0) |
||||
|
||||
rank(ts_delta(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), 20), rate=0) |
||||
|
||||
rank(ts_mean(if_else(ts_rank(abs(returns), 60) < 0.05, abs(returns), NaN), 60), rate=0) |
||||
|
||||
rank(ts_mean(if_else(ts_rank(returns, 60) < 0.05, subtract(returns, ts_mean(returns, 60)), NaN), 60), rate=0) |
||||
|
||||
rank(divide(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), ts_regression(returns, ts_mean(returns, 60), 60, lag=0, rettype=0)), rate=0) |
||||
|
||||
rank(ts_zscore(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), 60), rate=0) |
||||
|
||||
rank(ts_mean(if_else(ts_rank(winsorize(returns, std=3), 60) < 0.05, winsorize(returns, std=3), NaN), 60), rate=0) |
||||
|
||||
rank(ts_delay(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), 5), rate=0) |
||||
|
||||
rank(ts_delta(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), 10), rate=0) |
||||
|
||||
rank(ts_arg_min(returns, 60), rate=0) |
||||
|
||||
rank(ts_product(if_else(ts_rank(returns, 60) < 0.05, add(1, returns), NaN), 60), rate=0) |
||||
|
||||
rank(ts_sum(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), rate=0) |
||||
|
||||
rank(ts_mean(if_else(ts_rank(returns, 60) < 0.05, ts_decay_linear(returns, 60, dense=true), NaN), 60), rate=0) |
||||
|
||||
rank(ts_backfill(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), lookback=60), rate=0) |
||||
|
||||
rank(quantile(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), driver="cauchy"), rate=0) |
||||
|
||||
rank(log(abs(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60))), rate=0) |
||||
|
||||
rank(inverse(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60)), rate=0) |
||||
|
||||
rank(signed_power(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), 2), rate=0) |
||||
|
||||
rank(scale(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60)), rate=0) |
||||
|
||||
rank(zscore(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60)), rate=0) |
||||
|
||||
rank(add(ts_mean(if_else(ts_rank(returns, 30) < 0.05, returns, NaN), 30), ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), ts_mean(if_else(ts_rank(returns, 120) < 0.05, returns, NaN), 120)), rate=0) |
||||
|
||||
rank(multiply(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), ts_mean(returns, 20)), rate=0) |
||||
|
||||
rank(multiply(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), reverse(ts_mean(returns, 20))), rate=0) |
||||
|
||||
rank(if_else(ts_mean(returns, 60) < 0, ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), NaN), rate=0) |
||||
|
||||
rank(if_else(ts_std_dev(returns, 60) > ts_mean(ts_std_dev(returns, 60), 252), ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), NaN), rate=0) |
||||
|
||||
rank(multiply(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), ts_corr(returns, ts_mean(returns, 60), 60)), rate=0) |
||||
|
||||
rank(ts_mean(if_else(or(ts_rank(returns, 60) < 0.05, ts_rank(returns, 60) > 0.95), returns, NaN), 60), rate=0) |
||||
|
||||
rank(subtract(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), ts_mean(if_else(ts_rank(returns, 60) > 0.95, returns, NaN), 60)), rate=0) |
||||
|
||||
rank(ts_mean(if_else(ts_rank(hump(returns, hump=0.02), 60) < 0.05, hump(returns, hump=0.02), NaN), 60), rate=0) |
||||
|
||||
rank(ts_mean(if_else(and(ts_delta(returns, 1) > multiply(2, ts_std_dev(returns, 60)), ts_rank(returns, 60) < 0.05), returns, NaN), 60), rate=0) |
||||
|
||||
rank(subtract(ts_arg_min(returns, 60), ts_arg_max(returns, 60)), rate=0) |
||||
|
||||
rank(ts_av_diff(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), 60), rate=0) |
||||
|
||||
rank(ts_scale(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), 60), rate=0) |
||||
|
||||
rank(ts_zscore(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), 60), rate=0) |
||||
|
||||
rank(power(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), 2), rate=0) |
||||
|
||||
rank(sqrt(abs(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60))), rate=0) |
||||
|
||||
rank(multiply(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), sign(ts_mean(returns, 60))), rate=0) |
||||
|
||||
rank(divide(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), ts_mean(abs(returns), 60)), rate=0) |
||||
|
||||
rank(rank(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), rate=0), rate=0) |
||||
|
||||
rank(bucket(rank(ts_mean(if_else(ts_rank(returns, 60) < 0.05, returns, NaN), 60), rate=0), range="0,1,0.1"), rate=0) |
||||
@ -0,0 +1,109 @@ |
||||
ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60) |
||||
|
||||
rank(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
reverse(rank(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60))) |
||||
|
||||
ts_zscore(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
|
||||
normalize(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), true, 0.0) |
||||
|
||||
ts_rank(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
|
||||
group_zscore(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), bucket(rank(returns), buckets="2,5,6,7,10")) |
||||
|
||||
winsorize(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 4) |
||||
|
||||
scale(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 1, 1, 1) |
||||
|
||||
ts_scale(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60, 0) |
||||
|
||||
ts_av_diff(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
|
||||
ts_delta(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 5) |
||||
|
||||
ts_mean(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 30) |
||||
|
||||
ts_max(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
|
||||
ts_min(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
|
||||
ts_sum(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
|
||||
ts_product(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
|
||||
ts_decay_linear(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60, false) |
||||
|
||||
ts_backfill(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60, 1, "NAN") |
||||
|
||||
ts_corr(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), returns, 60) |
||||
|
||||
ts_covariance(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), returns, 60) |
||||
|
||||
ts_regression(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), returns, 60, 0, 0) |
||||
|
||||
ts_arg_max(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
|
||||
ts_arg_min(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
|
||||
ts_count_nans(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
|
||||
ts_delay(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 5) |
||||
|
||||
ts_quantile(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60, "gaussian") |
||||
|
||||
ts_rank(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60, 0) |
||||
|
||||
ts_scale(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60, 0) |
||||
|
||||
ts_step(1) |
||||
|
||||
ts_target_tvr_decay(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 0, 1, 0.1) |
||||
|
||||
ts_target_tvr_delta_limit(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), returns, 0, 1, 0.1) |
||||
|
||||
group_neutralize(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), bucket(rank(returns), buckets="2,5,6,7,10")) |
||||
|
||||
group_rank(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), bucket(rank(returns), buckets="2,5,6,7,10")) |
||||
|
||||
group_scale(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), bucket(rank(returns), buckets="2,5,6,7,10")) |
||||
|
||||
group_mean(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), returns, bucket(rank(returns), buckets="2,5,6,7,10")) |
||||
|
||||
group_backfill(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), bucket(rank(returns), buckets="2,5,6,7,10"), 60, 4.0) |
||||
|
||||
vec_avg(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
vec_sum(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
vec_max(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
vec_min(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
reduce_avg(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 0) |
||||
|
||||
reduce_max(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
reduce_min(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
reduce_stddev(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 0) |
||||
|
||||
reduce_sum(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
reduce_range(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
reduce_skewness(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
reduce_kurtosis(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
reduce_ir(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
reduce_norm(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
|
||||
reduce_powersum(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 2, false) |
||||
|
||||
reduce_percentage(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 0.5) |
||||
|
||||
reduce_choose(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 1, true) |
||||
|
||||
reduce_count(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 0) |
||||
@ -0,0 +1,44 @@ |
||||
ts_mean(volume, 60) |
||||
divide(ts_mean(volume, 60), cap) |
||||
rank(divide(ts_mean(volume, 60), cap)) |
||||
ts_arg_min(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_delta(ts_mean(volume, 60), 1) |
||||
ts_std_dev(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_zscore(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_rank(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_scale(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_decay_linear(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_corr(divide(ts_mean(volume, 60), cap), returns, 60) |
||||
ts_covariance(divide(ts_mean(volume, 60), cap), returns, 60) |
||||
ts_regression(returns, divide(ts_mean(volume, 60), cap), 60) |
||||
ts_backfill(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_product(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_quantile(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_av_diff(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_count_nans(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_sum(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_delay(divide(ts_mean(volume, 60), cap), 1) |
||||
ts_delta(divide(ts_mean(volume, 60), cap), 1) |
||||
ts_arg_max(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_min(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_max(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_mean(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_std_dev(ts_mean(volume, 60), 60) |
||||
ts_zscore(ts_mean(volume, 60), 60) |
||||
ts_rank(ts_mean(volume, 60), 60) |
||||
ts_scale(ts_mean(volume, 60), 60) |
||||
ts_decay_linear(ts_mean(volume, 60), 60) |
||||
ts_corr(ts_mean(volume, 60), returns, 60) |
||||
ts_covariance(ts_mean(volume, 60), returns, 60) |
||||
ts_regression(returns, ts_mean(volume, 60), 60) |
||||
ts_backfill(ts_mean(volume, 60), 60) |
||||
ts_product(ts_mean(volume, 60), 60) |
||||
ts_quantile(ts_mean(volume, 60), 60) |
||||
ts_av_diff(ts_mean(volume, 60), 60) |
||||
ts_count_nans(ts_mean(volume, 60), 60) |
||||
ts_sum(ts_mean(volume, 60), 60) |
||||
ts_delay(ts_mean(volume, 60), 1) |
||||
ts_delta(ts_mean(volume, 60), 1) |
||||
ts_arg_max(ts_mean(volume, 60), 60) |
||||
ts_min(ts_mean(volume, 60), 60) |
||||
ts_max(ts_mean(volume, 60), 60) |
||||
@ -0,0 +1,49 @@ |
||||
ts_mean(returns, 60) |
||||
ts_std_dev(returns, 60) |
||||
ts_quantile(returns, 60, driver="gaussian") |
||||
ts_rank(returns, 60) |
||||
ts_zscore(returns, 60) |
||||
ts_arg_min(returns, 60) |
||||
ts_arg_max(returns, 60) |
||||
ts_delta(returns, 60) |
||||
ts_backfill(returns, 60, k=1) |
||||
ts_decay_linear(returns, 60, dense=false) |
||||
ts_corr(returns, returns, 60) |
||||
ts_covariance(returns, returns, 60) |
||||
ts_scale(returns, 60, constant=0) |
||||
ts_av_diff(returns, 60) |
||||
ts_product(returns, 60) |
||||
ts_sum(returns, 60) |
||||
ts_count_nans(returns, 60) |
||||
ts_regression(returns, returns, 60, lag=0, rettype=0) |
||||
ts_step(1) |
||||
ts_target_tvr_decay(returns, lambda_min=0, lambda_max=1, target_tvr=0.1) |
||||
ts_target_tvr_delta_limit(returns, returns, lambda_min=0, lambda_max=1, target_tvr=0.1) |
||||
rank(ts_quantile(returns, 60, driver="gaussian")) |
||||
rank(ts_zscore(returns, 60)) |
||||
rank(ts_arg_min(returns, 60)) |
||||
rank(ts_arg_max(returns, 60)) |
||||
rank(ts_delta(returns, 60)) |
||||
rank(ts_backfill(returns, 60, k=1)) |
||||
rank(ts_decay_linear(returns, 60, dense=false)) |
||||
rank(ts_corr(returns, returns, 60)) |
||||
rank(ts_covariance(returns, returns, 60)) |
||||
rank(ts_scale(returns, 60, constant=0)) |
||||
rank(ts_av_diff(returns, 60)) |
||||
rank(ts_product(returns, 60)) |
||||
rank(ts_sum(returns, 60)) |
||||
rank(ts_count_nans(returns, 60)) |
||||
rank(ts_regression(returns, returns, 60, lag=0, rettype=0)) |
||||
rank(ts_step(1)) |
||||
rank(ts_target_tvr_decay(returns, lambda_min=0, lambda_max=1, target_tvr=0.1)) |
||||
rank(ts_target_tvr_delta_limit(returns, returns, lambda_min=0, lambda_max=1, target_tvr=0.1)) |
||||
quantile(ts_quantile(returns, 60, driver="gaussian"), driver="gaussian", sigma=1.0) |
||||
quantile(ts_zscore(returns, 60), driver="gaussian", sigma=1.0) |
||||
quantile(ts_arg_min(returns, 60), driver="gaussian", sigma=1.0) |
||||
quantile(ts_arg_max(returns, 60), driver="gaussian", sigma=1.0) |
||||
quantile(ts_delta(returns, 60), driver="gaussian", sigma=1.0) |
||||
quantile(ts_backfill(returns, 60, k=1), driver="gaussian", sigma=1.0) |
||||
quantile(ts_decay_linear(returns, 60, dense=false), driver="gaussian", sigma=1.0) |
||||
quantile(ts_corr(returns, returns, 60), driver="gaussian", sigma=1.0) |
||||
quantile(ts_covariance(returns, returns, 60), driver="gaussian", sigma=1.0) |
||||
quantile(ts_scale(returns, 60, constant=0), driver="gaussian", sigma=1.0) |
||||
@ -0,0 +1,346 @@ |
||||
尾部波动溢价因子 |
||||
假设 |
||||
在市场情绪极端波动时期，资产价格尾部风险（如暴跌或暴涨）往往被投资者过度反应或忽视，导致短期内出现错误定价。具有较高尾部波动历史的股票，在未来短期内可能因风险补偿不足而表现不佳，或因恐慌过度而提供反转机会。该因子基于尾部波动率与后续收益之间的负向关系构建，符合行为金融中的“过度反应”和“风险误判”逻辑。 |
||||
实施方案 |
||||
利用个股日频收益率数据，滚动计算过去N日（如60日）中跌幅超过一定分位数（如5%分位）的收益率标准差，作为尾部波动指标；再对全市场股票按该指标排序，做多尾部波动最低的组合、做空最高的组合，形成多空因子收益序列。 |
||||
*=========================================================================================* |
||||
输出格式： |
||||
输出必须是且仅是纯文本。 |
||||
每一行是一个完整、独立、语法正确的WebSim表达式。 |
||||
严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。 |
||||
===================== !!! 重点(输出方式) !!! ===================== |
||||
现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。 |
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 |
||||
**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不需要赋值, 不要解释, 不需要序号, 也不要输出多余的东西)： |
||||
表达式 |
||||
表达式 |
||||
表达式 |
||||
... |
||||
表达式 |
||||
================================================================= |
||||
重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。 |
||||
以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 50 个 alpha： |
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 |
||||
!! 数据集使用要求尽量分散, 不要重复使用 |
||||
!! 数据集使用要求尽量分散, 不要重复使用 |
||||
================================================================= |
||||
**操作符汇总 |
||||
**算术运算符 (Arithmetic): |
||||
abs(x) - 绝对值 |
||||
add(x, y, filter=false) - 加法 (x + y) |
||||
densify(x) - 分组字段稠密化 |
||||
divide(x, y) - 除法 (x / y) |
||||
inverse(x) - 倒数 (1/x) |
||||
log(x) - 自然对数 |
||||
max(x, y, ..) - 最大值 |
||||
min(x, y, ..) - 最小值 |
||||
multiply(x, y, filter=false) - 乘法 (x * y) |
||||
power(x, y) - 幂运算 (x^y) |
||||
reverse(x) - 取反 (-x) |
||||
sign(x) - 符号函数 |
||||
signed_power(x, y) - 保留符号的幂运算 |
||||
sqrt(x) - 平方根 |
||||
subtract(x, y, filter=false) - 减法 (x - y) |
||||
to_nan(x, value=0, reverse=false) - 值与NaN转换 |
||||
**逻辑运算符 (Logical): |
||||
and(input1, input2) - 逻辑与 |
||||
if_else(input1, input2, input3) - 条件判断 |
||||
input1 < input2 - 小于比较 |
||||
input1 <= input2 - 小于等于 |
||||
input1 == input2 - 等于比较 |
||||
input1 > input2 - 大于比较 |
||||
input1 >= input2 - 大于等于 |
||||
input1 != input2 - 不等于 |
||||
is_nan(input) - 是否为NaN |
||||
not(x) - 逻辑非 |
||||
or(input1, input2) - 逻辑或 |
||||
**时间序列运算符 (Time Series): |
||||
days_from_last_change(x) - 上次变化天数 |
||||
hump(x, hump=0.01) - 限制变化幅度 |
||||
jump_decay(x, d, sensitivity=0.5, force=0.1) - 跳跃衰减 |
||||
kth_element(x, d, k) - 第K个值 |
||||
last_diff_value(x, d) - 最后一个不同值 |
||||
ts_arg_max(x, d) - 最大值相对索引 |
||||
ts_arg_min(x, d) - 最小值相对索引 |
||||
ts_av_diff(x, d) - 与均值的差 |
||||
ts_backfill(x, lookback=d, k=1, ignore="NAN") - 回填 |
||||
ts_corr(x, y, d) - 时间序列相关性 |
||||
ts_count_nans(x, d) - NaN计数 |
||||
ts_covariance(y, x, d) - 协方差 |
||||
ts_decay_linear(x, d, dense=false) - 线性衰减 |
||||
ts_delay(x, d) - 延迟值 |
||||
ts_delta(x, d) - 差值 (x - 延迟值) |
||||
ts_max(x, d) - 时间序列最大值 |
||||
ts_mean(x, d) - 时间序列均值 |
||||
ts_min(x, d) - 时间序列最小值 |
||||
ts_product(x, d) - 时间序列乘积 |
||||
ts_quantile(x, d, driver="gaussian") - 分位数 |
||||
ts_rank(x, d, constant=0) - 时间序列排名 |
||||
ts_regression(y, x, d, lag=0, rettype=0) - 回归分析 |
||||
ts_scale(x, d, constant=0) - 时间序列缩放 |
||||
ts_std_dev(x, d) - 时间序列标准差 |
||||
ts_step(1) - 天数计数器 |
||||
ts_sum(x, d) - 时间序列求和 |
||||
ts_target_tvr_decay(x, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率衰减 |
||||
ts_target_tvr_delta_limit(x, y, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率差值限制 |
||||
ts_zscore(x, d) - 时间序列Z分数 |
||||
**横截面运算符 (Cross Sectional): |
||||
normalize(x, useStd=false, limit=0.0) - 标准化 |
||||
quantile(x, driver=gaussian, sigma=1.0) - 分位数转换 |
||||
rank(x, rate=2) - 排名 |
||||
scale(x, scale=1, longscale=1, shortscale=1) - 缩放 |
||||
scale_down(x, constant=0) - 按比例缩放 |
||||
vector_neut(x, y) - 向量中性化 |
||||
winsorize(x, std=4) - 缩尾处理 |
||||
zscore(x) - Z分数 |
||||
**向量运算符 (Vector): |
||||
vec_avg(x) - 向量均值 |
||||
vec_max(x) - 向量最大值 |
||||
vec_min(x) - 向量最小值 |
||||
vec_sum(x) - 向量求和 |
||||
**变换运算符 (Transformational): |
||||
bucket(rank(x), range="0,1,0.1" or buckets="2,5,6,7,10") - 分桶 |
||||
generate_stats(alpha) - 生成统计量 |
||||
trade_when(x, y, z) - 条件交易 |
||||
**分组运算符 (Group): |
||||
combo_a(alpha, nlength=250, mode='algo1') - 组合Alpha |
||||
group_backfill(x, group, d, std=4.0) - 分组回填 |
||||
group_cartesian_product(g1, g2) - 笛卡尔积分组 |
||||
group_max(x, group) - 分组最大值 |
||||
group_mean(x, weight, group) - 分组均值 |
||||
group_min(x, group) - 分组最小值 |
||||
group_neutralize(x, group) - 分组中性化 |
||||
group_rank(x, group) - 分组排名 |
||||
group_scale(x, group) - 分组缩放 |
||||
group_zscore(x, group) - 分组Z分数 |
||||
**特殊运算符 (Special): |
||||
in - 包含判断 |
||||
self_corr(input) - 自相关性 |
||||
universe_size - 宇宙大小 |
||||
**归约运算符 (Reduce): |
||||
reduce_avg(input, threshold=0) - 平均值归约 |
||||
reduce_choose(input, nth, ignoreNan=true) - 选择归约 |
||||
reduce_count(input, threshold) - 计数归约 |
||||
reduce_ir(input) - IR归约 |
||||
reduce_kurtosis(input) - 峰度归约 |
||||
reduce_max(input) - 最大值归约 |
||||
reduce_min(input) - 最小值归约 |
||||
reduce_norm(input) - 范数归约 |
||||
reduce_percentage(input, percentage=0.5) - 百分比归约 |
||||
reduce_powersum(input, constant=2, precise=false) - 幂和归约 |
||||
reduce_range(input) - 范围归约 |
||||
reduce_skewness(input) - 偏度归约 |
||||
reduce_stddev(input, threshold=0) - 标准差归约 |
||||
reduce_sum(input) - 求和归约 |
||||
|
||||
以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子 |
||||
|
||||
========================= 操作符开始 ======================================= |
||||
注意: Operator: 后面的是操作符(是可以使用的), |
||||
Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符 |
||||
特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x) |
||||
Description: Absolute value of x |
||||
Operator: add(x, y, filter = false) |
||||
Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding |
||||
Operator: densify(x) |
||||
Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient |
||||
Operator: divide(x, y) |
||||
Description: x / y |
||||
Operator: inverse(x) |
||||
Description: 1 / x |
||||
Operator: log(x) |
||||
Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights. |
||||
Operator: max(x, y, ..) |
||||
Description: Maximum value of all inputs. At least 2 inputs are required |
||||
Operator: min(x, y ..) |
||||
Description: Minimum value of all inputs. At least 2 inputs are required |
||||
Operator: multiply(x ,y, ... , filter=false) |
||||
Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1 |
||||
Operator: power(x, y) |
||||
Description: x ^ y |
||||
Operator: reverse(x) |
||||
Description: - x |
||||
Operator: sign(x) |
||||
Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN; |
||||
Operator: signed_power(x, y) |
||||
Description: x raised to the power of y such that final result preserves sign of x |
||||
Operator: sqrt(x) |
||||
Description: Square root of x |
||||
Operator: subtract(x, y, filter=false) |
||||
Description: x-y. If filter = true, filter all input NaN to 0 before subtracting |
||||
Operator: and(input1, input2) |
||||
Description: Logical AND operator, returns true if both operands are true and returns false otherwise |
||||
Operator: if_else(input1, input2, input 3) |
||||
Description: If input1 is true then return input2 else return input3. |
||||
Operator: input1 < input2 |
||||
Description: If input1 < input2 return true, else return false |
||||
Operator: input1 <= input2 |
||||
Description: Returns true if input1 <= input2, return false otherwise |
||||
Operator: input1 == input2 |
||||
Description: Returns true if both inputs are same and returns false otherwise |
||||
Operator: input1 > input2 |
||||
Description: Logic comparison operators to compares two inputs |
||||
Operator: input1 >= input2 |
||||
Description: Returns true if input1 >= input2, return false otherwise |
||||
Operator: input1!= input2 |
||||
Description: Returns true if both inputs are NOT the same and returns false otherwise |
||||
Operator: is_nan(input) |
||||
Description: If (input == NaN) return 1 else return 0 |
||||
Operator: not(x) |
||||
Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1). |
||||
Operator: or(input1, input2) |
||||
Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise |
||||
Operator: days_from_last_change(x) |
||||
Description: Amount of days since last change of x |
||||
Operator: hump(x, hump = 0.01) |
||||
Description: Limits amount and magnitude of changes in input (thus reducing turnover) |
||||
Operator: kth_element(x, d, k) |
||||
Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1 |
||||
Operator: last_diff_value(x, d) |
||||
Description: Returns last x value not equal to current x value from last d days |
||||
Operator: ts_arg_max(x, d) |
||||
Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1 |
||||
Operator: ts_arg_min(x, d) |
||||
Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1. |
||||
Operator: ts_av_diff(x, d) |
||||
Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation |
||||
Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN") |
||||
Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value) |
||||
Operator: ts_corr(x, y, d) |
||||
Description: Returns correlation of x and y for the past d days |
||||
Operator: ts_count_nans(x ,d) |
||||
Description: Returns the number of NaN values in x for the past d days |
||||
Operator: ts_covariance(y, x, d) |
||||
Description: Returns covariance of y and x for the past d days |
||||
Operator: ts_decay_linear(x, d, dense = false) |
||||
Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not. |
||||
Operator: ts_delay(x, d) |
||||
Description: Returns x value d days ago |
||||
Operator: ts_delta(x, d) |
||||
Description: Returns x - ts_delay(x, d) |
||||
Operator: ts_mean(x, d) |
||||
Description: Returns average value of x for the past d days. |
||||
Operator: ts_product(x, d) |
||||
Description: Returns product of x for the past d days |
||||
Operator: ts_quantile(x,d, driver="gaussian" ) |
||||
Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default. |
||||
Operator: ts_rank(x, d, constant = 0) |
||||
Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0. |
||||
Operator: ts_regression(y, x, d, lag = 0, rettype = 0) |
||||
Description: Returns various parameters related to regression function |
||||
Operator: ts_scale(x, d, constant = 0) |
||||
Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space |
||||
Operator: ts_std_dev(x, d) |
||||
Description: Returns standard deviation of x for the past d days |
||||
Operator: ts_step(1) |
||||
Description: Returns days' counter |
||||
Operator: ts_sum(x, d) |
||||
Description: Sum values of x for the past d days. |
||||
Operator: ts_zscore(x, d) |
||||
Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown. |
||||
Operator: normalize(x, useStd = false, limit = 0.0) |
||||
Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element |
||||
Operator: quantile(x, driver = gaussian, sigma = 1.0) |
||||
Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector |
||||
Operator: rank(x, rate=2) |
||||
Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0 |
||||
Operator: scale(x, scale=1, longscale=1, shortscale=1) |
||||
Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator |
||||
Operator: winsorize(x, std=4) |
||||
Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std. |
||||
Operator: zscore(x) |
||||
Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean |
||||
Operator: vec_avg(x) |
||||
Description: Taking mean of the vector field x |
||||
Operator: vec_sum(x) |
||||
Description: Sum of vector field x |
||||
Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10") |
||||
Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input |
||||
Operator: trade_when(x, y, z) |
||||
Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition |
||||
Operator: group_backfill(x, group, d, std = 4.0) |
||||
Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days |
||||
Operator: group_mean(x, weight, group) |
||||
Description: All elements in group equals to the mean |
||||
Operator: group_neutralize(x, group) |
||||
Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant |
||||
Operator: group_rank(x, group) |
||||
Description: Each elements in a group is assigned the corresponding rank in this group |
||||
Operator: group_scale(x, group) |
||||
Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin) |
||||
Operator: group_zscore(x, group) |
||||
Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group. |
||||
========================= 操作符结束 ======================================= |
||||
|
||||
========================= 数据字段开始 ======================================= |
||||
注意: data_set_name: 后面的是数据字段(可以使用), description: 此字段后面的是数据字段对应的描述或使用说明(不能使用) |
||||
|
||||
{'id': 62154, 'data_set_name': '可以使用:quarterly_return_on_assets_percent', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter, as a percentage.'} |
||||
{'id': 62155, 'data_set_name': '可以使用:quarterly_return_on_assets_percent_2', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter, as a percentage.'} |
||||
{'id': 62156, 'data_set_name': '可以使用:quarterly_return_on_average_assets', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter.'} |
||||
{'id': 62157, 'data_set_name': '可以使用:quarterly_return_on_average_equity', 'description': '不可使用,仅供参考:Annualized net income divided by average equity for the quarter.'} |
||||
{'id': 62158, 'data_set_name': '可以使用:quarterly_return_on_equity_percent', 'description': '不可使用,仅供参考:Annualized net income divided by average equity for the quarter, as a percentage.'} |
||||
{'id': 62159, 'data_set_name': '可以使用:quarterly_return_on_equity_percent_2', 'description': '不可使用,仅供参考:Annualized net income divided by average equity for the quarter, as a percentage.'} |
||||
{'id': 62160, 'data_set_name': '可以使用:quarterly_return_on_investment', 'description': '不可使用,仅供参考:Annualized net income divided by average investment for the quarter.'} |
||||
{'id': 62161, 'data_set_name': '可以使用:quarterly_return_on_investment_percent', 'description': '不可使用,仅供参考:Annualized net income divided by average investment for the quarter.'} |
||||
{'id': 62162, 'data_set_name': '可以使用:quarterly_return_on_investment_percent_2', 'description': '不可使用,仅供参考:Annualized net income divided by average investment for the quarter, as a percentage.'} |
||||
{'id': 62226, 'data_set_name': '可以使用:ttm_return_on_average_equity', 'description': '不可使用,仅供参考:Net income divided by average equity for the trailing twelve months.'} |
||||
{'id': 62227, 'data_set_name': '可以使用:ttm_return_on_equity_percent', 'description': '不可使用,仅供参考:Net income divided by average equity for trailing twelve months, as a percentage.'} |
||||
{'id': 62228, 'data_set_name': '可以使用:ttm_return_on_equity_percent_2', 'description': '不可使用,仅供参考:Net income divided by average equity for trailing twelve months, as a percentage.'} |
||||
{'id': 62410, 'data_set_name': '可以使用:anl45_risk_free_rate', 'description': '不可使用,仅供参考:The unrealised return on an open idea'} |
||||
{'id': 136453, 'data_set_name': '可以使用:total_equity_at_risk_2', 'description': '不可使用,仅供参考:Total stock, option, and incentive plan awards at risk for the director (alternate).'} |
||||
{'id': 137363, 'data_set_name': '可以使用:quarterly_return_on_equity_percent_3', 'description': '不可使用,仅供参考:Return on average equity for the most recent quarter (annualized).'} |
||||
{'id': 137391, 'data_set_name': '可以使用:return_on_average_assets_quarterly', 'description': '不可使用,仅供参考:Return on average assets for the most recent quarter, annualized.'} |
||||
{'id': 137460, 'data_set_name': '可以使用:annual_financial_risk_reserve', 'description': '不可使用,仅供参考:Annual financial risk reserve at the reporting date.'} |
||||
{'id': 137486, 'data_set_name': '可以使用:cumulative_financial_risk_reserve_since_q1', 'description': '不可使用,仅供参考:Cumulative financial risk reserve value since the first quarter.'} |
||||
{'id': 137538, 'data_set_name': '可以使用:financial_risk_reserve', 'description': '不可使用,仅供参考:Financial risk reserve at the reporting date.'} |
||||
{'id': 137544, 'data_set_name': '可以使用:financing_loan_drawdown_net', 'description': '不可使用,仅供参考:If long-term debt issuances and reductions are not delineated separately, the total is classified as Long Term Debt, Net'} |
||||
{'id': 137545, 'data_set_name': '可以使用:financing_loan_drawdown_receipts', 'description': '不可使用,仅供参考:represents cash outflow on the repayment of long-term debt in a company. Long-term debt obligations may be repaid upon maturity or replaced with new debt.'} |
||||
{'id': 139029, 'data_set_name': '可以使用:return_on_equity_ratio_3', 'description': "不可使用,仅供参考:Return on equity, calculated as net income divided by average shareholders' equity."} |
||||
{'id': 140116, 'data_set_name': '可以使用:fnd31_creditrisk', 'description': '不可使用,仅供参考:Credit Risk is measured by CDS levels based on end-of-day par spreads.'} |
||||
{'id': 140631, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_expected_return_pension', 'description': '不可使用,仅供参考:The component of net pension expense that pertains to the expected return on pension plan assets'} |
||||
{'id': 140632, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_expected_return_plan_assets', 'description': '不可使用,仅供参考:The estimated expected long-term rate of return on pension plan assets expressed as a percent'} |
||||
{'id': 140930, 'data_set_name': '可以使用:fnd86_risk_score', 'description': '不可使用,仅供参考:Risk score'} |
||||
{'id': 140940, 'data_set_name': '可以使用:srp_risk_score', 'description': '不可使用,仅供参考:Risk score'} |
||||
{'id': 373194, 'data_set_name': '可以使用:returns', 'description': '不可使用,仅供参考:Daily returns'} |
||||
{'id': 373297, 'data_set_name': '可以使用:pv173_ranksbondreturn20deqwt', 'description': '不可使用,仅供参考:It is defined as the equally weighted single bond return over last 20 days with the filter of the bonds that mature between 3 years and 7 years.'} |
||||
{'id': 373324, 'data_set_name': '可以使用:pv173_rawratiosbondreturn20deqwt', 'description': '不可使用,仅供参考:It is defined as the equally weighted single bond return over last 20 days with the filter of the bonds that mature between 3 years and 7 years.'} |
||||
{'id': 373351, 'data_set_name': '可以使用:pv173_zscoresbondreturn20deqwt', 'description': '不可使用,仅供参考:It is defined as the equally weighted single bond return over last 20 days with the filter of the bonds that mature between 3 years and 7 years.'} |
||||
========================= 数据字段结束 ======================================= |
||||
|
||||
以上数据字段和操作符, 按照Description说明组合, 但是每一个 alpha 组合的使用的数据字段和操作符不要过于集中, 在符合语法的情况下, 多尝试不同的组合 |
||||
|
||||
你再检查一下, 如果你使用了 |
||||
Operator abs does not support event inputs |
||||
Operator ts_mean does not support event inputs |
||||
Operator ts_scale does not support event inputs |
||||
Operator add does not support event inputs |
||||
Operator sign does not support event inputs |
||||
Operator greater does not support event inputs |
||||
Operator ts_av_diff does not support event inputs |
||||
Operator ts_quantile does not support event inputs |
||||
Operator ts_arg_min does not support event inputs |
||||
Operator divide does not support event inputs |
||||
Operator ts_corr does not support event inputs |
||||
Operator ts_decay_linear does not support event inputs |
||||
Operator ts_sum does not support event inputs |
||||
Operator ts_delay does not support event inputs |
||||
Operator ts_arg_max does not support event inputs |
||||
Operator ts_std_dev does not support event inputs |
||||
Operator ts_regression does not support event inputs |
||||
Operator ts_backfill does not support event inputs |
||||
Operator signed_power does not support event inputs |
||||
Operator ts_product does not support event inputs |
||||
Operator ts_zscore does not support event inputs |
||||
Operator group_rank does not support event inputs |
||||
Operator subtract does not support event inputs |
||||
Operator ts_delta does not support event inputs |
||||
Operator ts_rank does not support event inputs |
||||
Operator ts_count_nans does not support event inputs |
||||
Operator ts_covariance does not support event inputs |
||||
Operator multiply does not support event inputs |
||||
Operator if_else does not support event inputs |
||||
Operator group_neutralize does not support event inputs |
||||
Operator group_zscore does not support event inputs |
||||
Operator winsorize does not support event inputs |
||||
注意, 以上操作符不能使用事件类型的数据集, 以上操作符禁止使用事件类型的数据集!! |
||||
@ -0,0 +1,680 @@ |
||||
尾部流动性溢价因子 |
||||
假设 |
||||
在市场压力时期，流动性较差的小市值或低交易量股票往往遭遇更剧烈的价格下跌，但其反弹时也具备更高的超额收益潜力。这种“尾部流动性风险”被投资者系统性低估，导致这些股票在风险调整后长期提供正向alpha。该现象源于投资者对极端流动性枯竭事件的过度规避，形成行为偏差下的错误定价。 |
||||
实施方案 |
||||
基于过去60个交易日的日均成交额与流通市值比值，筛选处于全市场最低10%分位的股票；进一步计算其在最近一次市场大幅回撤（如指数单周跌幅>5%）后的20日累计收益，并以此作为因子值进行横截面排序。 |
||||
Tail Liquidity Premium Factor |
||||
Hypothesis |
||||
During periods of market stress, less liquid stocks—typically small-cap or low-trading-volume equities—experience sharper price declines but also exhibit greater upside potential during rebounds. This "tail liquidity risk" is systematically underpriced by investors, leading to persistent positive alphas on a risk-adjusted basis. The mispricing stems from behavioral biases, as investors overreact to the fear of extreme illiquidity events, resulting in excessive avoidance and undervaluation. |
||||
Implementation |
||||
Identify stocks falling within the lowest 10th percentile of the market based on the ratio of average daily trading volume to free-float market capitalization over the past 60 trading days. Then, compute each stock’s cumulative return over the 20 trading days following the most recent sharp market drawdown (defined as a >5% weekly drop in a broad market index). Use this post-crisis rebound return as the cross-sectional factor score. |
||||
|
||||
*=========================================================================================* |
||||
输出格式： |
||||
输出必须是且仅是纯文本。 |
||||
每一行是一个完整、独立、语法正确的WebSim表达式。 |
||||
严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。 |
||||
===================== !!! 重点(输出方式) !!! ===================== |
||||
现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。 |
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 |
||||
**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不需要赋值, 不要解释, 不需要序号, 也不要输出多余的东西)： |
||||
表达式 |
||||
表达式 |
||||
表达式 |
||||
... |
||||
表达式 |
||||
================================================================= |
||||
重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。 |
||||
以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 50 个 alpha： |
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 |
||||
!! 数据集使用要求尽量分散, 不要重复使用 |
||||
!! 数据集使用要求尽量分散, 不要重复使用 |
||||
================================================================= |
||||
**操作符汇总 |
||||
**算术运算符 (Arithmetic): |
||||
abs(x) - 绝对值 |
||||
add(x, y, filter=false) - 加法 (x + y) |
||||
densify(x) - 分组字段稠密化 |
||||
divide(x, y) - 除法 (x / y) |
||||
inverse(x) - 倒数 (1/x) |
||||
log(x) - 自然对数 |
||||
max(x, y, ..) - 最大值 |
||||
min(x, y, ..) - 最小值 |
||||
multiply(x, y, filter=false) - 乘法 (x * y) |
||||
power(x, y) - 幂运算 (x^y) |
||||
reverse(x) - 取反 (-x) |
||||
sign(x) - 符号函数 |
||||
signed_power(x, y) - 保留符号的幂运算 |
||||
sqrt(x) - 平方根 |
||||
subtract(x, y, filter=false) - 减法 (x - y) |
||||
to_nan(x, value=0, reverse=false) - 值与NaN转换 |
||||
**逻辑运算符 (Logical): |
||||
and(input1, input2) - 逻辑与 |
||||
if_else(input1, input2, input3) - 条件判断 |
||||
input1 < input2 - 小于比较 |
||||
input1 <= input2 - 小于等于 |
||||
input1 == input2 - 等于比较 |
||||
input1 > input2 - 大于比较 |
||||
input1 >= input2 - 大于等于 |
||||
input1 != input2 - 不等于 |
||||
is_nan(input) - 是否为NaN |
||||
not(x) - 逻辑非 |
||||
or(input1, input2) - 逻辑或 |
||||
**时间序列运算符 (Time Series): |
||||
days_from_last_change(x) - 上次变化天数 |
||||
hump(x, hump=0.01) - 限制变化幅度 |
||||
jump_decay(x, d, sensitivity=0.5, force=0.1) - 跳跃衰减 |
||||
kth_element(x, d, k) - 第K个值 |
||||
last_diff_value(x, d) - 最后一个不同值 |
||||
ts_arg_max(x, d) - 最大值相对索引 |
||||
ts_arg_min(x, d) - 最小值相对索引 |
||||
ts_av_diff(x, d) - 与均值的差 |
||||
ts_backfill(x, lookback=d, k=1, ignore="NAN") - 回填 |
||||
ts_corr(x, y, d) - 时间序列相关性 |
||||
ts_count_nans(x, d) - NaN计数 |
||||
ts_covariance(y, x, d) - 协方差 |
||||
ts_decay_linear(x, d, dense=false) - 线性衰减 |
||||
ts_delay(x, d) - 延迟值 |
||||
ts_delta(x, d) - 差值 (x - 延迟值) |
||||
ts_max(x, d) - 时间序列最大值 |
||||
ts_mean(x, d) - 时间序列均值 |
||||
ts_min(x, d) - 时间序列最小值 |
||||
ts_product(x, d) - 时间序列乘积 |
||||
ts_quantile(x, d, driver="gaussian") - 分位数 |
||||
ts_rank(x, d, constant=0) - 时间序列排名 |
||||
ts_regression(y, x, d, lag=0, rettype=0) - 回归分析 |
||||
ts_scale(x, d, constant=0) - 时间序列缩放 |
||||
ts_std_dev(x, d) - 时间序列标准差 |
||||
ts_step(1) - 天数计数器 |
||||
ts_sum(x, d) - 时间序列求和 |
||||
ts_target_tvr_decay(x, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率衰减 |
||||
ts_target_tvr_delta_limit(x, y, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率差值限制 |
||||
ts_zscore(x, d) - 时间序列Z分数 |
||||
**横截面运算符 (Cross Sectional): |
||||
normalize(x, useStd=false, limit=0.0) - 标准化 |
||||
quantile(x, driver=gaussian, sigma=1.0) - 分位数转换 |
||||
rank(x, rate=2) - 排名 |
||||
scale(x, scale=1, longscale=1, shortscale=1) - 缩放 |
||||
scale_down(x, constant=0) - 按比例缩放 |
||||
vector_neut(x, y) - 向量中性化 |
||||
winsorize(x, std=4) - 缩尾处理 |
||||
zscore(x) - Z分数 |
||||
**向量运算符 (Vector): |
||||
vec_avg(x) - 向量均值 |
||||
vec_max(x) - 向量最大值 |
||||
vec_min(x) - 向量最小值 |
||||
vec_sum(x) - 向量求和 |
||||
**变换运算符 (Transformational): |
||||
bucket(rank(x), range="0,1,0.1" or buckets="2,5,6,7,10") - 分桶 |
||||
generate_stats(alpha) - 生成统计量 |
||||
trade_when(x, y, z) - 条件交易 |
||||
**分组运算符 (Group): |
||||
combo_a(alpha, nlength=250, mode='algo1') - 组合Alpha |
||||
group_backfill(x, group, d, std=4.0) - 分组回填 |
||||
group_cartesian_product(g1, g2) - 笛卡尔积分组 |
||||
group_max(x, group) - 分组最大值 |
||||
group_mean(x, weight, group) - 分组均值 |
||||
group_min(x, group) - 分组最小值 |
||||
group_neutralize(x, group) - 分组中性化 |
||||
group_rank(x, group) - 分组排名 |
||||
group_scale(x, group) - 分组缩放 |
||||
group_zscore(x, group) - 分组Z分数 |
||||
**特殊运算符 (Special): |
||||
in - 包含判断 |
||||
self_corr(input) - 自相关性 |
||||
universe_size - 宇宙大小 |
||||
**归约运算符 (Reduce): |
||||
reduce_avg(input, threshold=0) - 平均值归约 |
||||
reduce_choose(input, nth, ignoreNan=true) - 选择归约 |
||||
reduce_count(input, threshold) - 计数归约 |
||||
reduce_ir(input) - IR归约 |
||||
reduce_kurtosis(input) - 峰度归约 |
||||
reduce_max(input) - 最大值归约 |
||||
reduce_min(input) - 最小值归约 |
||||
reduce_norm(input) - 范数归约 |
||||
reduce_percentage(input, percentage=0.5) - 百分比归约 |
||||
reduce_powersum(input, constant=2, precise=false) - 幂和归约 |
||||
reduce_range(input) - 范围归约 |
||||
reduce_skewness(input) - 偏度归约 |
||||
reduce_stddev(input, threshold=0) - 标准差归约 |
||||
reduce_sum(input) - 求和归约 |
||||
|
||||
以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子 |
||||
|
||||
========================= 操作符开始 ======================================= |
||||
注意: Operator: 后面的是操作符(是可以使用的), |
||||
Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符 |
||||
特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x) |
||||
Description: Absolute value of x |
||||
Operator: add(x, y, filter = false) |
||||
Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding |
||||
Operator: densify(x) |
||||
Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient |
||||
Operator: divide(x, y) |
||||
Description: x / y |
||||
Operator: inverse(x) |
||||
Description: 1 / x |
||||
Operator: log(x) |
||||
Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights. |
||||
Operator: max(x, y, ..) |
||||
Description: Maximum value of all inputs. At least 2 inputs are required |
||||
Operator: min(x, y ..) |
||||
Description: Minimum value of all inputs. At least 2 inputs are required |
||||
Operator: multiply(x ,y, ... , filter=false) |
||||
Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1 |
||||
Operator: power(x, y) |
||||
Description: x ^ y |
||||
Operator: reverse(x) |
||||
Description: - x |
||||
Operator: sign(x) |
||||
Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN; |
||||
Operator: signed_power(x, y) |
||||
Description: x raised to the power of y such that final result preserves sign of x |
||||
Operator: sqrt(x) |
||||
Description: Square root of x |
||||
Operator: subtract(x, y, filter=false) |
||||
Description: x-y. If filter = true, filter all input NaN to 0 before subtracting |
||||
Operator: and(input1, input2) |
||||
Description: Logical AND operator, returns true if both operands are true and returns false otherwise |
||||
Operator: if_else(input1, input2, input 3) |
||||
Description: If input1 is true then return input2 else return input3. |
||||
Operator: input1 < input2 |
||||
Description: If input1 < input2 return true, else return false |
||||
Operator: input1 <= input2 |
||||
Description: Returns true if input1 <= input2, return false otherwise |
||||
Operator: input1 == input2 |
||||
Description: Returns true if both inputs are same and returns false otherwise |
||||
Operator: input1 > input2 |
||||
Description: Logic comparison operators to compares two inputs |
||||
Operator: input1 >= input2 |
||||
Description: Returns true if input1 >= input2, return false otherwise |
||||
Operator: input1!= input2 |
||||
Description: Returns true if both inputs are NOT the same and returns false otherwise |
||||
Operator: is_nan(input) |
||||
Description: If (input == NaN) return 1 else return 0 |
||||
Operator: not(x) |
||||
Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1). |
||||
Operator: or(input1, input2) |
||||
Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise |
||||
Operator: days_from_last_change(x) |
||||
Description: Amount of days since last change of x |
||||
Operator: hump(x, hump = 0.01) |
||||
Description: Limits amount and magnitude of changes in input (thus reducing turnover) |
||||
Operator: kth_element(x, d, k) |
||||
Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1 |
||||
Operator: last_diff_value(x, d) |
||||
Description: Returns last x value not equal to current x value from last d days |
||||
Operator: ts_arg_max(x, d) |
||||
Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1 |
||||
Operator: ts_arg_min(x, d) |
||||
Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1. |
||||
Operator: ts_av_diff(x, d) |
||||
Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation |
||||
Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN") |
||||
Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value) |
||||
Operator: ts_corr(x, y, d) |
||||
Description: Returns correlation of x and y for the past d days |
||||
Operator: ts_count_nans(x ,d) |
||||
Description: Returns the number of NaN values in x for the past d days |
||||
Operator: ts_covariance(y, x, d) |
||||
Description: Returns covariance of y and x for the past d days |
||||
Operator: ts_decay_linear(x, d, dense = false) |
||||
Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not. |
||||
Operator: ts_delay(x, d) |
||||
Description: Returns x value d days ago |
||||
Operator: ts_delta(x, d) |
||||
Description: Returns x - ts_delay(x, d) |
||||
Operator: ts_mean(x, d) |
||||
Description: Returns average value of x for the past d days. |
||||
Operator: ts_product(x, d) |
||||
Description: Returns product of x for the past d days |
||||
Operator: ts_quantile(x,d, driver="gaussian" ) |
||||
Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default. |
||||
Operator: ts_rank(x, d, constant = 0) |
||||
Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0. |
||||
Operator: ts_regression(y, x, d, lag = 0, rettype = 0) |
||||
Description: Returns various parameters related to regression function |
||||
Operator: ts_scale(x, d, constant = 0) |
||||
Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space |
||||
Operator: ts_std_dev(x, d) |
||||
Description: Returns standard deviation of x for the past d days |
||||
Operator: ts_step(1) |
||||
Description: Returns days' counter |
||||
Operator: ts_sum(x, d) |
||||
Description: Sum values of x for the past d days. |
||||
Operator: ts_zscore(x, d) |
||||
Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown. |
||||
Operator: normalize(x, useStd = false, limit = 0.0) |
||||
Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element |
||||
Operator: quantile(x, driver = gaussian, sigma = 1.0) |
||||
Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector |
||||
Operator: rank(x, rate=2) |
||||
Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0 |
||||
Operator: scale(x, scale=1, longscale=1, shortscale=1) |
||||
Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator |
||||
Operator: winsorize(x, std=4) |
||||
Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std. |
||||
Operator: zscore(x) |
||||
Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean |
||||
Operator: vec_avg(x) |
||||
Description: Taking mean of the vector field x |
||||
Operator: vec_sum(x) |
||||
Description: Sum of vector field x |
||||
Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10") |
||||
Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input |
||||
Operator: trade_when(x, y, z) |
||||
Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition |
||||
Operator: group_backfill(x, group, d, std = 4.0) |
||||
Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days |
||||
Operator: group_mean(x, weight, group) |
||||
Description: All elements in group equals to the mean |
||||
Operator: group_neutralize(x, group) |
||||
Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant |
||||
Operator: group_rank(x, group) |
||||
Description: Each elements in a group is assigned the corresponding rank in this group |
||||
Operator: group_scale(x, group) |
||||
Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin) |
||||
Operator: group_zscore(x, group) |
||||
Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group. |
||||
========================= 操作符结束 ======================================= |
||||
|
||||
========================= 数据字段开始 ======================================= |
||||
注意: data_set_name: 后面的是数据字段(可以使用), description: 此字段后面的是数据字段对应的描述或使用说明(不能使用) |
||||
|
||||
{'id': 60724, 'data_set_name': '可以使用:anl14_actvalue_capex_fp0', 'description': '不可使用,仅供参考:Capital Expenditures - Recent Last Quarter'} |
||||
{'id': 60752, 'data_set_name': '可以使用:anl14_high_capex_fp1', 'description': '不可使用,仅供参考:The highest estimation of capital expenditures - upcoming quarter'} |
||||
{'id': 60753, 'data_set_name': '可以使用:anl14_high_capex_fp2', 'description': '不可使用,仅供参考:The Highest estimation of Capital Expenditures - upcoming 2 quarters'} |
||||
{'id': 60754, 'data_set_name': '可以使用:anl14_high_capex_fp3', 'description': '不可使用,仅供参考:The Highest Estimation of Capital Expenditures - upcoming 3 quarters'} |
||||
{'id': 60755, 'data_set_name': '可以使用:anl14_high_capex_fp4', 'description': '不可使用,仅供参考:The Highest Estimation of Capital Expenditures - Upcoming 4 Quarters'} |
||||
{'id': 60756, 'data_set_name': '可以使用:anl14_high_capex_fp5', 'description': '不可使用,仅供参考:The Highest Estimation of Capital Expenditures - upcoming 5 quarters'} |
||||
{'id': 60757, 'data_set_name': '可以使用:anl14_high_capex_fy1', 'description': '不可使用,仅供参考:The highest estimation of Capital Expenditures - upcoming year'} |
||||
{'id': 60758, 'data_set_name': '可以使用:anl14_high_capex_fy2', 'description': '不可使用,仅供参考:The Highest Estimation of Capital Expenditures - upcoming 2 years'} |
||||
{'id': 60759, 'data_set_name': '可以使用:anl14_high_capex_fy3', 'description': '不可使用,仅供参考:The highest estimation of capital expenditures - upcoming 3 years'} |
||||
{'id': 60760, 'data_set_name': '可以使用:anl14_high_capex_fy4', 'description': '不可使用,仅供参考:The highest estimation of capital expenditures - upcoming 4 years'} |
||||
{'id': 60761, 'data_set_name': '可以使用:anl14_high_capex_fy5', 'description': '不可使用,仅供参考:The Highest Estimation of Capital Expenditures - upcoming 5 years'} |
||||
{'id': 60902, 'data_set_name': '可以使用:anl14_low_capex_fp1', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - upcoming quarter'} |
||||
{'id': 60903, 'data_set_name': '可以使用:anl14_low_capex_fp2', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - upcoming 2 quarters'} |
||||
{'id': 60904, 'data_set_name': '可以使用:anl14_low_capex_fp3', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - upcoming 3 quarters'} |
||||
{'id': 60905, 'data_set_name': '可以使用:anl14_low_capex_fp4', 'description': '不可使用,仅供参考:The lowest estimation of capital expenditures - upcoming 4 quarters'} |
||||
{'id': 60906, 'data_set_name': '可以使用:anl14_low_capex_fp5', 'description': '不可使用,仅供参考:The lowest estimation of Capital Expenditures - upcoming 5 quarters'} |
||||
{'id': 60907, 'data_set_name': '可以使用:anl14_low_capex_fy1', 'description': '不可使用,仅供参考:The lowest estimation of capital expenditures - upcoming year'} |
||||
{'id': 60908, 'data_set_name': '可以使用:anl14_low_capex_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - Upcoming 2 Years'} |
||||
{'id': 60909, 'data_set_name': '可以使用:anl14_low_capex_fy3', 'description': '不可使用,仅供参考:The Lowest estimation of Capital Expenditures - upcoming 3 years'} |
||||
{'id': 60910, 'data_set_name': '可以使用:anl14_low_capex_fy4', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - upcoming 4 years'} |
||||
{'id': 60911, 'data_set_name': '可以使用:anl14_low_capex_fy5', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - upcoming 5 years'} |
||||
{'id': 61052, 'data_set_name': '可以使用:anl14_mean_capex_fp1', 'description': '不可使用,仅供参考:Mean of Estimations of Capital Expenditures - upcoming quarter'} |
||||
{'id': 61053, 'data_set_name': '可以使用:anl14_mean_capex_fp2', 'description': '不可使用,仅供参考:Mean of Estimations of Capital Expenditures - Upcoming 2 Quarters'} |
||||
{'id': 61054, 'data_set_name': '可以使用:anl14_mean_capex_fp3', 'description': '不可使用,仅供参考:Mean of estimations of capital expenditures - upcoming 3 quarters'} |
||||
{'id': 61055, 'data_set_name': '可以使用:anl14_mean_capex_fp4', 'description': '不可使用,仅供参考:Mean of estimations of Capital Expenditures - upcoming 4 quarters'} |
||||
{'id': 61056, 'data_set_name': '可以使用:anl14_mean_capex_fp5', 'description': '不可使用,仅供参考:Mean of estimations of capital expenditures - upcoming 5 quarters'} |
||||
{'id': 61057, 'data_set_name': '可以使用:anl14_mean_capex_fy1', 'description': '不可使用,仅供参考:Mean of Estimations of Capital Expenditures - upcoming year'} |
||||
{'id': 61058, 'data_set_name': '可以使用:anl14_mean_capex_fy2', 'description': '不可使用,仅供参考:Mean of Estimations of Capital Expenditures - upcoming 2 years'} |
||||
{'id': 61059, 'data_set_name': '可以使用:anl14_mean_capex_fy3', 'description': '不可使用,仅供参考:Mean of Estimations of Capital Expenditures - upcoming 3 years'} |
||||
{'id': 61060, 'data_set_name': '可以使用:anl14_mean_capex_fy4', 'description': '不可使用,仅供参考:Mean of Estimations of Capital Expenditures - Upcoming 4 Years'} |
||||
{'id': 61061, 'data_set_name': '可以使用:anl14_mean_capex_fy5', 'description': '不可使用,仅供参考:Mean of Estimations of Capital Expenditures - upcoming 5 years'} |
||||
{'id': 61202, 'data_set_name': '可以使用:anl14_median_capex_fp1', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - Upcoming Quarter'} |
||||
{'id': 61203, 'data_set_name': '可以使用:anl14_median_capex_fp2', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 2 quarters'} |
||||
{'id': 61204, 'data_set_name': '可以使用:anl14_median_capex_fp3', 'description': '不可使用,仅供参考:Median of estimations of capital expenditures - upcoming 3 quarters'} |
||||
{'id': 61205, 'data_set_name': '可以使用:anl14_median_capex_fp4', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 4 quarters'} |
||||
{'id': 61206, 'data_set_name': '可以使用:anl14_median_capex_fp5', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 5 quarters'} |
||||
{'id': 61207, 'data_set_name': '可以使用:anl14_median_capex_fy1', 'description': '不可使用,仅供参考:Median of estimations of Capital Expenditures - upcoming year'} |
||||
{'id': 61208, 'data_set_name': '可以使用:anl14_median_capex_fy2', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 2 years'} |
||||
{'id': 61209, 'data_set_name': '可以使用:anl14_median_capex_fy3', 'description': '不可使用,仅供参考:Median of estimations of capital expenditures - upcoming 3 years'} |
||||
{'id': 61210, 'data_set_name': '可以使用:anl14_median_capex_fy4', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 4 years'} |
||||
{'id': 61211, 'data_set_name': '可以使用:anl14_median_capex_fy5', 'description': '不可使用,仅供参考:Median of estimations of capital expenditures - upcoming 5 years'} |
||||
{'id': 61352, 'data_set_name': '可以使用:anl14_numofests_capex_fp1', 'description': '不可使用,仅供参考:Num of estimations of capital expenditures - upcoming quarter'} |
||||
{'id': 61353, 'data_set_name': '可以使用:anl14_numofests_capex_fp2', 'description': '不可使用,仅供参考:Number of Estimations of Capital Expenditures - Upcoming 2 Quarters'} |
||||
{'id': 61354, 'data_set_name': '可以使用:anl14_numofests_capex_fp3', 'description': '不可使用,仅供参考:Num of Estimations of Capital Expenditures - Upcoming 3 Quarters'} |
||||
{'id': 61355, 'data_set_name': '可以使用:anl14_numofests_capex_fp4', 'description': '不可使用,仅供参考:Num of Estimations of Capital Expenditures - upcoming 4 quarters'} |
||||
{'id': 61356, 'data_set_name': '可以使用:anl14_numofests_capex_fp5', 'description': '不可使用,仅供参考:Num of Estimations of Capital Expenditures - Upcoming 5 Quarters'} |
||||
{'id': 61357, 'data_set_name': '可以使用:anl14_numofests_capex_fy1', 'description': '不可使用,仅供参考:Num of estimations of capital expenditures - upcoming year'} |
||||
{'id': 61358, 'data_set_name': '可以使用:anl14_numofests_capex_fy2', 'description': '不可使用,仅供参考:Num of Estimations of Capital Expenditures - upcoming 2 years'} |
||||
{'id': 61359, 'data_set_name': '可以使用:anl14_numofests_capex_fy3', 'description': '不可使用,仅供参考:Num of Estimations of Capital Expenditures - Upcoming 3 Years'} |
||||
{'id': 61360, 'data_set_name': '可以使用:anl14_numofests_capex_fy4', 'description': '不可使用,仅供参考:Num of Estimations of Capital Expenditures - Upcoming 4 Years'} |
||||
{'id': 61361, 'data_set_name': '可以使用:anl14_numofests_capex_fy5', 'description': '不可使用,仅供参考:Num of Estimations of Capital Expenditures - upcoming 5 years'} |
||||
{'id': 61502, 'data_set_name': '可以使用:anl14_stddev_capex_fp1', 'description': '不可使用,仅供参考:Standard Deviation of Estimations of Capital Expenditures - upcoming quarter'} |
||||
{'id': 61503, 'data_set_name': '可以使用:anl14_stddev_capex_fp2', 'description': '不可使用,仅供参考:Standard Deviation of Estimations of Capital Expenditures - upcoming 2 quarters'} |
||||
{'id': 61504, 'data_set_name': '可以使用:anl14_stddev_capex_fp3', 'description': '不可使用,仅供参考:Standard Deviation of Estimations of Capital Expenditures - upcoming 3 quarters'} |
||||
{'id': 61505, 'data_set_name': '可以使用:anl14_stddev_capex_fp4', 'description': '不可使用,仅供参考:Standard deviation of estimations of capital expenditures - upcoming 4 quarters'} |
||||
{'id': 61506, 'data_set_name': '可以使用:anl14_stddev_capex_fp5', 'description': '不可使用,仅供参考:Standard Deviation of Estimations of Capital Expenditures - Upcoming 5 Quarters'} |
||||
{'id': 61507, 'data_set_name': '可以使用:anl14_stddev_capex_fy1', 'description': '不可使用,仅供参考:Standard deviation of estimations of capital expenditures - upcoming year'} |
||||
{'id': 61508, 'data_set_name': '可以使用:anl14_stddev_capex_fy2', 'description': '不可使用,仅供参考:Standard Deviation of Estimations of Capital Expenditures - upcoming 2 years'} |
||||
{'id': 61509, 'data_set_name': '可以使用:anl14_stddev_capex_fy3', 'description': '不可使用,仅供参考:Standard Deviation of Estimations of Capital Expenditures - upcoming 3 years'} |
||||
{'id': 61510, 'data_set_name': '可以使用:anl14_stddev_capex_fy4', 'description': '不可使用,仅供参考:Standard Deviation of Estimations of Capital Expenditures - upcoming 4 years'} |
||||
{'id': 61511, 'data_set_name': '可以使用:anl14_stddev_capex_fy5', 'description': '不可使用,仅供参考:Standard Deviation of Estimations of Capital Expenditures - upcoming 5 years'} |
||||
{'id': 61659, 'data_set_name': '可以使用:anl15_gr_12_m_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS group grouping with a 12-month forward mean price estimation.'} |
||||
{'id': 61675, 'data_set_name': '可以使用:anl15_gr_18_m_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS group grouping with an 18-month forward mean price estimation.'} |
||||
{'id': 61690, 'data_set_name': '可以使用:anl15_gr_cal_fy1_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS group grouping with a calendarized 1 fiscal year mean price estimation.'} |
||||
{'id': 61701, 'data_set_name': '可以使用:anl15_gr_cal_fy2_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS group grouping with a calendarized 2 fiscal year mean price estimation.'} |
||||
{'id': 61712, 'data_set_name': '可以使用:anl15_gr_cal_fy3_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS group grouping with a calendarized 3 fiscal year mean price estimation.'} |
||||
{'id': 61741, 'data_set_name': '可以使用:anl15_gr_ltg_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS group grouping with a long-term growth price estimation.'} |
||||
{'id': 61755, 'data_set_name': '可以使用:anl15_ind_12_m_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS industry grouping with a 12-month forward mean price estimation.'} |
||||
{'id': 61771, 'data_set_name': '可以使用:anl15_ind_18_m_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS industry grouping with an 18-month forward mean price estimation.'} |
||||
{'id': 61786, 'data_set_name': '可以使用:anl15_ind_cal_fy1_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS industry grouping with a calendarized 1 fiscal year mean price estimation.'} |
||||
{'id': 61797, 'data_set_name': '可以使用:anl15_ind_cal_fy2_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS industry grouping with a calendarized 2 fiscal year mean price estimation.'} |
||||
{'id': 61808, 'data_set_name': '可以使用:anl15_ind_cal_fy3_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS industry grouping with a calendarized 3 fiscal year mean price estimation.'} |
||||
{'id': 61837, 'data_set_name': '可以使用:anl15_ind_ltg_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS industry grouping with a long-term growth price estimation.'} |
||||
{'id': 61851, 'data_set_name': '可以使用:anl15_s_12_m_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS sector grouping with a 12-month forward mean price estimation.'} |
||||
{'id': 61867, 'data_set_name': '可以使用:anl15_s_18_m_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS sector grouping with an 18-month forward mean price estimation.'} |
||||
{'id': 61882, 'data_set_name': '可以使用:anl15_s_cal_fy1_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS sector grouping with a calendarized 1 fiscal year mean price estimation.'} |
||||
{'id': 61893, 'data_set_name': '可以使用:anl15_s_cal_fy2_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS sector grouping with a calendarized 2 fiscal year mean price estimation.'} |
||||
{'id': 61904, 'data_set_name': '可以使用:anl15_s_cal_fy3_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS sector grouping with a calendarized 3 fiscal year mean price estimation.'} |
||||
{'id': 61933, 'data_set_name': '可以使用:anl15_s_ltg_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS sector grouping with a long-term growth price estimation.'} |
||||
{'id': 61967, 'data_set_name': '可以使用:debt_to_equity_ratio_quarterly_prior_year', 'description': '不可使用,仅供参考:Ratio of total debt to total equity for the same quarter one year ago.'} |
||||
{'id': 61973, 'data_set_name': '可以使用:lowest_pe_ratio_quarterly', 'description': '不可使用,仅供参考:Lowest price-to-earnings ratio for the most recent quarter.'} |
||||
{'id': 61979, 'data_set_name': '可以使用:quarterly_asset_turnover', 'description': '不可使用,仅供参考:Annualized ratio of sales to average assets for the quarter.'} |
||||
{'id': 61980, 'data_set_name': '可以使用:quarterly_asset_turnover_2', 'description': '不可使用,仅供参考:Annualized ratio of sales to average assets for the quarter.'} |
||||
{'id': 61981, 'data_set_name': '可以使用:quarterly_asset_turnover_db', 'description': '不可使用,仅供参考:Asset turnover for the most recent quarter (alternate source).'} |
||||
{'id': 61988, 'data_set_name': '可以使用:quarterly_capital_spending_per_share', 'description': '不可使用,仅供参考:Capital spending divided by average shares for the quarter.'} |
||||
{'id': 61989, 'data_set_name': '可以使用:quarterly_capital_spending_per_share_2', 'description': '不可使用,仅供参考:Capital spending per share for the most recent quarter.'} |
||||
{'id': 61990, 'data_set_name': '可以使用:quarterly_capital_spending_per_share_3', 'description': '不可使用,仅供参考:Capital spending divided by average shares for the quarter.'} |
||||
{'id': 62021, 'data_set_name': '可以使用:quarterly_current_ratio', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the quarter.'} |
||||
{'id': 62022, 'data_set_name': '可以使用:quarterly_current_ratio_2', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the most recent quarter.'} |
||||
{'id': 62023, 'data_set_name': '可以使用:quarterly_current_ratio_3', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the quarter.'} |
||||
{'id': 62024, 'data_set_name': '可以使用:quarterly_current_ratio_alt', 'description': '不可使用,仅供参考:Alternate calculation of current ratio for the most recent quarter.'} |
||||
{'id': 62025, 'data_set_name': '可以使用:quarterly_current_ratio_prior', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the same quarter last year.'} |
||||
{'id': 62026, 'data_set_name': '可以使用:quarterly_current_ratio_prior_year', 'description': '不可使用,仅供参考:Current assets divided by current liabilities for the same quarter last year.'} |
||||
{'id': 62032, 'data_set_name': '可以使用:quarterly_dividend_payout_ratio', 'description': '不可使用,仅供参考:Percentage of earnings paid out as dividends for the quarter.'} |
||||
{'id': 62068, 'data_set_name': '可以使用:quarterly_interest_coverage_ratio', 'description': '不可使用,仅供参考:Earnings before interest and taxes divided by interest expense for the quarter.'} |
||||
{'id': 62074, 'data_set_name': '可以使用:quarterly_inventory_turnover', 'description': '不可使用,仅供参考:Annualized ratio of cost of goods sold to average inventory for the quarter.'} |
||||
{'id': 62075, 'data_set_name': '可以使用:quarterly_inventory_turnover_2', 'description': '不可使用,仅供参考:Annualized cost of goods sold divided by average inventory for the quarter.'} |
||||
{'id': 62076, 'data_set_name': '可以使用:quarterly_inventory_turnover_ratio', 'description': '不可使用,仅供参考:Inventory turnover ratio for the most recent quarter.'} |
||||
{'id': 62086, 'data_set_name': '可以使用:quarterly_long_term_debt_to_assets_ratio', 'description': '不可使用,仅供参考:Long-term debt divided by total assets for the most recent quarter.'} |
||||
{'id': 62087, 'data_set_name': '可以使用:quarterly_long_term_debt_to_capital', 'description': '不可使用,仅供参考:Long-term debt divided by total capital for the quarter.'} |
||||
{'id': 62092, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_ratio', 'description': '不可使用,仅供参考:Long-term debt divided by equity for the most recent quarter.'} |
||||
{'id': 62093, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_ratio_prior_year', 'description': '不可使用,仅供参考:Long-term debt divided by equity for the same quarter last year.'} |
||||
{'id': 62094, 'data_set_name': '可以使用:quarterly_long_term_debt_to_total_capital', 'description': '不可使用,仅供参考:Ratio of long-term debt to total capital for the quarter.'} |
||||
{'id': 62095, 'data_set_name': '可以使用:quarterly_long_term_debt_to_total_capital_2', 'description': '不可使用,仅供参考:Long-term debt divided by total capital for the quarter.'} |
||||
{'id': 62122, 'data_set_name': '可以使用:quarterly_payout_ratio', 'description': '不可使用,仅供参考:Percentage of earnings paid as dividends for the quarter.'} |
||||
{'id': 62123, 'data_set_name': '可以使用:quarterly_payout_ratio_2', 'description': '不可使用,仅供参考:Percentage of earnings paid as dividends for the quarter.'} |
||||
{'id': 62124, 'data_set_name': '可以使用:quarterly_pe_ratio_high', 'description': '不可使用,仅供参考:Highest price-to-earnings ratio for the most recent quarter.'} |
||||
{'id': 62125, 'data_set_name': '可以使用:quarterly_pe_ratio_high_2', 'description': '不可使用,仅供参考:Highest price-to-earnings ratio for the quarter.'} |
||||
{'id': 62126, 'data_set_name': '可以使用:quarterly_pe_ratio_low', 'description': '不可使用,仅供参考:Lowest price-to-earnings ratio for the most recent quarter.'} |
||||
{'id': 62127, 'data_set_name': '可以使用:quarterly_pe_ratio_maximum', 'description': '不可使用,仅供参考:Highest price-to-earnings ratio observed in the latest quarter.'} |
||||
{'id': 62128, 'data_set_name': '可以使用:quarterly_pe_ratio_minimum', 'description': '不可使用,仅供参考:Lowest price-to-earnings ratio observed in the latest quarter.'} |
||||
{'id': 62134, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'} |
||||
{'id': 62135, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio_2', 'description': '不可使用,仅供参考:Ratio of price to sales for the most recent quarter.'} |
||||
{'id': 62136, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio_3', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'} |
||||
{'id': 62137, 'data_set_name': '可以使用:quarterly_quick_ratio', 'description': '不可使用,仅供参考:Ratio of liquid assets to current liabilities for the latest quarter.'} |
||||
{'id': 62138, 'data_set_name': '可以使用:quarterly_quick_ratio_2', 'description': '不可使用,仅供参考:Quick ratio for the most recent quarter.'} |
||||
{'id': 62139, 'data_set_name': '可以使用:quarterly_quick_ratio_3', 'description': '不可使用,仅供参考:Quick ratio for the most recent quarter.'} |
||||
{'id': 62140, 'data_set_name': '可以使用:quarterly_quick_ratio_alt', 'description': '不可使用,仅供参考:Alternate calculation of quick ratio for the most recent quarter.'} |
||||
{'id': 62141, 'data_set_name': '可以使用:quarterly_quick_ratio_prior', 'description': '不可使用,仅供参考:Quick ratio for the same quarter last year.'} |
||||
{'id': 62142, 'data_set_name': '可以使用:quarterly_quick_ratio_prior_year', 'description': '不可使用,仅供参考:Quick ratio for the same quarter in the previous year.'} |
||||
{'id': 62146, 'data_set_name': '可以使用:quarterly_receivables_turnover', 'description': '不可使用,仅供参考:Annualized ratio of sales to average receivables for the quarter.'} |
||||
{'id': 62147, 'data_set_name': '可以使用:quarterly_receivables_turnover_2', 'description': '不可使用,仅供参考:Receivables turnover ratio for the most recent quarter.'} |
||||
{'id': 62154, 'data_set_name': '可以使用:quarterly_return_on_assets_percent', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter, as a percentage.'} |
||||
{'id': 62155, 'data_set_name': '可以使用:quarterly_return_on_assets_percent_2', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter, as a percentage.'} |
||||
{'id': 62156, 'data_set_name': '可以使用:quarterly_return_on_average_assets', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter.'} |
||||
{'id': 62157, 'data_set_name': '可以使用:quarterly_return_on_average_equity', 'description': '不可使用,仅供参考:Annualized net income divided by average equity for the quarter.'} |
||||
{'id': 62158, 'data_set_name': '可以使用:quarterly_return_on_equity_percent', 'description': '不可使用,仅供参考:Annualized net income divided by average equity for the quarter, as a percentage.'} |
||||
{'id': 62159, 'data_set_name': '可以使用:quarterly_return_on_equity_percent_2', 'description': '不可使用,仅供参考:Annualized net income divided by average equity for the quarter, as a percentage.'} |
||||
{'id': 62160, 'data_set_name': '可以使用:quarterly_return_on_investment', 'description': '不可使用,仅供参考:Annualized net income divided by average investment for the quarter.'} |
||||
{'id': 62161, 'data_set_name': '可以使用:quarterly_return_on_investment_percent', 'description': '不可使用,仅供参考:Annualized net income divided by average investment for the quarter.'} |
||||
{'id': 62162, 'data_set_name': '可以使用:quarterly_return_on_investment_percent_2', 'description': '不可使用,仅供参考:Annualized net income divided by average investment for the quarter, as a percentage.'} |
||||
{'id': 62172, 'data_set_name': '可以使用:quarterly_sga_to_revenue_ratio', 'description': '不可使用,仅供参考:Selling, general, and administrative expenses as a percentage of revenue for the quarter.'} |
||||
{'id': 62173, 'data_set_name': '可以使用:quarterly_sga_to_sales_ratio', 'description': '不可使用,仅供参考:Selling, general, and administrative expenses as a percent of revenue for the quarter.'} |
||||
{'id': 62174, 'data_set_name': '可以使用:quarterly_sga_to_sales_ratio_2', 'description': '不可使用,仅供参考:SG&A expenses as a percent of revenue for the quarter.'} |
||||
{'id': 62195, 'data_set_name': '可以使用:quarterly_total_debt_to_assets_ratio', 'description': '不可使用,仅供参考:Total debt divided by total assets for the most recent quarter.'} |
||||
{'id': 62196, 'data_set_name': '可以使用:quarterly_total_debt_to_capital', 'description': '不可使用,仅供参考:Ratio of total debt to total capital for the quarter.'} |
||||
{'id': 62197, 'data_set_name': '可以使用:quarterly_total_debt_to_capital_2', 'description': '不可使用,仅供参考:Total debt divided by total capital for the quarter.'} |
||||
{'id': 62201, 'data_set_name': '可以使用:quarterly_total_debt_to_equity_ratio', 'description': '不可使用,仅供参考:Total debt divided by equity for the most recent quarter.'} |
||||
{'id': 62202, 'data_set_name': '可以使用:quarterly_total_debt_to_equity_ratio_prior_year', 'description': '不可使用,仅供参考:Total debt divided by equity for the same quarter last year.'} |
||||
{'id': 62203, 'data_set_name': '可以使用:quarterly_total_debt_to_total_capital_ratio', 'description': '不可使用,仅供参考:Total debt divided by total capital for the most recent quarter.'} |
||||
{'id': 62211, 'data_set_name': '可以使用:quarterly_working_capital_per_share_to_price', 'description': '不可使用,仅供参考:Working capital per share divided by current price for the quarter.'} |
||||
{'id': 62212, 'data_set_name': '可以使用:quarterly_working_capital_per_share_to_price_2', 'description': '不可使用,仅供参考:Ratio of working capital per share to current price for the quarter.'} |
||||
{'id': 62213, 'data_set_name': '可以使用:quarterly_working_capital_per_share_to_price_db', 'description': '不可使用,仅供参考:Working capital per share divided by price for the quarter (alternate source).'} |
||||
{'id': 62214, 'data_set_name': '可以使用:receivables_turnover_quarterly', 'description': '不可使用,仅供参考:Annualized ratio of total revenue to average receivables for the most recent quarter.'} |
||||
{'id': 62226, 'data_set_name': '可以使用:ttm_return_on_average_equity', 'description': '不可使用,仅供参考:Net income divided by average equity for the trailing twelve months.'} |
||||
{'id': 62227, 'data_set_name': '可以使用:ttm_return_on_equity_percent', 'description': '不可使用,仅供参考:Net income divided by average equity for trailing twelve months, as a percentage.'} |
||||
{'id': 62228, 'data_set_name': '可以使用:ttm_return_on_equity_percent_2', 'description': '不可使用,仅供参考:Net income divided by average equity for trailing twelve months, as a percentage.'} |
||||
{'id': 62236, 'data_set_name': '可以使用:anl44_2_capex_coveredby', 'description': '不可使用,仅供参考:capex coveredby'} |
||||
{'id': 62237, 'data_set_name': '可以使用:anl44_2_capex_lastactccy', 'description': '不可使用,仅供参考:capex lastactccy'} |
||||
{'id': 62238, 'data_set_name': '可以使用:anl44_2_capex_lastactvalue', 'description': '不可使用,仅供参考:capex lastactvalue'} |
||||
{'id': 62239, 'data_set_name': '可以使用:anl44_2_capex_prevalue', 'description': '不可使用,仅供参考:capex prevalue'} |
||||
{'id': 62240, 'data_set_name': '可以使用:anl44_2_capex_value', 'description': '不可使用,仅供参考:capex value'} |
||||
{'id': 62346, 'data_set_name': '可以使用:capex_currency_code', 'description': '不可使用,仅供参考:Currency code in which the capital expenditure forecast is denominated.'} |
||||
{'id': 62392, 'data_set_name': '可以使用:anl45_net_market_exposure', 'description': '不可使用,仅供参考:Difference in fractional total value between long and short ideas'} |
||||
{'id': 62419, 'data_set_name': '可以使用:anl45_treynor_ratio', 'description': '不可使用,仅供参考:A ratio that measures returns earned in excess of that which could have been earned on a riskless investment (such as the risk free rate).??'} |
||||
{'id': 62425, 'data_set_name': '可以使用:security_trading_currency_3', 'description': '不可使用,仅供参考:Currency in which the security is traded.'} |
||||
{'id': 62480, 'data_set_name': '可以使用:anl69_best_pe_ratio', 'description': '不可使用,仅供参考:P/E Ratio'} |
||||
{'id': 62481, 'data_set_name': '可以使用:anl69_best_px_bps_ratio', 'description': '不可使用,仅供参考:P/Bk'} |
||||
{'id': 62482, 'data_set_name': '可以使用:anl69_best_px_cps_ratio', 'description': '不可使用,仅供参考:P/CF'} |
||||
{'id': 136375, 'data_set_name': '可以使用:board_nationality_diversity_ratio_2', 'description': '不可使用,仅供参考:Proportion of directors from different countries on the board (alternate).'} |
||||
{'id': 136383, 'data_set_name': '可以使用:director_attrition_ratio_2', 'description': '不可使用,仅供参考:Proportion of directors who have left a role in the preceding period (alternate).'} |
||||
{'id': 136384, 'data_set_name': '可以使用:director_attrition_ratio_3yr', 'description': '不可使用,仅供参考:Proportion of directors who have left a role in the preceding three years.'} |
||||
{'id': 136386, 'data_set_name': '可以使用:equity_linked_compensation_ratio_2', 'description': '不可使用,仅供参考:Proportion of equity-linked compensation in total director remuneration (alternate).'} |
||||
{'id': 136417, 'data_set_name': '可以使用:fnd1_genderratio', 'description': '不可使用,仅供参考:The proportion of male directors whether for the Executive Directors'} |
||||
{'id': 136434, 'data_set_name': '可以使用:ltip_to_total_remuneration_ratio_2', 'description': '不可使用,仅供参考:Ratio of long-term incentive plan value to total remuneration for the period (alternate).'} |
||||
{'id': 136446, 'data_set_name': '可以使用:security_market_price_ltip', 'description': '不可使用,仅供参考:Price of the stock at the relevant report date for LTIP compensation.'} |
||||
{'id': 136456, 'data_set_name': '可以使用:total_remuneration_period', 'description': '不可使用,仅供参考:Total remuneration awarded to the director for the period.'} |
||||
{'id': 136530, 'data_set_name': '可以使用:annual_debt_to_total_capital', 'description': '不可使用,仅供参考:Ratio of total debt to total capital for the most recent fiscal year.'} |
||||
{'id': 136557, 'data_set_name': '可以使用:daily_volume_percent_shares_out', 'description': '不可使用,仅供参考:Average daily trading volume as a percentage of shares outstanding.'} |
||||
{'id': 136558, 'data_set_name': '可以使用:daily_volume_to_shares_outstanding', 'description': '不可使用,仅供参考:Average daily trading volume as a percentage of shares outstanding.'} |
||||
{'id': 136614, 'data_set_name': '可以使用:fnd17_acapspps', 'description': '不可使用,仅供参考:Capital Spending per Share, most recent fiscal year'} |
||||
{'id': 136618, 'data_set_name': '可以使用:fnd17_acurratio', 'description': '不可使用,仅供参考:Current ratio - most recent fiscal year'} |
||||
{'id': 136649, 'data_set_name': '可以使用:fnd17_alldelay1_acapspps', 'description': '不可使用,仅供参考:Capital Spending per Share, most recent fiscal year'} |
||||
{'id': 136653, 'data_set_name': '可以使用:fnd17_alldelay1_acurratio', 'description': '不可使用,仅供参考:Current ratio - most recent fiscal year'} |
||||
{'id': 136675, 'data_set_name': '可以使用:fnd17_alldelay1_altd2cap', 'description': '不可使用,仅供参考:LT debt/total capital - most recent fiscal year'} |
||||
{'id': 136690, 'data_set_name': '可以使用:fnd17_alldelay1_apayratio', 'description': '不可使用,仅供参考:Payout ratio - most recent fiscal year'} |
||||
{'id': 136691, 'data_set_name': '可以使用:fnd17_alldelay1_apayratio2', 'description': '不可使用,仅供参考:Payout ratio - most recent fiscal year -1'} |
||||
{'id': 136726, 'data_set_name': '可以使用:fnd17_alldelay1_atotd2cap', 'description': '不可使用,仅供参考:Total debt/total capital - most recent fiscal year'} |
||||
{'id': 136731, 'data_set_name': '可以使用:fnd17_alldelay1_awcappspr', 'description': '不可使用,仅供参考:Working Capital per share/Price - most recent fiscal year'} |
||||
{'id': 136744, 'data_set_name': '可以使用:fnd17_alldelay1_debtcap_a', 'description': '不可使用,仅供参考:Debt divided by capitalization, LFY'} |
||||
{'id': 136745, 'data_set_name': '可以使用:fnd17_alldelay1_debtcap_i', 'description': '不可使用,仅供参考:Debt divided by capitalization, Interim'} |
||||
{'id': 136825, 'data_set_name': '可以使用:fnd17_alldelay1_qcapspps', 'description': '不可使用,仅供参考:Capital Spending per Share - Most Recent Quarter'} |
||||
{'id': 136830, 'data_set_name': '可以使用:fnd17_alldelay1_qcurratio', 'description': '不可使用,仅供参考:Current ratio - most recent quarter'} |
||||
{'id': 136831, 'data_set_name': '可以使用:fnd17_alldelay1_qcurratio2', 'description': '不可使用,仅供参考:Current ratio - most recent quarter, 1 year ago'} |
||||
{'id': 136841, 'data_set_name': '可以使用:fnd17_alldelay1_qltd2cap', 'description': '不可使用,仅供参考:LT debt/total capital - most recent quarter'} |
||||
{'id': 136843, 'data_set_name': '可以使用:fnd17_alldelay1_qpayratio', 'description': '不可使用,仅供参考:Payout ratio - most recent quarter'} |
||||
{'id': 136867, 'data_set_name': '可以使用:fnd17_alldelay1_qtotd2cap', 'description': '不可使用,仅供参考:Total debt/total capital - most recent quarter'} |
||||
{'id': 136873, 'data_set_name': '可以使用:fnd17_alldelay1_qwcappspr', 'description': '不可使用,仅供参考:Working Capital per share/Price - most recent quarter'} |
||||
{'id': 136929, 'data_set_name': '可以使用:fnd17_alldelay1_ttmcapspps', 'description': '不可使用,仅供参考:Capital Spending per share, trailing 12 months'} |
||||
{'id': 136972, 'data_set_name': '可以使用:fnd17_altd2cap', 'description': '不可使用,仅供参考:LT debt/total capital - most recent fiscal year'} |
||||
{'id': 136987, 'data_set_name': '可以使用:fnd17_apayratio', 'description': '不可使用,仅供参考:Payout ratio - most recent fiscal year'} |
||||
{'id': 136988, 'data_set_name': '可以使用:fnd17_apayratio2', 'description': '不可使用,仅供参考:Payout ratio - most recent fiscal year -1'} |
||||
{'id': 137023, 'data_set_name': '可以使用:fnd17_atotd2cap', 'description': '不可使用,仅供参考:Total debt/total capital - most recent fiscal year'} |
||||
{'id': 137028, 'data_set_name': '可以使用:fnd17_awcappspr', 'description': '不可使用,仅供参考:Working Capital per share/Price - most recent fiscal year'} |
||||
{'id': 137041, 'data_set_name': '可以使用:fnd17_debtcap_a', 'description': '不可使用,仅供参考:Debt divided by capitalization, LFY'} |
||||
{'id': 137042, 'data_set_name': '可以使用:fnd17_debtcap_i', 'description': '不可使用,仅供参考:Debt divided by capitalization, Interim'} |
||||
{'id': 137064, 'data_set_name': '可以使用:fnd17_float', 'description': '不可使用,仅供参考:Float'} |
||||
{'id': 137081, 'data_set_name': '可以使用:fnd17_mktcap', 'description': '不可使用,仅供参考:Market capitalization'} |
||||
{'id': 137131, 'data_set_name': '可以使用:fnd17_qcapspps', 'description': '不可使用,仅供参考:Capital Spending per Share. most recent quarter'} |
||||
{'id': 137136, 'data_set_name': '可以使用:fnd17_qcurratio', 'description': '不可使用,仅供参考:Current ratio - most recent quarter'} |
||||
{'id': 137137, 'data_set_name': '可以使用:fnd17_qcurratio2', 'description': '不可使用,仅供参考:Current ratio - most recent quarter, 1 year ago'} |
||||
{'id': 137147, 'data_set_name': '可以使用:fnd17_qltd2cap', 'description': '不可使用,仅供参考:LT debt/total capital - most recent quarter'} |
||||
{'id': 137149, 'data_set_name': '可以使用:fnd17_qpayratio', 'description': '不可使用,仅供参考:Payout ratio - most recent quarter'} |
||||
{'id': 137173, 'data_set_name': '可以使用:fnd17_qtotd2cap', 'description': '不可使用,仅供参考:Total debt/total capital - most recent quarter'} |
||||
{'id': 137179, 'data_set_name': '可以使用:fnd17_qwcappspr', 'description': '不可使用,仅供参考:Working Capital per share/Price - most recent quarter'} |
||||
{'id': 137214, 'data_set_name': '可以使用:fnd17_si2float1', 'description': '不可使用,仅供参考:Short Interest as % of float - most recent'} |
||||
{'id': 137215, 'data_set_name': '可以使用:fnd17_si2float2', 'description': '不可使用,仅供参考:Short Interest as % of float - 1 month ago'} |
||||
{'id': 137216, 'data_set_name': '可以使用:fnd17_si2float3', 'description': '不可使用,仅供参考:Short Interest as % of float - 2 months ago'} |
||||
{'id': 137217, 'data_set_name': '可以使用:fnd17_si2float4', 'description': '不可使用,仅供参考:Short Interest as % of float - 3 months ago'} |
||||
{'id': 137223, 'data_set_name': '可以使用:fnd17_siratio1', 'description': '不可使用,仅供参考:Short Interest Ratio - most recent'} |
||||
{'id': 137224, 'data_set_name': '可以使用:fnd17_siratio2', 'description': '不可使用,仅供参考:Short Interest Ratio - 1 month ago'} |
||||
{'id': 137225, 'data_set_name': '可以使用:fnd17_siratio3', 'description': '不可使用,仅供参考:Short Interest ratio - 2 months ago'} |
||||
{'id': 137226, 'data_set_name': '可以使用:fnd17_siratio4', 'description': '不可使用,仅供参考:Short Interest ratio - 3 months ago'} |
||||
{'id': 137264, 'data_set_name': '可以使用:fnd17_ttmcapspps', 'description': '不可使用,仅供参考:Capital Spending per share, trailing 12 month'} |
||||
{'id': 137307, 'data_set_name': '可以使用:interim_debt_to_capital_ratio', 'description': '不可使用,仅供参考:Ratio of total debt to total capital for the most recent interim period.'} |
||||
{'id': 137311, 'data_set_name': '可以使用:market_capitalization_current', 'description': "不可使用,仅供参考:The current total market value of a company's outstanding shares."} |
||||
{'id': 137312, 'data_set_name': '可以使用:market_capitalization_latest', 'description': "不可使用,仅供参考:The latest available total market value of a company's outstanding shares."} |
||||
{'id': 137313, 'data_set_name': '可以使用:market_capitalization_value_5', 'description': "不可使用,仅供参考:Total market value of a company's outstanding shares."} |
||||
{'id': 137341, 'data_set_name': '可以使用:public_float_shares', 'description': '不可使用,仅供参考:Number of shares available for public trading (float).'} |
||||
{'id': 137351, 'data_set_name': '可以使用:quarterly_debt_to_assets_ratio', 'description': '不可使用,仅供参考:Ratio of total debt to total assets for the most recent quarter.'} |
||||
{'id': 137352, 'data_set_name': '可以使用:quarterly_debt_to_equity_ratio_2', 'description': '不可使用,仅供参考:Ratio of total debt to total equity for the most recent quarter.'} |
||||
{'id': 137363, 'data_set_name': '可以使用:quarterly_return_on_equity_percent_3', 'description': '不可使用,仅供参考:Return on average equity for the most recent quarter (annualized).'} |
||||
{'id': 137391, 'data_set_name': '可以使用:return_on_average_assets_quarterly', 'description': '不可使用,仅供参考:Return on average assets for the most recent quarter, annualized.'} |
||||
{'id': 137468, 'data_set_name': '可以使用:annual_reserves_capital', 'description': '不可使用,仅供参考:Restricted Cash – Current not available for use immediately and due within a year'} |
||||
{'id': 137469, 'data_set_name': '可以使用:annual_share_premium_reserve', 'description': '不可使用,仅供参考:Preferred Stock – Non-Redeemable, Total.'} |
||||
{'id': 137479, 'data_set_name': '可以使用:capital_invested_accumulated_cost', 'description': '不可使用,仅供参考:Capital invested at accumulated cost for the interim period.'} |
||||
{'id': 137480, 'data_set_name': '可以使用:capitalized_interest_expense', 'description': '不可使用,仅供参考:[Quarterly] Interest Capitalized, Supplemental'} |
||||
{'id': 137482, 'data_set_name': '可以使用:cash_and_marketable_investments', 'description': '不可使用,仅供参考:Cash and marketable investment securities held at period end.'} |
||||
{'id': 137518, 'data_set_name': '可以使用:earnings_from_residual_operations', 'description': '不可使用,仅供参考:[Quarterly] Restructuring Charge'} |
||||
{'id': 137544, 'data_set_name': '可以使用:financing_loan_drawdown_net', 'description': '不可使用,仅供参考:If long-term debt issuances and reductions are not delineated separately, the total is classified as Long Term Debt, Net'} |
||||
{'id': 137545, 'data_set_name': '可以使用:financing_loan_drawdown_receipts', 'description': '不可使用,仅供参考:represents cash outflow on the repayment of long-term debt in a company. Long-term debt obligations may be repaid upon maturity or replaced with new debt.'} |
||||
{'id': 137663, 'data_set_name': '可以使用:fnd23_capex', 'description': '不可使用,仅供参考:capital expenditure'} |
||||
{'id': 138883, 'data_set_name': '可以使用:fully_paid_share_capital', 'description': '不可使用,仅供参考:Total value of fully paid share capital issued by the company.'} |
||||
{'id': 138896, 'data_set_name': '可以使用:income_from_discontinued_operations', 'description': '不可使用,仅供参考:[Quarterly] Interest Cost - Domestic'} |
||||
{'id': 138898, 'data_set_name': '可以使用:insurance_premiums_subscribed', 'description': '不可使用,仅供参考:[Quarterly] Prior Service Cost - Domestic'} |
||||
{'id': 138924, 'data_set_name': '可以使用:long_term_capital_commitments', 'description': '不可使用,仅供参考:[Quarterly] Current Portion of Long-Term Capital Leases, Supplemental'} |
||||
{'id': 138932, 'data_set_name': '可以使用:long_term_debt_premium_balance', 'description': '不可使用,仅供参考:Balance of premium on long-term debt.'} |
||||
{'id': 138965, 'data_set_name': '可以使用:net_premiums_written', 'description': '不可使用,仅供参考:Net premiums written during the period.'} |
||||
{'id': 139001, 'data_set_name': '可以使用:preferred_investment_capital', 'description': '不可使用,仅供参考:Preferred investment capital outstanding.'} |
||||
{'id': 139007, 'data_set_name': '可以使用:quarterly_cash_market_securities', 'description': '不可使用,仅供参考:Cash and marketable securities at quarter end.'} |
||||
{'id': 139016, 'data_set_name': '可以使用:quarterly_total_share_capital', 'description': '不可使用,仅供参考:Total share capital at the end of the quarter.'} |
||||
{'id': 139029, 'data_set_name': '可以使用:return_on_equity_ratio_3', 'description': "不可使用,仅供参考:Return on equity, calculated as net income divided by average shareholders' equity."} |
||||
{'id': 139030, 'data_set_name': '可以使用:revenue_from_domestic_operations', 'description': '不可使用,仅供参考:[Quarterly] Expected Rate of Return - Domestic'} |
||||
{'id': 139039, 'data_set_name': '可以使用:share_capital_extraordinary', 'description': '不可使用,仅供参考:Extraordinary share capital reported for the annual period.'} |
||||
{'id': 139040, 'data_set_name': '可以使用:share_capital_in_preference', 'description': '不可使用,仅供参考:Share capital in preference reported for the interim period.'} |
||||
{'id': 139041, 'data_set_name': '可以使用:share_capital_ordinary', 'description': '不可使用,仅供参考:Ordinary share capital reported for the annual period.'} |
||||
{'id': 139042, 'data_set_name': '可以使用:share_capital_ordinary_quarterly', 'description': '不可使用,仅供参考:Total value of ordinary share capital issued by the company for the quarter.'} |
||||
{'id': 139043, 'data_set_name': '可以使用:share_capital_subscribed', 'description': '不可使用,仅供参考:Share capital subscribed reported for the annual period.'} |
||||
{'id': 139044, 'data_set_name': '可以使用:share_capital_subscribed_2', 'description': '不可使用,仅供参考:Total value of subscribed share capital at the end of the period.'} |
||||
{'id': 139048, 'data_set_name': '可以使用:share_premium_reserve', 'description': '不可使用,仅供参考:Preferred Stock – Non-Redeemable, Total.'} |
||||
{'id': 139062, 'data_set_name': '可以使用:short_term_cash_market_securities', 'description': '不可使用,仅供参考:Short-term cash and marketable securities held.'} |
||||
{'id': 139117, 'data_set_name': '可以使用:special_premium_trust_monetary', 'description': '不可使用,仅供参考:Special premium trust monetary value for the interim period.'} |
||||
{'id': 139123, 'data_set_name': '可以使用:stockholders_other_paid_in_capital', 'description': '不可使用,仅供参考:Other paid-in capital attributable to stockholders.'} |
||||
{'id': 139152, 'data_set_name': '可以使用:valuation_market_adjustment_amount', 'description': '不可使用,仅供参考:Amount of market adjustment made to valuation.'} |
||||
{'id': 140109, 'data_set_name': '可以使用:fnd31_capexdeplink', 'description': '不可使用,仅供参考:Capital Expenditures to Depreciation Linkage. It is defined as the absolute value of the difference between ranked (1-1000) quarterly capital expenditures to assets and ranked (1-1000) quarterly depreciation to assets.'} |
||||
{'id': 140198, 'data_set_name': '可以使用:fnd44_working_capital_accruals', 'description': '不可使用,仅供参考:Working Capital Accruals'} |
||||
{'id': 140223, 'data_set_name': '可以使用:fnd6_newqint_capsq', 'description': '不可使用,仅供参考:Capital Surplus/Share Premium Reserve'} |
||||
{'id': 140262, 'data_set_name': '可以使用:fnd6_newqus_capsq', 'description': '不可使用,仅供参考:Capital Surplus/Share Premium Reserve'} |
||||
{'id': 140294, 'data_set_name': '可以使用:fnd6_newqus_icaptq', 'description': '不可使用,仅供参考:Invested Capital - Total - Quarterly'} |
||||
{'id': 140346, 'data_set_name': '可以使用:fnd6_newqus_wcapq', 'description': '不可使用,仅供参考:Working Capital (Balance Sheet)'} |
||||
{'id': 140421, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_add_paid_in_cap', 'description': '不可使用,仅供参考:Additional Paid in Capital'} |
||||
{'id': 140424, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_capital_reserve', 'description': '不可使用,仅供参考:Includes accumulative comprehensive income reserve, merger reserve, as well as all other reserves and equities not included in retained earnings'} |
||||
{'id': 140460, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_sh_cap_and_apic', 'description': '不可使用,仅供参考:Share Capital & APIC'} |
||||
{'id': 140469, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_total_capital_leases', 'description': '不可使用,仅供参考:Total of short- and long-term amounts payable by a lessee under capital leases'} |
||||
{'id': 140478, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_lt_capital_lease_obligations', 'description': '不可使用,仅供参考:Noncurrent amount payable by a lessee under capital leases'} |
||||
{'id': 140480, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_st_capital_lease_obligations', 'description': '不可使用,仅供参考:Amount payable within 1 year by a lessee under capital leases'} |
||||
{'id': 140488, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_add_paid_in_cap', 'description': '不可使用,仅供参考:Additional Paid in Capital'} |
||||
{'id': 140491, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_capital_reserve', 'description': '不可使用,仅供参考:Includes Accumulative Comprehensive Income Reserve, Merger Reserve, as Well as All Other Reserves and Equities Not Included in Retained Earnings'} |
||||
{'id': 140523, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_sh_cap_and_apic', 'description': '不可使用,仅供参考:Share Capital & APIC'} |
||||
{'id': 140532, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_total_capital_leases', 'description': '不可使用,仅供参考:Total of short- and long-term amounts payable by a lessee under capital leases'} |
||||
{'id': 140541, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_lt_capital_lease_obligations', 'description': '不可使用,仅供参考:Noncurrent amount payable by a lessee under capital leases'} |
||||
{'id': 140546, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_cap_expend_prpty_add', 'description': '不可使用,仅供参考:Capital Expenditures/Property Additions'} |
||||
{'id': 140552, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_chng_non_cash_work_cap', 'description': '不可使用,仅供参考:Changes in Non-Cash Working Capital'} |
||||
{'id': 140553, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_decr_cap_stock', 'description': '不可使用,仅供参考:Always negative. Repurchase of common stock, common stock warrants, or other common stock equivalents. Includes redemption of preferred share capital. Includes purchase of treasury stock. Index'} |
||||
{'id': 140559, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_incr_cap_stock', 'description': '不可使用,仅供参考:Increase in Capital Stocks'} |
||||
{'id': 140578, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_cap_expend_prpty_add', 'description': '不可使用,仅供参考:Capital Expenditures/Property Additions'} |
||||
{'id': 140584, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_chng_non_cash_work_cap', 'description': '不可使用,仅供参考:Changes in Non-Cash Working Capital'} |
||||
{'id': 140585, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_decr_cap_stock', 'description': '不可使用,仅供参考:Always negative. Repurchase of common stock, common stock warrants, or other common stock equivalents. Includes redemption of preferred share capital. Includes purchase of treasury stock. Index'} |
||||
{'id': 140591, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_incr_cap_stock', 'description': '不可使用,仅供参考:Increase in Capital Stocks'} |
||||
{'id': 140631, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_expected_return_pension', 'description': '不可使用,仅供参考:The component of net pension expense that pertains to the expected return on pension plan assets'} |
||||
{'id': 140632, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_expected_return_plan_assets', 'description': '不可使用,仅供参考:The estimated expected long-term rate of return on pension plan assets expressed as a percent'} |
||||
{'id': 140663, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_trading_acct_prof', 'description': '不可使用,仅供参考:Trading Acct'} |
||||
{'id': 140753, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_sh_cap_and_apic', 'description': '不可使用,仅供参考:Share Capital & APIC'} |
||||
{'id': 140768, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_2_bs_add_paid_in_cap', 'description': '不可使用,仅供参考:Additional Paid in Capital'} |
||||
{'id': 140770, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_2_bs_capital_reserve', 'description': '不可使用,仅供参考:Includes accumulative comprehensive income reserve, merger reserve, as well as all other reserves and equities not included in retained earnings'} |
||||
{'id': 140771, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_2_bs_capital_stock', 'description': '不可使用,仅供参考:Capital Stock: When a company issues shares with par value for cash, a part of the proceeds are recorded as capital stock'} |
||||
{'id': 140796, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_add_paid_in_cap', 'description': '不可使用,仅供参考:Additional Paid in Capital'} |
||||
{'id': 140799, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_capital_reserve', 'description': '不可使用,仅供参考:Includes accumulative comprehensive income reserve, merger reserve, as well as all other reserves and equities not included in Retained Earnings'} |
||||
{'id': 140800, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_capital_stock', 'description': '不可使用,仅供参考:Capital Stock: When a company issues shares with par value for cash, a part of the proceeds are recorded as capital stock'} |
||||
{'id': 140827, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_sh_cap_and_apic', 'description': '不可使用,仅供参考:Share Capital & APIC'} |
||||
{'id': 140847, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_cap_expend_prpty_add', 'description': '不可使用,仅供参考:Capital Expenditures/Property Additions'} |
||||
{'id': 140853, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_chng_non_cash_work_cap', 'description': '不可使用,仅供参考:Changes in Non-Cash Working Capital'} |
||||
{'id': 140854, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_decr_cap_stock', 'description': '不可使用,仅供参考:Always negative. Repurchase of common stock, common stock warrants, or other common stock equivalents. Includes redemption of preferred share capital. Includes purchase of treasury stock. Index'} |
||||
{'id': 140859, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_incr_cap_stock', 'description': '不可使用,仅供参考:Increase in Capital Stocks'} |
||||
{'id': 140927, 'data_set_name': '可以使用:fnd86_insider_trading_score', 'description': '不可使用,仅供参考:Insider trading score'} |
||||
{'id': 140937, 'data_set_name': '可以使用:srp_insider_trading_score', 'description': '不可使用,仅供参考:Insider trading score'} |
||||
{'id': 373181, 'data_set_name': '可以使用:cap', 'description': '不可使用,仅供参考:Daily market capitalization (in millions)'} |
||||
{'id': 373192, 'data_set_name': '可以使用:market', 'description': '不可使用,仅供参考:Market grouping'} |
||||
{'id': 373194, 'data_set_name': '可以使用:returns', 'description': '不可使用,仅供参考:Daily returns'} |
||||
{'id': 373201, 'data_set_name': '可以使用:volume', 'description': '不可使用,仅供参考:Daily volume'} |
||||
{'id': 373297, 'data_set_name': '可以使用:pv173_ranksbondreturn20deqwt', 'description': '不可使用,仅供参考:It is defined as the equally weighted single bond return over last 20 days with the filter of the bonds that mature between 3 years and 7 years.'} |
||||
{'id': 373323, 'data_set_name': '可以使用:pv173_rawratiosatlas_unit_name', 'description': '不可使用,仅供参考:Atlas unit name'} |
||||
{'id': 373324, 'data_set_name': '可以使用:pv173_rawratiosbondreturn20deqwt', 'description': '不可使用,仅供参考:It is defined as the equally weighted single bond return over last 20 days with the filter of the bonds that mature between 3 years and 7 years.'} |
||||
{'id': 373325, 'data_set_name': '可以使用:pv173_rawratioseqbddivrgbdrtn120d', 'description': '不可使用,仅供参考:It is defined as the difference of market equity return and estimated equity return based on 120-day linear regression of equity return on aggregated bond return and MSCI ACWIIndex return.'} |
||||
{'id': 373326, 'data_set_name': '可以使用:pv173_rawratioseqbddivrgsprd5y120d', 'description': '不可使用,仅供参考:It is defined as the difference of market equity return and estimated equity return based on 120-day linear regression of equity return on 5-year mid z-spread percentage change and MSCI ACWIIndex return.'} |
||||
{'id': 373327, 'data_set_name': '可以使用:pv173_rawratioseqbddivrgsprd5y120dsbst', 'description': '不可使用,仅供参考:It is defined as the difference of market equity return and estimated equity return based on 120-day linear regression of equity return on 5-year mid z-spread percentage change and MSCI ACWIIndex return'} |
||||
{'id': 373328, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspread', 'description': '不可使用,仅供参考:It is defined as the region relative 5-year mid z-spreadIn the bond z-spread curve.'} |
||||
{'id': 373329, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadchg60d', 'description': '不可使用,仅供参考:It is defined as the 60-day change of 5-year mid z-spreadIn the bond z-spread curve.'} |
||||
{'id': 373330, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadchg60dsbst', 'description': '不可使用,仅供参考:It is defined as the 60-day change of 5-year mid z-spreadIn the bond z-spread curve'} |
||||
{'id': 373331, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadchgstd20d', 'description': '不可使用,仅供参考:It is defined as the 20-day standard deviation of changeIn 5-year mid z-spreadIn the bond z-spread curve.'} |
||||
{'id': 373332, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadchgstd20dsbst', 'description': '不可使用,仅供参考:It is defined as the 20-day standard deviation of changeIn 5-year mid z-spreadIn the bond z-spread curve'} |
||||
{'id': 373333, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadindrel', 'description': '不可使用,仅供参考:It is defined as theIndustry relative 5-year mid z-spreadIn the bond z-spread curve.'} |
||||
{'id': 373334, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadindrelsbst', 'description': '不可使用,仅供参考:It is defined as theIndustry relative 5-year mid z-spreadIn the bond z-spread curve'} |
||||
{'id': 373335, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadsbst', 'description': '不可使用,仅供参考:It is defined as the region relative 5-year mid z-spreadIn the bond z-spread curve'} |
||||
{'id': 373336, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadzscore60d', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve.'} |
||||
{'id': 373337, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadzscore60dsbst', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve'} |
||||
{'id': 373338, 'data_set_name': '可以使用:pv173_rawratiosreltermspreadtransfema120d', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of transformed relative term spread.'} |
||||
{'id': 373339, 'data_set_name': '可以使用:pv173_rawratiosreltermspreadtransfema120dsbst', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of transformed relative term spread'} |
||||
{'id': 373340, 'data_set_name': '可以使用:pv173_rawratiosshift60dchg60d', 'description': '不可使用,仅供参考:It is defined as the 60-day change of ShiftIn Bond Z-spread Curve.'} |
||||
{'id': 373341, 'data_set_name': '可以使用:pv173_rawratiosshift60dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the first principal component for the z-spread between 6 months and 40 years'} |
||||
{'id': 373342, 'data_set_name': '可以使用:pv173_rawratiosshift60dstd60d', 'description': '不可使用,仅供参考:It is defined as the 60-day standard deviation of ShiftIn Bond Z-spread Curve.'} |
||||
{'id': 373343, 'data_set_name': '可以使用:pv173_rawratiosshift60dzscore60d', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of ShiftIn Bond Z-spread Curve.'} |
||||
{'id': 373344, 'data_set_name': '可以使用:pv173_rawratiostilt5y1ydiff60dema60d', 'description': '不可使用,仅供参考:It is defined as the 60-day exponential average of the difference of factor loading of the second principal component for the 5-year z-spread and 1-year z-spread'} |
||||
{'id': 373345, 'data_set_name': '可以使用:pv173_rawratiostilt60dchg60d', 'description': '不可使用,仅供参考:It is defined as the 60-day change of TiltIn Bond Z-spread Curve.'} |
||||
{'id': 373346, 'data_set_name': '可以使用:pv173_rawratiostilt60dema60d', 'description': '不可使用,仅供参考:It is defined as the 60-day exponential average of average factor loading of the second principal component for the z-spread between 6 months and 40 years'} |
||||
{'id': 373347, 'data_set_name': '可以使用:pv173_rawratiostilt6m1yavg60dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the second principal component for the 6-month and 1-year z-spread'} |
||||
{'id': 373348, 'data_set_name': '可以使用:pv173_rawratiostwist120dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the third principal component for the z-spread between 6 months and 40 years'} |
||||
{'id': 373349, 'data_set_name': '可以使用:pv173_rawratiostwist6m1yavg120dema120d', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of average factor loading of the third principal component for the 6-month and 1-year z-spread'} |
||||
{'id': 373351, 'data_set_name': '可以使用:pv173_zscoresbondreturn20deqwt', 'description': '不可使用,仅供参考:It is defined as the equally weighted single bond return over last 20 days with the filter of the bonds that mature between 3 years and 7 years.'} |
||||
{'id': 373547, 'data_set_name': '可以使用:pv37_cap_13', 'description': '不可使用,仅供参考:Market cap'} |
||||
{'id': 373548, 'data_set_name': '可以使用:pv37_cap_14', 'description': '不可使用,仅供参考:14-Day Market Capitalization'} |
||||
{'id': 373549, 'data_set_name': '可以使用:pv37_cap_15', 'description': '不可使用,仅供参考:Market cap'} |
||||
{'id': 373550, 'data_set_name': '可以使用:pv37_cap_global', 'description': '不可使用,仅供参考:Global Market Capitalization'} |
||||
{'id': 373551, 'data_set_name': '可以使用:pv37_cap_global1h', 'description': '不可使用,仅供参考:Global Market Capitalization 1H'} |
||||
{'id': 373552, 'data_set_name': '可以使用:pv37_cap_global2', 'description': '不可使用,仅供参考:Global Market Capitalization 2'} |
||||
{'id': 373553, 'data_set_name': '可以使用:pv37_cap_global2h', 'description': '不可使用,仅供参考:Global Market Capitalization 2H.'} |
||||
{'id': 373554, 'data_set_name': '可以使用:pv37_cap_global30m', 'description': '不可使用,仅供参考:Global Market Capitalization 30M'} |
||||
{'id': 373555, 'data_set_name': '可以使用:pv37_cap_global3h', 'description': '不可使用,仅供参考:Global Market Capitalization 3H'} |
||||
{'id': 373556, 'data_set_name': '可以使用:pv37_cap_global4h', 'description': '不可使用,仅供参考:Global Market Capitalization 4H'} |
||||
{'id': 373607, 'data_set_name': '可以使用:pv37_volume_13', 'description': '不可使用,仅供参考:Trading volume'} |
||||
{'id': 373608, 'data_set_name': '可以使用:pv37_volume_14', 'description': '不可使用,仅供参考:14-Day Trading Volume'} |
||||
{'id': 373609, 'data_set_name': '可以使用:pv37_volume_15', 'description': '不可使用,仅供参考:Trading volume'} |
||||
{'id': 373610, 'data_set_name': '可以使用:pv37_volume_global', 'description': '不可使用,仅供参考:Global Trading Volume'} |
||||
{'id': 373611, 'data_set_name': '可以使用:pv37_volume_global1h', 'description': '不可使用,仅供参考:Global Trading Volume 1H'} |
||||
{'id': 373612, 'data_set_name': '可以使用:pv37_volume_global2', 'description': '不可使用,仅供参考:Global Trading Volume 2'} |
||||
{'id': 373613, 'data_set_name': '可以使用:pv37_volume_global2h', 'description': '不可使用,仅供参考:Global Trading Volume 2H'} |
||||
{'id': 373614, 'data_set_name': '可以使用:pv37_volume_global30m', 'description': '不可使用,仅供参考:Global Trading Volume 30M'} |
||||
{'id': 373615, 'data_set_name': '可以使用:pv37_volume_global3h', 'description': '不可使用,仅供参考:Global Trading Volume 3H'} |
||||
{'id': 373616, 'data_set_name': '可以使用:pv37_volume_global4h', 'description': '不可使用,仅供参考:Global Trading Volume 4H'} |
||||
{'id': 373640, 'data_set_name': '可以使用:pv72_ibeseps2_score_float', 'description': '不可使用,仅供参考:pv_score'} |
||||
{'id': 373643, 'data_set_name': '可以使用:pv72_ibeseps_score_float', 'description': '不可使用,仅供参考:pv_score'} |
||||
{'id': 373646, 'data_set_name': '可以使用:pv72_ibesptg2_score_float', 'description': '不可使用,仅供参考:pv_score'} |
||||
{'id': 373649, 'data_set_name': '可以使用:pv72_ibesptg_score_float', 'description': '不可使用,仅供参考:pv_score'} |
||||
{'id': 373652, 'data_set_name': '可以使用:pv72_ibesrec2_score_float', 'description': '不可使用,仅供参考:pv_score'} |
||||
{'id': 373655, 'data_set_name': '可以使用:pv72_news_score_float', 'description': '不可使用,仅供参考:pv_score'} |
||||
{'id': 373658, 'data_set_name': '可以使用:pv72_pv_score_float', 'description': '不可使用,仅供参考:pv_score'} |
||||
{'id': 373671, 'data_set_name': '可以使用:pv96_eqy_split_ratio', 'description': '不可使用,仅供参考:This number describes the rate at which the company will be dividing their current shares outstanding'} |
||||
========================= 数据字段结束 ======================================= |
||||
|
||||
以上数据字段和操作符, 按照Description说明组合, 但是每一个 alpha 组合的使用的数据字段和操作符不要过于集中, 在符合语法的情况下, 多尝试不同的组合 |
||||
|
||||
你再检查一下, 如果你使用了 |
||||
Operator abs does not support event inputs |
||||
Operator ts_mean does not support event inputs |
||||
Operator ts_scale does not support event inputs |
||||
Operator add does not support event inputs |
||||
Operator sign does not support event inputs |
||||
Operator greater does not support event inputs |
||||
Operator ts_av_diff does not support event inputs |
||||
Operator ts_quantile does not support event inputs |
||||
Operator ts_arg_min does not support event inputs |
||||
Operator divide does not support event inputs |
||||
Operator ts_corr does not support event inputs |
||||
Operator ts_decay_linear does not support event inputs |
||||
Operator ts_sum does not support event inputs |
||||
Operator ts_delay does not support event inputs |
||||
Operator ts_arg_max does not support event inputs |
||||
Operator ts_std_dev does not support event inputs |
||||
Operator ts_regression does not support event inputs |
||||
Operator ts_backfill does not support event inputs |
||||
Operator signed_power does not support event inputs |
||||
Operator ts_product does not support event inputs |
||||
Operator ts_zscore does not support event inputs |
||||
Operator group_rank does not support event inputs |
||||
Operator subtract does not support event inputs |
||||
Operator ts_delta does not support event inputs |
||||
Operator ts_rank does not support event inputs |
||||
Operator ts_count_nans does not support event inputs |
||||
Operator ts_covariance does not support event inputs |
||||
Operator multiply does not support event inputs |
||||
Operator if_else does not support event inputs |
||||
Operator group_neutralize does not support event inputs |
||||
Operator group_zscore does not support event inputs |
||||
Operator winsorize does not support event inputs |
||||
注意, 以上操作符不能使用事件类型的数据集, 以上操作符禁止使用事件类型的数据集!! |
||||
|
||||
输出只要语法正确的WebSim表达式, 不需要任何解释 |
||||
@ -0,0 +1,371 @@ |
||||
尾部风险溢价因子 |
||||
假设 |
||||
在市场极端下跌期间，投资者往往因恐慌情绪而过度抛售高尾部风险资产，导致这些资产价格被暂时低估；而在市场恢复平稳后，此类资产会因风险补偿机制获得超额收益。因此，过去经历显著尾部损失的股票在未来短期内可能具备正向预期收益。 |
||||
实施方案 |
||||
基于个股日收益率序列，滚动计算其在过去N个交易日（如60日）中低于VaR阈值（如5%分位数）的极端负收益的平均值或条件VaR（Expected Shortfall），构建尾部风险指标；对全市场股票按该指标升序排序，做多尾部风险最高（即历史极端损失最严重）的十分之一组合，做空最低的十分之一组合，形成多空因子收益序列。 |
||||
Tail Risk Premium Factor |
||||
Hypothesis |
||||
During periods of extreme market downturns, investors often panic and excessively sell assets with high tail risk, temporarily depressing their prices. As market conditions normalize, these assets tend to earn excess returns as compensation for bearing tail risk. Consequently, stocks that have experienced significant tail losses in the recent past may exhibit positive expected returns over the short term. |
||||
Implementation |
||||
Using daily stock return series, compute a rolling tail risk metric—such as the average of returns below the 5th percentile VaR threshold or the Expected Shortfall—over the past N trading days (e.g., 60 days). Rank all tradable stocks by this metric in ascending order, construct a long-short portfolio by going long the top decile (highest historical tail losses) and short the bottom decile, and record the resulting factor return series. |
||||
|
||||
*=========================================================================================* |
||||
输出格式： |
||||
输出必须是且仅是纯文本。 |
||||
每一行是一个完整、独立、语法正确的WebSim表达式。 |
||||
严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。 |
||||
===================== !!! 重点(输出方式) !!! ===================== |
||||
现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。 |
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 |
||||
**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不需要赋值, 不要解释, 不需要序号, 也不要输出多余的东西)： |
||||
表达式 |
||||
表达式 |
||||
表达式 |
||||
... |
||||
表达式 |
||||
================================================================= |
||||
重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。 |
||||
以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 50 个 alpha： |
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合 |
||||
!! 数据集使用要求尽量分散, 不要重复使用 |
||||
!! 数据集使用要求尽量分散, 不要重复使用 |
||||
================================================================= |
||||
**操作符汇总 |
||||
**算术运算符 (Arithmetic): |
||||
abs(x) - 绝对值 |
||||
add(x, y, filter=false) - 加法 (x + y) |
||||
densify(x) - 分组字段稠密化 |
||||
divide(x, y) - 除法 (x / y) |
||||
inverse(x) - 倒数 (1/x) |
||||
log(x) - 自然对数 |
||||
max(x, y, ..) - 最大值 |
||||
min(x, y, ..) - 最小值 |
||||
multiply(x, y, filter=false) - 乘法 (x * y) |
||||
power(x, y) - 幂运算 (x^y) |
||||
reverse(x) - 取反 (-x) |
||||
sign(x) - 符号函数 |
||||
signed_power(x, y) - 保留符号的幂运算 |
||||
sqrt(x) - 平方根 |
||||
subtract(x, y, filter=false) - 减法 (x - y) |
||||
to_nan(x, value=0, reverse=false) - 值与NaN转换 |
||||
**逻辑运算符 (Logical): |
||||
and(input1, input2) - 逻辑与 |
||||
if_else(input1, input2, input3) - 条件判断 |
||||
input1 < input2 - 小于比较 |
||||
input1 <= input2 - 小于等于 |
||||
input1 == input2 - 等于比较 |
||||
input1 > input2 - 大于比较 |
||||
input1 >= input2 - 大于等于 |
||||
input1 != input2 - 不等于 |
||||
is_nan(input) - 是否为NaN |
||||
not(x) - 逻辑非 |
||||
or(input1, input2) - 逻辑或 |
||||
**时间序列运算符 (Time Series): |
||||
days_from_last_change(x) - 上次变化天数 |
||||
hump(x, hump=0.01) - 限制变化幅度 |
||||
jump_decay(x, d, sensitivity=0.5, force=0.1) - 跳跃衰减 |
||||
kth_element(x, d, k) - 第K个值 |
||||
last_diff_value(x, d) - 最后一个不同值 |
||||
ts_arg_max(x, d) - 最大值相对索引 |
||||
ts_arg_min(x, d) - 最小值相对索引 |
||||
ts_av_diff(x, d) - 与均值的差 |
||||
ts_backfill(x, lookback=d, k=1, ignore="NAN") - 回填 |
||||
ts_corr(x, y, d) - 时间序列相关性 |
||||
ts_count_nans(x, d) - NaN计数 |
||||
ts_covariance(y, x, d) - 协方差 |
||||
ts_decay_linear(x, d, dense=false) - 线性衰减 |
||||
ts_delay(x, d) - 延迟值 |
||||
ts_delta(x, d) - 差值 (x - 延迟值) |
||||
ts_max(x, d) - 时间序列最大值 |
||||
ts_mean(x, d) - 时间序列均值 |
||||
ts_min(x, d) - 时间序列最小值 |
||||
ts_product(x, d) - 时间序列乘积 |
||||
ts_quantile(x, d, driver="gaussian") - 分位数 |
||||
ts_rank(x, d, constant=0) - 时间序列排名 |
||||
ts_regression(y, x, d, lag=0, rettype=0) - 回归分析 |
||||
ts_scale(x, d, constant=0) - 时间序列缩放 |
||||
ts_std_dev(x, d) - 时间序列标准差 |
||||
ts_step(1) - 天数计数器 |
||||
ts_sum(x, d) - 时间序列求和 |
||||
ts_target_tvr_decay(x, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率衰减 |
||||
ts_target_tvr_delta_limit(x, y, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率差值限制 |
||||
ts_zscore(x, d) - 时间序列Z分数 |
||||
**横截面运算符 (Cross Sectional): |
||||
normalize(x, useStd=false, limit=0.0) - 标准化 |
||||
quantile(x, driver=gaussian, sigma=1.0) - 分位数转换 |
||||
rank(x, rate=2) - 排名 |
||||
scale(x, scale=1, longscale=1, shortscale=1) - 缩放 |
||||
scale_down(x, constant=0) - 按比例缩放 |
||||
vector_neut(x, y) - 向量中性化 |
||||
winsorize(x, std=4) - 缩尾处理 |
||||
zscore(x) - Z分数 |
||||
**向量运算符 (Vector): |
||||
vec_avg(x) - 向量均值 |
||||
vec_max(x) - 向量最大值 |
||||
vec_min(x) - 向量最小值 |
||||
vec_sum(x) - 向量求和 |
||||
**变换运算符 (Transformational): |
||||
bucket(rank(x), range="0,1,0.1" or buckets="2,5,6,7,10") - 分桶 |
||||
generate_stats(alpha) - 生成统计量 |
||||
trade_when(x, y, z) - 条件交易 |
||||
**分组运算符 (Group): |
||||
combo_a(alpha, nlength=250, mode='algo1') - 组合Alpha |
||||
group_backfill(x, group, d, std=4.0) - 分组回填 |
||||
group_cartesian_product(g1, g2) - 笛卡尔积分组 |
||||
group_max(x, group) - 分组最大值 |
||||
group_mean(x, weight, group) - 分组均值 |
||||
group_min(x, group) - 分组最小值 |
||||
group_neutralize(x, group) - 分组中性化 |
||||
group_rank(x, group) - 分组排名 |
||||
group_scale(x, group) - 分组缩放 |
||||
group_zscore(x, group) - 分组Z分数 |
||||
**特殊运算符 (Special): |
||||
in - 包含判断 |
||||
self_corr(input) - 自相关性 |
||||
universe_size - 宇宙大小 |
||||
**归约运算符 (Reduce): |
||||
reduce_avg(input, threshold=0) - 平均值归约 |
||||
reduce_choose(input, nth, ignoreNan=true) - 选择归约 |
||||
reduce_count(input, threshold) - 计数归约 |
||||
reduce_ir(input) - IR归约 |
||||
reduce_kurtosis(input) - 峰度归约 |
||||
reduce_max(input) - 最大值归约 |
||||
reduce_min(input) - 最小值归约 |
||||
reduce_norm(input) - 范数归约 |
||||
reduce_percentage(input, percentage=0.5) - 百分比归约 |
||||
reduce_powersum(input, constant=2, precise=false) - 幂和归约 |
||||
reduce_range(input) - 范围归约 |
||||
reduce_skewness(input) - 偏度归约 |
||||
reduce_stddev(input, threshold=0) - 标准差归约 |
||||
reduce_sum(input) - 求和归约 |
||||
|
||||
以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子 |
||||
|
||||
========================= 操作符开始 ======================================= |
||||
注意: Operator: 后面的是操作符(是可以使用的), |
||||
Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符 |
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特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x) |
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Description: Absolute value of x |
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Operator: add(x, y, filter = false) |
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Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding |
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Operator: densify(x) |
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Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient |
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Operator: divide(x, y) |
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Description: x / y |
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Operator: inverse(x) |
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Description: 1 / x |
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Operator: log(x) |
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Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights. |
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Operator: max(x, y, ..) |
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Description: Maximum value of all inputs. At least 2 inputs are required |
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Operator: min(x, y ..) |
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Description: Minimum value of all inputs. At least 2 inputs are required |
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Operator: multiply(x ,y, ... , filter=false) |
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Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1 |
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Operator: power(x, y) |
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Description: x ^ y |
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Operator: reverse(x) |
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Description: - x |
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Operator: sign(x) |
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Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN; |
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Operator: signed_power(x, y) |
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Description: x raised to the power of y such that final result preserves sign of x |
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Operator: sqrt(x) |
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Description: Square root of x |
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Operator: subtract(x, y, filter=false) |
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Description: x-y. If filter = true, filter all input NaN to 0 before subtracting |
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Operator: and(input1, input2) |
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Description: Logical AND operator, returns true if both operands are true and returns false otherwise |
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Operator: if_else(input1, input2, input 3) |
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Description: If input1 is true then return input2 else return input3. |
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Operator: input1 < input2 |
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Description: If input1 < input2 return true, else return false |
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Operator: input1 <= input2 |
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Description: Returns true if input1 <= input2, return false otherwise |
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Operator: input1 == input2 |
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Description: Returns true if both inputs are same and returns false otherwise |
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Operator: input1 > input2 |
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Description: Logic comparison operators to compares two inputs |
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Operator: input1 >= input2 |
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Description: Returns true if input1 >= input2, return false otherwise |
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Operator: input1!= input2 |
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Description: Returns true if both inputs are NOT the same and returns false otherwise |
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Operator: is_nan(input) |
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Description: If (input == NaN) return 1 else return 0 |
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Operator: not(x) |
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Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1). |
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Operator: or(input1, input2) |
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Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise |
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Operator: days_from_last_change(x) |
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Description: Amount of days since last change of x |
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Operator: hump(x, hump = 0.01) |
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Description: Limits amount and magnitude of changes in input (thus reducing turnover) |
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Operator: kth_element(x, d, k) |
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Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1 |
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Operator: last_diff_value(x, d) |
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Description: Returns last x value not equal to current x value from last d days |
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Operator: ts_arg_max(x, d) |
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Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1 |
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Operator: ts_arg_min(x, d) |
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Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1. |
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Operator: ts_av_diff(x, d) |
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Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation |
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Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN") |
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Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value) |
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Operator: ts_corr(x, y, d) |
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Description: Returns correlation of x and y for the past d days |
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Operator: ts_count_nans(x ,d) |
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Description: Returns the number of NaN values in x for the past d days |
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Operator: ts_covariance(y, x, d) |
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Description: Returns covariance of y and x for the past d days |
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Operator: ts_decay_linear(x, d, dense = false) |
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Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not. |
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Operator: ts_delay(x, d) |
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Description: Returns x value d days ago |
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Operator: ts_delta(x, d) |
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Description: Returns x - ts_delay(x, d) |
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Operator: ts_mean(x, d) |
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Description: Returns average value of x for the past d days. |
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Operator: ts_product(x, d) |
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Description: Returns product of x for the past d days |
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Operator: ts_quantile(x,d, driver="gaussian" ) |
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Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default. |
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Operator: ts_rank(x, d, constant = 0) |
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Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0. |
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Operator: ts_regression(y, x, d, lag = 0, rettype = 0) |
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Description: Returns various parameters related to regression function |
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Operator: ts_scale(x, d, constant = 0) |
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Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space |
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Operator: ts_std_dev(x, d) |
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Description: Returns standard deviation of x for the past d days |
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Operator: ts_step(1) |
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Description: Returns days' counter |
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Operator: ts_sum(x, d) |
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Description: Sum values of x for the past d days. |
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Operator: ts_zscore(x, d) |
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Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown. |
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Operator: normalize(x, useStd = false, limit = 0.0) |
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Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element |
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Operator: quantile(x, driver = gaussian, sigma = 1.0) |
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Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector |
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Operator: rank(x, rate=2) |
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Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0 |
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Operator: scale(x, scale=1, longscale=1, shortscale=1) |
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Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator |
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Operator: winsorize(x, std=4) |
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Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std. |
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Operator: zscore(x) |
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Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean |
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Operator: vec_avg(x) |
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Description: Taking mean of the vector field x |
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Operator: vec_sum(x) |
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Description: Sum of vector field x |
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Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10") |
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Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input |
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Operator: trade_when(x, y, z) |
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Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition |
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Operator: group_backfill(x, group, d, std = 4.0) |
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Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days |
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Operator: group_mean(x, weight, group) |
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Description: All elements in group equals to the mean |
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Operator: group_neutralize(x, group) |
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Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant |
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Operator: group_rank(x, group) |
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Description: Each elements in a group is assigned the corresponding rank in this group |
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Operator: group_scale(x, group) |
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Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin) |
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Operator: group_zscore(x, group) |
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Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group. |
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========================= 操作符结束 ======================================= |
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|
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========================= 数据字段开始 ======================================= |
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注意: data_set_name: 后面的是数据字段(可以使用), description: 此字段后面的是数据字段对应的描述或使用说明(不能使用) |
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|
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{'id': 62154, 'data_set_name': '可以使用:quarterly_return_on_assets_percent', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter, as a percentage.'} |
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{'id': 62155, 'data_set_name': '可以使用:quarterly_return_on_assets_percent_2', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter, as a percentage.'} |
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{'id': 62156, 'data_set_name': '可以使用:quarterly_return_on_average_assets', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter.'} |
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{'id': 62157, 'data_set_name': '可以使用:quarterly_return_on_average_equity', 'description': '不可使用,仅供参考:Annualized net income divided by average equity for the quarter.'} |
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{'id': 62158, 'data_set_name': '可以使用:quarterly_return_on_equity_percent', 'description': '不可使用,仅供参考:Annualized net income divided by average equity for the quarter, as a percentage.'} |
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{'id': 62159, 'data_set_name': '可以使用:quarterly_return_on_equity_percent_2', 'description': '不可使用,仅供参考:Annualized net income divided by average equity for the quarter, as a percentage.'} |
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{'id': 62160, 'data_set_name': '可以使用:quarterly_return_on_investment', 'description': '不可使用,仅供参考:Annualized net income divided by average investment for the quarter.'} |
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{'id': 62161, 'data_set_name': '可以使用:quarterly_return_on_investment_percent', 'description': '不可使用,仅供参考:Annualized net income divided by average investment for the quarter.'} |
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{'id': 62162, 'data_set_name': '可以使用:quarterly_return_on_investment_percent_2', 'description': '不可使用,仅供参考:Annualized net income divided by average investment for the quarter, as a percentage.'} |
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{'id': 62226, 'data_set_name': '可以使用:ttm_return_on_average_equity', 'description': '不可使用,仅供参考:Net income divided by average equity for the trailing twelve months.'} |
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{'id': 62227, 'data_set_name': '可以使用:ttm_return_on_equity_percent', 'description': '不可使用,仅供参考:Net income divided by average equity for trailing twelve months, as a percentage.'} |
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{'id': 62228, 'data_set_name': '可以使用:ttm_return_on_equity_percent_2', 'description': '不可使用,仅供参考:Net income divided by average equity for trailing twelve months, as a percentage.'} |
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{'id': 62410, 'data_set_name': '可以使用:anl45_risk_free_rate', 'description': '不可使用,仅供参考:The unrealised return on an open idea'} |
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{'id': 136453, 'data_set_name': '可以使用:total_equity_at_risk_2', 'description': '不可使用,仅供参考:Total stock, option, and incentive plan awards at risk for the director (alternate).'} |
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{'id': 136771, 'data_set_name': '可以使用:fnd17_alldelay1_gvary5ep', 'description': '不可使用,仅供参考:P/E Excluding Extraordinary Items, 5 Year Average'} |
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{'id': 137069, 'data_set_name': '可以使用:fnd17_gvary5ep', 'description': '不可使用,仅供参考:P/E excluding extordinary items, 5 Year Average'} |
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{'id': 137363, 'data_set_name': '可以使用:quarterly_return_on_equity_percent_3', 'description': '不可使用,仅供参考:Return on average equity for the most recent quarter (annualized).'} |
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{'id': 137391, 'data_set_name': '可以使用:return_on_average_assets_quarterly', 'description': '不可使用,仅供参考:Return on average assets for the most recent quarter, annualized.'} |
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{'id': 137460, 'data_set_name': '可以使用:annual_financial_risk_reserve', 'description': '不可使用,仅供参考:Annual financial risk reserve at the reporting date.'} |
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{'id': 137486, 'data_set_name': '可以使用:cumulative_financial_risk_reserve_since_q1', 'description': '不可使用,仅供参考:Cumulative financial risk reserve value since the first quarter.'} |
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{'id': 137525, 'data_set_name': '可以使用:equity_securities_gain_loss', 'description': '不可使用,仅供参考:Net gain or loss on equity securities during the period.'} |
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{'id': 137535, 'data_set_name': '可以使用:extraordinary_losses', 'description': '不可使用,仅供参考:[Quarterly] Expected Return on Assets - Domestic'} |
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{'id': 137538, 'data_set_name': '可以使用:financial_risk_reserve', 'description': '不可使用,仅供参考:Financial risk reserve at the reporting date.'} |
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{'id': 137544, 'data_set_name': '可以使用:financing_loan_drawdown_net', 'description': '不可使用,仅供参考:If long-term debt issuances and reductions are not delineated separately, the total is classified as Long Term Debt, Net'} |
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{'id': 137545, 'data_set_name': '可以使用:financing_loan_drawdown_receipts', 'description': '不可使用,仅供参考:represents cash outflow on the repayment of long-term debt in a company. Long-term debt obligations may be repaid upon maturity or replaced with new debt.'} |
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{'id': 139029, 'data_set_name': '可以使用:return_on_equity_ratio_3', 'description': "不可使用,仅供参考:Return on equity, calculated as net income divided by average shareholders' equity."} |
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{'id': 140116, 'data_set_name': '可以使用:fnd31_creditrisk', 'description': '不可使用,仅供参考:Credit Risk is measured by CDS levels based on end-of-day par spreads.'} |
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{'id': 140141, 'data_set_name': '可以使用:fnd31_earnshortfall', 'description': '不可使用,仅供参考:Earnings Shortfall. It is defined as the difference between reported accounting earnings and free cash flow, scaled by average assets.'} |
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{'id': 140616, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_act_ret_loss_pension_plan_ast', 'description': '不可使用,仅供参考:The actual gain or loss on pension plan assets'} |
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{'id': 140631, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_expected_return_pension', 'description': '不可使用,仅供参考:The component of net pension expense that pertains to the expected return on pension plan assets'} |
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{'id': 140632, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_expected_return_plan_assets', 'description': '不可使用,仅供参考:The estimated expected long-term rate of return on pension plan assets expressed as a percent'} |
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{'id': 140636, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_foreign_exch_loss', 'description': '不可使用,仅供参考:Foreign Exchange Losses'} |
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{'id': 140646, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_net_non_oper_loss', 'description': '不可使用,仅供参考:Net Non-Operating Losses'} |
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{'id': 140664, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_unrealized_gain_loss_comp_inc', 'description': '不可使用,仅供参考:Disclosed as a component in the calculation of Comprehensive Income'} |
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{'id': 140665, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_xo_loss_bef_tax_eff', 'description': '不可使用,仅供参考:Extraordinary Loss Before Tax Effects'} |
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{'id': 140670, 'data_set_name': '可以使用:fnd72_pit_or_is_a_net_non_oper_loss', 'description': '不可使用,仅供参考:Net non-operating loss or gain as a percentage of net shares'} |
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{'id': 140700, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_foreign_exch_loss', 'description': '不可使用,仅供参考:Foreign Exchange Losses'} |
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{'id': 140709, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_net_non_oper_loss', 'description': '不可使用,仅供参考:Net Non-Operating Losses'} |
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{'id': 140719, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_unrealized_gain_loss_comp_inc', 'description': '不可使用,仅供参考:Disclosed as a component in the calculation of Comprehensive Income'} |
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{'id': 140724, 'data_set_name': '可以使用:fnd72_pit_or_is_q_net_non_oper_loss', 'description': '不可使用,仅供参考:Net non-operating loss or gain as a percentage of net shares'} |
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{'id': 140901, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_is_net_non_oper_loss', 'description': '不可使用,仅供参考:Net Non-Operating Losses'} |
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{'id': 140910, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_is_unrealized_gain_loss_comp_inc', 'description': '不可使用,仅供参考:Disclosed as a component in the calculation of Comprehensive Income'} |
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{'id': 140915, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_net_non_oper_loss', 'description': '不可使用,仅供参考:Net non-operating loss or gain as a percentage of net shares'} |
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{'id': 140930, 'data_set_name': '可以使用:fnd86_risk_score', 'description': '不可使用,仅供参考:Risk score'} |
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{'id': 140940, 'data_set_name': '可以使用:srp_risk_score', 'description': '不可使用,仅供参考:Risk score'} |
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{'id': 373194, 'data_set_name': '可以使用:returns', 'description': '不可使用,仅供参考:Daily returns'} |
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{'id': 373297, 'data_set_name': '可以使用:pv173_ranksbondreturn20deqwt', 'description': '不可使用,仅供参考:It is defined as the equally weighted single bond return over last 20 days with the filter of the bonds that mature between 3 years and 7 years.'} |
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{'id': 373324, 'data_set_name': '可以使用:pv173_rawratiosbondreturn20deqwt', 'description': '不可使用,仅供参考:It is defined as the equally weighted single bond return over last 20 days with the filter of the bonds that mature between 3 years and 7 years.'} |
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{'id': 373351, 'data_set_name': '可以使用:pv173_zscoresbondreturn20deqwt', 'description': '不可使用,仅供参考:It is defined as the equally weighted single bond return over last 20 days with the filter of the bonds that mature between 3 years and 7 years.'} |
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========================= 数据字段结束 ======================================= |
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|
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以上数据字段和操作符, 按照Description说明组合, 但是每一个 alpha 组合的使用的数据字段和操作符不要过于集中, 在符合语法的情况下, 多尝试不同的组合 |
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|
||||
输出只要语法正确的WebSim表达式, 不需要任何解释 |
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你再检查一下, 如果你使用了 |
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Operator abs does not support event inputs |
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Operator ts_mean does not support event inputs |
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Operator ts_scale does not support event inputs |
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Operator add does not support event inputs |
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Operator sign does not support event inputs |
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Operator greater does not support event inputs |
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Operator ts_av_diff does not support event inputs |
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Operator ts_quantile does not support event inputs |
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Operator ts_arg_min does not support event inputs |
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Operator divide does not support event inputs |
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Operator ts_corr does not support event inputs |
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Operator ts_decay_linear does not support event inputs |
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Operator ts_sum does not support event inputs |
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Operator ts_delay does not support event inputs |
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Operator ts_arg_max does not support event inputs |
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Operator ts_std_dev does not support event inputs |
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Operator ts_regression does not support event inputs |
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Operator ts_backfill does not support event inputs |
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Operator signed_power does not support event inputs |
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Operator ts_product does not support event inputs |
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Operator ts_zscore does not support event inputs |
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Operator group_rank does not support event inputs |
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Operator subtract does not support event inputs |
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Operator ts_delta does not support event inputs |
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Operator ts_rank does not support event inputs |
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Operator ts_count_nans does not support event inputs |
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Operator ts_covariance does not support event inputs |
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Operator multiply does not support event inputs |
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Operator if_else does not support event inputs |
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Operator group_neutralize does not support event inputs |
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Operator group_zscore does not support event inputs |
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Operator winsorize does not support event inputs |
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注意, 以上操作符不能使用事件类型的数据集, 以上操作符禁止使用事件类型的数据集!! |
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@ -1,324 +1,239 @@ |
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zscore(multiply(anl14_mean_eps_fy1,market_capitalization_latest)) |
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ts_decay_linear(fnd28_value_19501, 5) |
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abs(subtract(anl69_net_best_eeps_cur_yr,anl69_net_best_eeps_nxt_yr)) |
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subtract(ts_sum(anl14_numofests_eps_fy2, 60), ts_sum(anl14_numofests_epsrep_fp1, 60)) |
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rank(multiply(ts_decay_linear(power(abs(subtract(ts_regression(bid4_volume, bid4_price, 4), ts_regression(ask4_volume, ask4_price, 4))), 1.5), 5), abs(divide(subtract(last_closing_price, (vec_sum(bid1_5_price*bid1_5_volume)+vec_sum(ask1_5_price*ask1_5_volume))/(vec_sum(bid1_5_volume)+vec_sum(ask1_5_volume))), latest_closing_price)*ts_mean(volume, 3)))) |
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ts_delay(fnd28_value_18189a, 5) |
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group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, ts_mean(last_closing_price, 20))), 5), ts_delta(pv37_volume_global, 5))), industry_grouping_level5_top1500) |
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ts_product(anl69_best_eps_chg_pct, 10) |
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signed_power(quarterly_return_on_equity_percent,3) |
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ts_regression(anl69_best_eps_gaap, fifty_day_average_price, 20, 0, 0) |
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ts_av_diff(anl69_roe_best_cur_fiscal_year_period, 5) |
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add(ts_rank(anl69_roa_best_eeps_cur_yr, 15), ts_rank(anl69_roa_best_eeps_nxt_yr, 15)) |
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group_scale(anl69_best_eps_chg_pct, industry_grouping_level2_top1500) |
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signed_power(ts_zscore(quarterly_net_loans_change_percent, 35), 0.3) |
||||
ts_arg_max(quarterly_reinvestment_rate, 12) |
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group_mean(anl69_best_eps_chg_pct, 1, industry_grouping_level2_top1500) |
||||
add(rank(anl14_mean_eps_fy1), rank(anl69_eps_best_eeps_cur_yr)) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, ts_mean(last_closing_price, 10))), 10), ts_delta(pv37_intv_mean, 10))), industry_grouping_level10_top3000_2) |
||||
ts_sum(anl69_eps_best_eeps_cur_yr, 10) |
||||
winsorize(ts_delta(rank(anl69_roe_best_eeps_cur_yr), 5), std=3) |
||||
power(anl69_best_eps_chg_pct,2) |
||||
rank(anl14_mean_eps_fy2) |
||||
ts_count_nans(quarterly_total_share_capital, 15) |
||||
inverse(anl14_numofests_epsrep_fp1) |
||||
zscore(divide(fnd6_newqus_epspiq,quarterly_common_shares_outstanding_quarter_2)) |
||||
rank(winsorize(anl69_eps_best_eps_4wk_chg, std=2)) |
||||
add(ts_mean(quarterly_cash_flow_per_share_2, 25), ts_mean(quarterly_cash_flow_per_share_3, 25)) |
||||
group_neutralize(rank(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(pv37_volume_global3h, 5))), industry_grouping_level5_top1500) |
||||
group_neutralize(rank(multiply(ts_decay_linear(log(abs(subtract(ts_regression(bid3_volume, bid3_price, 6), ts_regression(ask3_volume, ask3_price, 6)))+1), 5), inverse(1+abs(subtract(latest_closing_price, (vec_sum(bid1_5_price*bid1_5_volume)+vec_sum(ask1_5_price*ask1_5_volume))/(vec_sum(bid1_5_volume)+vec_sum(ask1_5_volume))))))), top2000_factor4_group10_score) |
||||
and(anl14_numofests_eps_fp1>5,anl69_best_eps_chg_pct>0) |
||||
ts_zscore(anl69_eps_best_eeps_cur_yr, 30) |
||||
zscore(ts_delta(anl14_mean_eps_fp1, 5)) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(pv37_high_13, pv37_ivwp_mean)), 10), ts_delta(pv37_intv_mean, 10))), industry_grouping_level5_top1500) |
||||
group_neutralize(rank(multiply(ts_decay_linear(abs(subtract(last_closing_price, fnd17_priceavg200day)), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(signed_power(subtract(pv37_high_13, pv37_ivwp_mean), 0.5), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
group_neutralize(anl69_best_eps_chg_pct, industry_grouping_level2_top1500) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(power(abs(subtract(last_closing_price, pv37_ivwp_mean)), 0.5), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
group_neutralize(rank(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level10_top3000_2) |
||||
ts_av_diff(anl69_eps_best_eeps_cur_yr, 30) |
||||
ts_arg_min(fnd28_value_08371q, 15) |
||||
ts_sum(fnd28_value_08803a, 10) |
||||
divide(ts_product(anl69_dps_best_eeps_cur_yr, 5), ts_product(quarterly_dividend_per_share, 5)) |
||||
group_neutralize(ts_mean(anl14_mean_revenue_fy2, 10), pca_industry_grouping_method2_2) |
||||
group_rank(ts_sum(quarterly_total_debt, 40), industry_grouping_level50_top2000) |
||||
vector_neut(anl69_ebit_best_eeps_nxt_yr,industry_grouping_level50_top2000) |
||||
ts_rank(winsorize(anl69_eps_best_eps_4wk_chg, std=4), 30) |
||||
divide(rank(anl69_net_best_eeps_cur_yr), rank(quarterly_net_income_2)) |
||||
if_else(ts_mean(last_closing_price, 5) > ts_delay(last_closing_price, 5), 1, -1) |
||||
multiply(ts_corr(anl14_mean_eps_fp2, anl14_mean_epsrep_fp2, 20), ts_corr(quarterly_asset_turnover_2, quarterly_revenue_per_share, 20)) |
||||
signed_power(ts_zscore(quarterly_free_cash_flow_per_share_prior, 28), 0.4) |
||||
group_rank(ts_sum(quarterly_accumulated_depreciation_2, 35), industry_grouping_level50_top2000) |
||||
divide(ts_sum(fnd28_value_04052q, 10), ts_sum(fnd28_value_04355q, 10)) |
||||
ts_delay(anl69_cps_best_eeps_cur_yr, 5) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(pv37_high_global1h, fnd17_priceavg200day)), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(log(add(abs(subtract(pv37_high_13, pv37_ivwp_mean)), 1)), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
multiply(ts_corr(anl69_epss_best_eps, anl69_epss_best_eps_gaap, 15), ts_corr(quarterly_revenue_per_employee_3, quarterly_revenue_per_share_2, 15)) |
||||
ts_rank(fnd28_value_09416a, 20) |
||||
subtract(anl69_best_eps_gaap, anl69_best_eps_gaap_stddev) |
||||
abs(ts_delta(anl69_dpss_best_eeps_cur_yr, 6)) |
||||
ts_min(anl69_best_eps_gaap, 20) |
||||
divide(rank(anl69_eps_best_eps_4wk_chg), rank(quarterly_book_value_per_share_2)) |
||||
divide(ts_product(quarterly_cost_of_goods_sold, 7), ts_product(quarterly_cost_of_goods_sold_3, 7)) |
||||
ts_rank(fnd17_aprice2bk, 30) |
||||
group_zscore(anl69_best_eps_chg_pct, industry_grouping_level2_top1500) |
||||
rank(divide(fnd31_coreepsp,anl69_pe_best_eeps_cur_yr)) |
||||
last_diff_value(anl69_best_eps_chg_pct, 20) |
||||
group_zscore(anl69_eps_best_eps_4wk_chg, industry_grouping_level2_top1500) |
||||
ts_quantile(fnd28_value_05091, 10, "gaussian") |
||||
ts_decay_linear(anl69_best_eps_chg_pct, 10) |
||||
ts_regression(anl69_saless_best_eeps_cur_yr, quarterly_sales_best_eeps_nxt_yr, 22, 0, 2) |
||||
ts_decay_linear(anl69_eps_best_eeps_cur_yr, 10) |
||||
group_neutralize(rank(ts_delta(anl69_eps_best_eps_4wk_chg, 5)), industry_grouping_level2_top1500) |
||||
rank(ts_delay(anl69_roa_best_eeps_cur_yr, 10)) |
||||
add(ts_mean(fnd28_value_05092, 10), ts_std_dev(fnd28_value_05092, 10)) |
||||
group_scale(anl14_stddev_eps_fy5,sta2_top500_fact1_c5) |
||||
ts_count_nans(anl14_high_ntp_fy4, 10) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(ts_regression(bid3_volume, bid3_price, 4), ts_regression(ask3_volume, ask3_price, 4))), 5), inverse(1+abs(subtract(latest_closing_price, (vec_sum(bid1_5_price*bid1_5_volume)+vec_sum(ask1_5_price*ask1_5_volume))/(vec_sum(bid1_5_volume)+vec_sum(ask1_5_volume))))))), top2000_factor2_group10_score) |
||||
log(max(anl14_high_eps_fy1,anl14_low_eps_fy1)) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(power(abs(subtract(last_closing_price, pv37_ivwp_mean)), 0.3), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(signed_power(subtract(last_closing_price, pv37_ivwp_mean), 0.5), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
rank(multiply(anl69_eps_best_eps_4wk_chg, quarterly_eps_excl_extraordinary_2)) |
||||
group_neutralize(ts_delta(anl69_eps_best_eps, 5), industry_grouping_level2_top1500) |
||||
ts_rank(quarterly_net_income_2, 40) |
||||
ts_delay(anl69_best_eps_gaap, 5) |
||||
ts_backfill(quarterly_total_liabilities_3, 10, 1, "NAN") |
||||
ts_rank(anl69_best_eps_median, 30) |
||||
trade_when(anl69_best_eps_chg_pct > 0, anl69_best_eps_chg_pct, 0) |
||||
kth_element(quarterly_ebit_4,60,3) |
||||
ts_backfill(quarterly_common_equity_value, 9, 1, "NAN") |
||||
subtract(ts_sum(anl14_stddev_eps_fp3, 45), ts_sum(anl14_stddev_epsrep_fy1, 45)) |
||||
if_else(ts_min(anl69_roe_best_cur_fiscal_qtr_period, 8) > 0, ts_max(quarterly_total_shareholder_equity, 8), ts_min(quarterly_total_liabilities_4, 8)) |
||||
ts_arg_max(fnd28_value_05191q, 15) |
||||
group_neutralize(zscore(anl69_best_eps_chg_pct), industry_grouping_level50_top2000) |
||||
ts_mean(anl14_median_eps_fy1, 20) |
||||
sqrt(min(quarterly_cash_flow_per_share_2,quarterly_free_cash_flow_per_share_alt)) |
||||
ts_delay(rank(anl69_eps_best_eps_gaap_4wk_chg), 3) |
||||
ts_count_nans(fnd28_value_05450, 10) |
||||
ts_rank(anl14_mean_eps_fy2, 60) |
||||
group_neutralize(rank(multiply(ts_decay_linear(abs(subtract(pv37_high_global2h, pv37_ivwp_mean)), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
ts_rank(anl69_eps_best_eeps_cur_yr, 60) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(pv37_high_global1h, pv37_ivwp_mean)), 5), ts_delta(pv37_intv_mean, 5))), pca_industry_grouping_method2_5) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, fnd17_priceavg200day)), 5), ts_delta(pv37_intv_mean, 5))), pca_industry_grouping_method2_50) |
||||
quantile(anl69_best_eps_chg_pct, "gaussian", 1.0) |
||||
ts_backfill(fnd28_value_08805a, 10, 1, "NAN") |
||||
divide(anl69_eps_best_eps_4wk_chg, ts_std_dev(anl69_eps_best_eps_4wk_chg, 20)) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(signed_power(subtract(pv37_high_global2h, pv37_ivwp_mean), 0.5), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
ts_mean(anl69_cps_best_eeps_cur_yr, 20) |
||||
group_neutralize(ts_delta(quarterly_asset_turnover, 6), industry_grouping_level2_top3000_2) |
||||
winsorize(group_zscore(anl69_roe_best_eeps_cur_yr, industry_grouping_level2_top1500), std=2) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(pv37_high_global1h, ts_mean(pv37_high_global1h, 10))), 10), ts_delta(pv37_intv_mean, 10))), industry_grouping_level5_top1500) |
||||
ts_rank(anl69_eps_best_eps_4wk_chg, 20) |
||||
group_zscore(ts_delay(anl69_eps_best_eps_4wk_chg, 5), industry_grouping_level2_top3000_2) |
||||
ts_backfill(anl69_eps_best_eeps_cur_yr, 5) |
||||
ts_quantile(anl69_best_eps_gaap, 20, "gaussian") |
||||
group_neutralize(zscore(multiply(ts_decay_linear(log(add(abs(subtract(last_closing_price, pv37_ivwp_mean)), 0.01)), 10), ts_delta(pv37_intv_mean, 10))), industry_grouping_level5_top1500) |
||||
ts_av_diff(fnd28_value_18310q, 10) |
||||
ts_backfill(anl14_mean_ebitda_fy1, 10, 1, "NAN") |
||||
ts_std_dev(fnd28_value_09126a, 15) |
||||
rank(group_zscore(anl69_roa_best_eeps_nxt_yr,top2000_factor1_group20_score)) |
||||
ts_corr(anl69_cps_best_eeps_cur_yr, anl69_eps_best_eeps_cur_yr, 30) |
||||
zscore(quarterly_asset_turnover) |
||||
ts_arg_max(quarterly_net_loans_2, 18) |
||||
multiply(anl69_best_eps_chg_pct, log(market_capitalization_latest)) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, fnd17_priceavg200day)), 10), ts_delta(pv37_volume_global, 10))), industry_grouping_level5_top1500) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(log(add(abs(subtract(last_closing_price, pv37_ivwp_mean)), 0.1)), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
ts_corr(last_closing_price, pv37_volume_global, 10) |
||||
ts_corr(anl69_eps_best_eeps_cur_yr, anl69_cps_best_eeps_cur_yr, 30) |
||||
trade_when(anl69_best_eps_chg_pct>0,anl69_eps_best_eeps_nxt_yr,nan) |
||||
hump(anl69_best_eps_chg_pct, 0.01) |
||||
group_rank(anl69_best_eps_chg_pct, industry_grouping_level2_top1500) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, ts_mean(last_closing_price, 30))), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
group_zscore(ts_sum(fnd28_value_08140, 5), industry_grouping_level5_top1500) |
||||
multiply(rank(ts_mean(anl69_best_eps_chg_pct, 10)), rank(ts_delta(anl69_eps_best_eeps_nxt_yr, 10))) |
||||
ts_delta(anl69_cps_best_eeps_cur_yr, 5) |
||||
ts_covariance(anl69_best_eps_gaap, fifty_day_average_price, 20) |
||||
ts_max(anl69_best_eps_gaap, 20) |
||||
power(quarterly_return_on_equity_percent, 2) |
||||
ts_corr(anl14_mean_eps_fy2, quarterly_return_on_equity_percent, 10) |
||||
ts_av_diff(fnd28_value_18309q, 10) |
||||
group_backfill(anl69_best_eps_gaap, industry_grouping_level2_top1500, 10, 4.0) |
||||
ts_backfill(anl69_best_eps_gaap, 10) |
||||
multiply(ts_zscore(anl14_high_roe_fy3, 15), ts_av_diff(fnd28_value_08631, 10)) |
||||
zscore(divide(anl69_eps_best_eps_4wk_chg, quarterly_eps_incl_extraordinary)) |
||||
ts_product(anl69_cps_best_eeps_cur_yr, 5) |
||||
ts_arg_max(returns, 60) |
||||
rank(ts_count_nans(returns, 60)) |
||||
ts_rank(ts_sum(multiply(returns, returns < ts_quantile(returns, 120, "gaussian")), 120), 60) |
||||
zscore(group_rank(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),divide(returns,100),0),60),subindustry)) |
||||
rank(ts_step(1)) |
||||
ts_std_dev(ts_mean(volume, 60), 60) |
||||
ts_product(divide(ts_mean(volume, 60), cap), 60) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), rank(ttm_return_on_equity_percent)) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), quarterly_return_on_assets_percent_2) |
||||
rank(ts_product(returns, 60)) |
||||
vec_sum(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
rank(subtract(ts_sum(returns, 60), ts_sum(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60))) |
||||
ts_step(1) |
||||
divide(ts_std_dev(quarterly_long_term_debt_to_equity, 40), ts_std_dev(quarterly_total_debt_to_equity_ratio, 40)) |
||||
ts_scale(anl69_eps_best_eeps_cur_yr, 20) |
||||
divide(anl14_mean_eps_fy1, fifty_day_average_price) |
||||
ts_count_nans(anl69_epss_best_eps_gaap, 20) |
||||
signed_power(ts_zscore(quarterly_free_cash_flow_2, 30), 0.5) |
||||
ts_scale(anl69_cps_best_eeps_cur_yr, 20) |
||||
subtract(ts_mean(fnd28_value_07210q, 10), ts_mean(fnd28_value_05190, 10)) |
||||
normalize(add(anl69_eps_best_eeps_cur_yr,anl69_eps_best_eeps_nxt_yr)) |
||||
ts_count_nans(anl69_cps_best_eeps_cur_yr, 20) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, ts_mean(last_closing_price, 15))), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
not(anl69_eps_best_fperiod_override==0) |
||||
signed_power(ts_delta(fnd28_value_08335a, 5), 0.5) |
||||
abs(ts_delta(anl69_cpss_best_eeps_nxt_yr, 4)) |
||||
subtract(rank(quarterly_price_to_sales_ratio_3), rank(quarterly_price_to_cash_flow_per_share_2)) |
||||
days_from_last_change(anl69_eps_expected_report_dt) |
||||
ts_zscore(anl69_best_eps_gaap, 20) |
||||
signed_power(ts_delta(fnd28_value_09326a, 5), 0.5) |
||||
ts_product(ts_scale(fnd28_value_08125, 10), 1) |
||||
rank(ts_mean(anl69_best_eps_chg_pct, 20)) |
||||
ts_scale(quarterly_current_assets, 20, 0.01) |
||||
group_zscore(ts_mean(quarterly_operating_margin_percent, 20), industry_grouping_level10_top2000) |
||||
ts_arg_min(anl69_net_best_eeps_nxt_yr, 15) |
||||
ts_corr(anl69_best_eps_chg_pct, quarterly_eps_excluding_extraordinary, 30) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 15), ts_delta(pv37_intv_mean, 15))), industry_grouping_level5_top1500) |
||||
ts_covariance(quarterly_ebit_value, quarterly_net_income_to_common, 25) |
||||
subtract(ts_std_dev(anl69_eps_best_eps_4wk_chg, 10), ts_std_dev(anl69_eps_best_eps_4wk_chg, 30)) |
||||
group_rank(fnd28_value_18469q, industry_grouping_level50_top2000) |
||||
multiply(rank(anl69_best_eps_chg_pct), rank(social_score_subsector_position)) |
||||
ts_decay_linear(fnd28_value_08381, 5) |
||||
ts_regression(anl69_ndebt_best_eeps_cur_yr, quarterly_total_debt_to_assets, 25, 0, 1) |
||||
rank(anl69_best_eps_chg_pct) |
||||
divide(ts_std_dev(anl69_roa_best_eeps_cur_yr, 15), ts_mean(anl69_roa_best_eeps_cur_yr, 15)) |
||||
multiply(rank(fnd28_value_09904), rank(fnd28_value_09106)) |
||||
subtract(ts_mean(fnd28_value_08650a, 10), ts_mean(fnd28_value_08631a, 10)) |
||||
group_rank(fnd28_value_18318q, pca_industry_grouping_method4_10) |
||||
ts_arg_max(anl69_roa_best_eeps_nxt_yr, 15) |
||||
zscore(ts_delta(fnd28_value_19534, 5)) |
||||
ts_backfill(anl69_cps_best_eeps_cur_yr, 5) |
||||
ts_scale(anl14_numofests_eps_fy2, 20) |
||||
bucket(rank(divide(anl69_best_eps_median,anl14_numofests_eps_fp1)),range="0,1,0.2") |
||||
ts_sum(anl69_best_eps_chg_pct, 30) |
||||
ts_delta(anl69_eps_best_eeps_cur_yr, 5) |
||||
add(rank(anl69_roe_best_eeps_cur_yr), rank(anl69_roe_best_eeps_nxt_yr)) |
||||
ts_mean(last_closing_price, 5) |
||||
ts_covariance(quarterly_return_on_average_assets, quarterly_return_on_investment, 22) |
||||
densify(sta2_top3000_fact1_c20) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(pv37_high_13, pv37_ivwp_mean)), 5), ts_delta(pv37_volume_13, 5))), industry_grouping_level5_top1500) |
||||
ts_rank(divide(anl69_eps_best_eps_gaap, anl69_eps_best_eps_4wk_chg), 10) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
ts_delay(anl69_eps_best_eeps_cur_yr, 5) |
||||
ts_quantile(fnd28_value_09104q, 10, "gaussian") |
||||
self_corr(anl69_eps_best_eps_4wk_chg) |
||||
multiply(sign(ts_delta(anl14_stddev_eps_fy5, 5)), ts_rank(quarterly_asset_turnover, 15)) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, fnd17_priceavg200day)), 15), ts_delta(pv37_intv_mean, 15))), industry_grouping_level5_top1500) |
||||
ts_delta(anl69_eps_best_eps_4wk_chg, 10) |
||||
sign(divide(anl69_eps_best_eps_gaap,quarterly_book_value_per_share_2)) |
||||
subtract(ts_sum(anl14_numofests_eps_fp4, 50), ts_sum(anl14_numofests_epsrep_fp3, 50)) |
||||
rank(subtract(ts_mean(anl14_mean_eps_fy3, 20), ts_mean(anl14_median_eps_fy1, 20))) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, fnd17_priceavg200day)), 5), ts_delta(pv37_intv_mean, 5))), pca_industry_grouping_method4_10) |
||||
group_neutralize(rank(multiply(ts_decay_linear(abs(subtract(last_closing_price, ts_mean(last_closing_price, 25))), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(sqrt(abs(subtract(last_closing_price, pv37_ivwp_mean))), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
group_neutralize(rank(anl69_eps_best_eps_4wk_chg), industry_grouping_level2_top1500) |
||||
if_else(ts_mean(fnd28_value_08621, 5) > ts_delay(fnd28_value_08621, 5), 1, -1) |
||||
ts_backfill(anl69_cps_best_eeps_cur_yr, 5, 1, "NAN") |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, fnd17_priceavg200day)), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
ts_count_nans(anl69_best_eps_gaap, 50) |
||||
ts_arg_max(anl69_cps_best_eeps_cur_yr, 20) |
||||
signed_power(anl69_best_eps_chg_pct, 0.5) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(pv37_volume_13, 5))), pca_industry_grouping_method4_20) |
||||
add(ts_mean(anl14_mean_div_fy5, 10), ts_std_dev(anl14_mean_div_fy5, 10)) |
||||
ts_regression(anl69_sales_best_eeps_nxt_yr, quarterly_total_revenue, 20, 0, 0) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(pv37_intv_mean, 5))), pca_industry_grouping_method2_5) |
||||
divide(ts_std_dev(quarterly_interest_coverage_ratio, 30), ts_std_dev(quarterly_debt_service_to_eps_2, 30)) |
||||
ts_arg_min(anl69_cps_best_eeps_cur_yr, 20) |
||||
subtract(ts_decay_linear(anl69_pe_best_eeps_cur_yr, 10), ts_decay_linear(quarterly_pe_ratio_high_2, 10)) |
||||
ts_decay_linear(anl69_cps_best_eeps_cur_yr, 10) |
||||
quantile(anl14_median_eps_fp5,driver="uniform") |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(pv37_high_global2h, pv37_ivwp_mean)), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
group_backfill(anl69_cps_best_eeps_cur_yr,exchange,30) |
||||
rank(divide(anl69_best_eps_chg_pct,quarterly_eps_excluding_extraordinary)) |
||||
ts_delta(quarterly_return_on_equity_percent, 4) |
||||
ts_quantile(anl69_eps_best_eeps_cur_yr, 60) |
||||
normalize(quarterly_ebit_value) |
||||
if_else(ts_mean(anl14_low_eps_fy1, 10) > ts_mean(anl14_high_eps_fy5, 10), ts_std_dev(quarterly_return_on_equity_percent, 20), ts_std_dev(quarterly_return_on_assets_percent, 20)) |
||||
ts_regression(last_closing_price, pv37_volume_global, 10, 0, 1) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(pv37_volume_global, 5))), industry_grouping_level10_top3000_2) |
||||
ts_mean(anl69_eps_best_eeps_cur_yr, 20) |
||||
multiply(rank(anl14_median_eps_fy1), rank(anl69_eps_best_eps_4wk_chg)) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(pv37_volume_global3h, 5))), industry_grouping_level5_top1500) |
||||
ts_rank(add(rank(anl69_eps_best_eps_4wk_chg), rank(anl69_eps_best_eps_numest)), 20) |
||||
normalize(multiply(anl14_mean_epsrep_fy2,quarterly_revenue_per_share)) |
||||
ts_mean(fnd28_value_19505, 10) |
||||
ts_arg_min(fnd28_value_05145, 15) |
||||
ts_corr(fnd28_value_19542, fnd28_value_19545, 10) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(sqrt(abs(subtract(pv37_high_13, pv37_ivwp_mean))), 5), ts_delta(pv37_volume_13, 5))), industry_grouping_level5_top1500) |
||||
divide(ts_sum(pv37_volume_global, 20), ts_mean(pv37_volume_global, 20)) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(pv37_high_global1h, pv37_ivwp_mean)), 5), ts_delta(pv37_volume_global3h, 5))), industry_grouping_level5_top1500) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(ts_mean(pv37_intv_mean, 5), 5))), industry_grouping_level5_top1500) |
||||
hump(anl14_actvalue_epsrep_fp0,0.02) |
||||
zscore(multiply(ts_decay_linear(log(abs(subtract(ts_regression(bid2_volume, bid2_price, 3), ts_regression(ask2_volume, ask2_price, 3)))+1), 5), power(abs(divide(subtract(latest_closing_price, (vec_sum(bid1_5_price*bid1_5_volume)+vec_sum(ask1_5_price*ask1_5_volume))/(vec_sum(bid1_5_volume)+vec_sum(ask1_5_volume))), latest_closing_price)), 0.5))) |
||||
ts_delta(rank(anl69_roe_best_eeps_cur_yr), 5) |
||||
ts_covariance(quarterly_earnings_before_tax_3, quarterly_ebit_4, 20) |
||||
multiply(rank(quarterly_eps_excl_extraordinary), rank(anl69_eps_best_eps_4wk_chg)) |
||||
ts_arg_max(anl69_best_eps_gaap, 30) |
||||
or(anl14_stddev_eps_fp1<0.1,anl69_eps_best_eps_lo>0) |
||||
divide(anl69_eps_best_eps_4wk_chg, anl69_eps_best_eps_gaap_4wk_chg) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(pv37_intv_mean, 5))), top1500_pca_factor1_grouping10) |
||||
ts_sum(anl69_cps_best_eeps_cur_yr, 10) |
||||
ts_regression(anl69_cps_best_eeps_cur_yr, anl69_eps_best_eeps_cur_yr, 30) |
||||
scale_down(anl69_sales_best_eeps_nxt_yr,0.1) |
||||
ts_zscore(anl69_cps_best_eeps_cur_yr, 30) |
||||
multiply(rank(fnd28_value_09304q), rank(fnd28_value_09602)) |
||||
abs(ts_delta(anl14_median_epsrep_fp4, 3)) |
||||
ts_quantile(anl69_cps_best_eeps_cur_yr, 60) |
||||
ts_covariance(anl69_cps_best_eeps_cur_yr, anl69_eps_best_eeps_cur_yr, 30) |
||||
ts_count_nans(quarterly_total_debt_to_capital_2, 18) |
||||
group_neutralize(rank(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 10), ts_delta(pv37_volume_13, 10))), industry_grouping_level5_top1500) |
||||
ts_count_nans(anl69_eps_best_eeps_cur_yr, 20) |
||||
ts_arg_min(anl69_eps_best_eeps_cur_yr, 20) |
||||
rank(ts_delta(fifty_day_average_price, 5)) |
||||
subtract(ts_rank(anl69_eps_best_eps_4wk_chg, 10), ts_rank(anl69_eps_best_eps_gaap_4wk_chg, 10)) |
||||
normalize(multiply(ts_decay_linear(power(abs(subtract(ts_regression(bid1_volume, bid1_price, 3), ts_regression(ask1_volume, ask1_price, 3))), 3), 5), abs(divide(subtract(last_closing_price, (vec_sum(bid1_5_price*bid1_5_volume)+vec_sum(ask1_5_price*ask1_5_volume))/(vec_sum(bid1_5_volume)+vec_sum(ask1_5_volume))), last_closing_price)))) |
||||
abs(ts_av_diff(quarterly_quick_ratio_prior, 10)) |
||||
ts_arg_min(quarterly_reinvestment_rate_3, 12) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(pv37_volume_global, 5))), industry_grouping_level50_top2000) |
||||
multiply(zscore(anl69_best_eps_chg_pct), zscore(anl69_eps_best_eps_numest)) |
||||
group_rank(ts_sum(quarterly_long_term_debt_3, 30), industry_grouping_level20_top3000_2) |
||||
subtract(ts_max(last_closing_price, 20), ts_min(last_closing_price, 20)) |
||||
days_from_last_change(anl69_best_eps_chg_pct) |
||||
vec_sum(quarterly_return_on_equity_percent) |
||||
ts_delta(fnd17_priceavg200day, 10) |
||||
ts_av_diff(anl69_best_eps_gaap, 20) |
||||
bucket(rank(anl69_best_eps_chg_pct), "0,1,0.1") |
||||
ts_std_dev(anl69_best_eps_gaap, 20) |
||||
kth_element(anl69_best_eps_gaap, 10, 1) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(ts_mean(pv37_intv_mean, 10), 5))), industry_grouping_level5_top1500) |
||||
subtract(rank(anl69_eps_best_eps_lo), rank(anl69_eps_best_eps_gaap)) |
||||
add(rank(anl69_sales_best_eeps_cur_yr), rank(quarterly_revenue_per_share)) |
||||
subtract(rank(anl69_eps_best_eps_gaap_4wk_dn), rank(anl69_eps_best_eps_4wk_chg)) |
||||
ts_arg_max(anl69_eps_best_eeps_cur_yr, 20) |
||||
group_neutralize(ts_delta(quarterly_inventory_turnover_ratio, 5), exchange) |
||||
subtract(rank(anl69_eps_best_eeps_nxt_yr), rank(ts_delay(anl69_eps_best_eeps_cur_yr, 20))) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(pv37_high_13, ts_mean(pv37_high_13, 20))), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
ts_av_diff(anl69_cps_best_eeps_cur_yr, 30) |
||||
winsorize(anl69_eps_best_eps_4wk_chg, std=3) |
||||
ts_product(anl69_eps_best_eeps_cur_yr, 5) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(log(add(abs(subtract(last_closing_price, pv37_ivwp_mean)), 0.001)), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
ts_product(ts_scale(fnd28_value_08636a, 10), 1) |
||||
add(ts_mean(quarterly_cash_per_share_amt, 30), ts_mean(quarterly_book_value_per_share_2, 30)) |
||||
group_neutralize(rank(multiply(ts_decay_linear(abs(subtract(last_closing_price, ts_mean(last_closing_price, 20))), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
ts_regression(anl69_eps_best_eeps_cur_yr, anl69_cps_best_eeps_cur_yr, 30) |
||||
ts_rank(anl69_cps_best_eeps_cur_yr, 60) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, fnd17_priceavg200day)), 20), ts_delta(pv37_intv_mean, 20))), industry_grouping_level5_top1500) |
||||
group_neutralize(rank(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(pv37_volume_13, 5))), industry_grouping_level5_top1500) |
||||
multiply(ts_delay(rank(anl69_eps_best_eeps_cur_yr), 10), rank(anl69_eps_best_eps_4wk_chg)) |
||||
divide(ts_sum(fnd28_value_05508a, 10), ts_sum(fnd28_value_07230a, 10)) |
||||
ts_delay(fnd28_value_18186a, 5) |
||||
multiply(rank(anl69_eps_best_eps_4wk_chg), rank(anl69_eps_best_eps_numest)) |
||||
rank(divide(fnd28_value_09402, fnd28_value_09411)) |
||||
abs(ts_av_diff(quarterly_current_liabilities_2, 12)) |
||||
add(anl14_mean_eps_fy2, anl14_mean_eps_fy3) |
||||
ts_delay(anl69_eps_best_eps_4wk_chg, 5) |
||||
if_else(anl69_best_eps_chg_pct > 0, 1, -1) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 20), ts_delta(pv37_volume_global, 20))), industry_grouping_level5_top1500) |
||||
group_neutralize(rank(multiply(ts_decay_linear(subtract(last_closing_price, pv37_ivwp_mean), 5), ts_delta(pv37_intv_mean, 5))), industry_grouping_level5_top1500) |
||||
zscore(ts_rank(anl69_eps_best_eps_4wk_chg, 10)) |
||||
divide(ts_product(quarterly_depreciation_expense, 8), ts_product(quarterly_depreciation_expense_2, 8)) |
||||
ts_std_dev(anl69_eps_best_eeps_cur_yr, 20) |
||||
ts_arg_min(quarterly_net_loans_3, 18) |
||||
last_diff_value(market_capitalization_latest,30) |
||||
ts_covariance(anl69_eps_best_eeps_cur_yr, anl69_cps_best_eeps_cur_yr, 30) |
||||
rank(multiply(zscore(anl69_best_eps_chg_pct), zscore(quarterly_eps_excl_extraordinary))) |
||||
ts_arg_max(fnd28_value_08326q, 15) |
||||
group_neutralize(zscore(multiply(ts_decay_linear(abs(subtract(last_closing_price, pv37_ivwp_mean)), 5), ts_delta(pv37_volume_global, 10))), industry_grouping_level5_top1500) |
||||
is_nan(anl14_low_epsrep_fp1) |
||||
if_else(ts_min(anl69_eps_best_eps_gaap_stddev, 10) < 0, ts_max(quarterly_operating_margin_percent_2, 10), ts_min(quarterly_operating_margin_percent_3, 10)) |
||||
subtract(rank(anl69_eps_best_eeps_cur_yr), rank(anl69_eps_best_eps_gaap_4wk_chg)) |
||||
zscore(anl69_best_eps_chg_pct) |
||||
ts_std_dev(anl69_cps_best_eeps_cur_yr, 20) |
||||
quantile(ts_delta(returns, 60), driver="gaussian", sigma=1.0) |
||||
ts_count_nans(divide(ts_mean(volume, 60), cap), 60) |
||||
group_zscore(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), bucket(rank(returns), buckets="2,5,6,7,10")) |
||||
ts_corr(returns, returns, 60) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), anl45_risk_free_rate) |
||||
ts_min(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_min(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), ttm_return_on_equity_percent) |
||||
rank(group_rank(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),log(abs(returns)+2),0),60),subindustry)) |
||||
subtract(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), rank(quarterly_return_on_assets_percent)) |
||||
group_zscore(rank(ts_mean(if_else(returns < ts_quantile(returns,60,gaussian),signed_power(returns,2.5),0),60)),country) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), quarterly_return_on_equity_percent) |
||||
-rank(ts_std_dev(if_else(ts_rank(returns,60)<0.05,returns,NaN),60)) |
||||
zscore(normalize(ts_mean(if_else(returns < ts_quantile(returns,50,uniform),min(returns,-0.1),0),50))) |
||||
zscore(ts_mean(if_else(returns < ts_quantile(returns,40,uniform),power(abs(returns),1.5),0),40)) |
||||
group_neutralize(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), bucket(rank(returns), buckets="2,5,6,7,10")) |
||||
reverse(rank(ts_std_dev(if_else(returns < ts_min(returns, 60), returns, NaN), 60))) |
||||
normalize(group_neutralize(ts_mean(if_else(returns < ts_quantile(returns,60,gaussian),sqrt(abs(returns))*sign(returns),0),60),industry)) |
||||
rank(abs(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),returns,0),60))) |
||||
reverse(rank(ts_std_dev(if_else(returns < ts_mean(returns, 120), returns, NaN), 60))) |
||||
ts_arg_max(divide(ts_mean(volume, 60), cap), 60) |
||||
quantile(ts_corr(returns, returns, 60), driver="gaussian", sigma=1.0) |
||||
reverse(rank(ts_std_dev(if_else(returns < subtract(ts_mean(returns, 60), multiply(ts_std_dev(returns, 60), 1.5)), returns, NaN), 60))) |
||||
zscore(group_neutralize(ts_mean(if_else(returns < ts_quantile(returns,50,cauchy),returns,0),50),country)) |
||||
zscore(ts_mean(if_else(returns < ts_quantile(returns,65,gaussian),ts_av_diff(returns,15),0),65)) |
||||
ts_rank(ts_sum(multiply(returns, returns < ts_quantile(returns, 60, "gaussian")), 60), 60) |
||||
rank(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),inverse(abs(returns)+1),0),60)) |
||||
ts_rank(ts_sum(multiply(returns, returns < ts_quantile(returns, 60, "gaussian")), 30), 60) |
||||
reverse(rank(ts_std_dev(if_else(returns < -0.01, returns, NaN), 60))) |
||||
rank(ts_sum(returns, 60)) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), ttm_return_on_average_equity) |
||||
ts_quantile(divide(ts_mean(volume, 60), cap), 60) |
||||
reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 120))) |
||||
if_else(and(rank(ts_scale(daily_volume_percent_shares_out,60))<0.1,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
||||
ts_delta(returns, 60) |
||||
reverse(rank(ts_std_dev(if_else(ts_delay(returns, 1) < 0, ts_delay(returns, 1), NaN), 60))) |
||||
ts_corr(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), returns, 60) |
||||
rank(divide(ts_mean(volume, 60), cap)) |
||||
if_else(and(rank(ts_mean(divide(volume,fnd17_float),40))<0.1,ts_arg_max(ts_sum(group_mean(returns,cap,market),5)<-0.04,150)<=30),ts_sum(returns,30),0) |
||||
rank(ts_mean(if_else(returns < ts_quantile(returns,95,gaussian),subtract(0,returns),0),95)) |
||||
normalize(group_rank(abs(ts_mean(if_else(returns < ts_quantile(returns,70,gaussian),returns,0),70)),country)) |
||||
reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))) |
||||
ts_rank(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_av_diff(ts_mean(volume, 60), 60) |
||||
ts_corr(divide(ts_mean(volume, 60), cap), returns, 60) |
||||
rank(subtract(ts_mean(returns, 60), ts_mean(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60))) |
||||
ts_decay_linear(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_quantile(ts_mean(volume, 60), 60) |
||||
ts_backfill(ts_mean(volume, 60), 60) |
||||
reverse(rank(ts_std_dev(if_else(abs(returns) > 0.05, returns, NaN), 60))) |
||||
reverse(rank(ts_std_dev(if_else(returns < subtract(ts_delay(ts_mean(returns, 60), 1), ts_delay(ts_std_dev(returns, 60), 1)), returns, NaN), 60))) |
||||
ts_corr(ts_mean(volume, 60), returns, 60) |
||||
ts_zscore(returns, 60) |
||||
zscore(ts_mean(if_else(returns < ts_delay(ts_mean(returns, 30), 5), returns, 0), 60)) |
||||
rank(ts_zscore(returns, 60)) |
||||
ts_count_nans(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
ts_delay(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 5) |
||||
rank(subtract(ts_sum(returns, 30), ts_sum(if_else(returns < ts_quantile(returns, 30, "gaussian"), returns, 0), 30))) |
||||
ts_rank(ts_sum(if_else(returns < ts_quantile(returns, 60, "cauchy"), returns, 0), 60), 120) |
||||
normalize(zscore(abs(ts_mean(if_else(returns < ts_quantile(returns,70,uniform),returns,0),70)))) |
||||
reverse(rank(ts_std_dev(if_else(returns < 0, ts_delay(returns, 1), NaN), 60))) |
||||
rank(ts_sum(if_else(returns < ts_delay(ts_quantile(returns, 60, "gaussian"), 1), returns, 0), 60)) |
||||
vec_avg(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
reverse(rank(ts_std_dev(if_else(returns < -0.03, returns, NaN), 60))) |
||||
ts_av_diff(divide(ts_mean(volume, 60), cap), 60) |
||||
group_zscore(rank(ts_mean(if_else(returns < ts_quantile(returns,65,uniform),returns,0),65)),sector) |
||||
reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 250))) |
||||
ts_covariance(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), returns, 60) |
||||
rank(abs(zscore(ts_mean(if_else(returns < ts_quantile(returns,80,gaussian),subtract(0,abs(returns)),0),80)))) |
||||
ts_arg_min(returns, 60) |
||||
reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 90))) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), min(10, abs(ttm_return_on_equity_percent))) |
||||
reverse(rank(ts_std_dev(if_else(returns < subtract(ts_mean(returns, 60), ts_std_dev(returns, 60)), returns, NaN), 60))) |
||||
rank(ts_corr(returns, returns, 60)) |
||||
ts_rank(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60, 0) |
||||
vec_min(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
rank(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),returns,0),60)) |
||||
rank(group_neutralize(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),ts_delta(returns,5),0),60),industry)) |
||||
group_scale(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), bucket(rank(returns), buckets="2,5,6,7,10")) |
||||
ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60) |
||||
rank(ts_target_tvr_decay(returns, lambda_min=0, lambda_max=1, target_tvr=0.1)) |
||||
ts_sum(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
signed_power(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), 2) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), add(1, anl45_risk_free_rate)) |
||||
vec_max(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
rank(ts_mean(if_else(returns < ts_delay(ts_quantile(returns, 60, "uniform"), 5), returns, 0), 60)) |
||||
rank(group_rank(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),ts_rank(returns,10),0),60),subindustry)) |
||||
ts_arg_max(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
zscore(ts_sum(multiply(returns, returns < ts_mean(returns, 60)), 60)) |
||||
zscore(rank(ts_mean(if_else(returns < ts_quantile(returns,70,cauchy),divide(returns,abs(returns)+1),0),70))) |
||||
ts_regression(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), returns, 60, 0, 0) |
||||
ts_rank(ts_sum(multiply(returns, returns < ts_quantile(returns, 120, "gaussian")), 60), 120) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), return_on_equity_ratio_3) |
||||
group_zscore(rank(abs(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),returns,0),60))),sector) |
||||
ts_count_nans(returns, 60) |
||||
quantile(ts_covariance(returns, returns, 60), driver="gaussian", sigma=1.0) |
||||
rank(group_rank(ts_mean(if_else(returns < ts_quantile(returns,60,gaussian),signed_power(returns,3),0),60),industry)) |
||||
rank(ts_covariance(returns, returns, 60)) |
||||
quantile(ts_arg_max(returns, 60), driver="gaussian", sigma=1.0) |
||||
group_neutralize(rank(ts_mean(if_else(returns < ts_quantile(returns,50,uniform),returns,0),50)),industry) |
||||
if_else(and(rank(ts_mean(daily_volume_percent_shares_out,60))<0.1,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
||||
ts_product(returns, 60) |
||||
ts_regression(returns, ts_mean(volume, 60), 60) |
||||
if_else(and(rank(ts_delta(daily_volume_percent_shares_out,60))<0.1,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), sign(quarterly_return_on_investment)) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), fnd86_risk_score) |
||||
ts_delay(divide(ts_mean(volume, 60), cap), 1) |
||||
ts_rank(ts_mean(multiply(returns, returns < ts_delay(ts_quantile(returns, 60, "uniform"), 3)), 60), 60) |
||||
group_neutralize(zscore(ts_mean(if_else(returns < ts_quantile(returns,75,gaussian),inverse(abs(returns)+2),0),75)),country) |
||||
rank(ts_delta(returns, 60)) |
||||
ts_target_tvr_delta_limit(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), returns, 0, 1, 0.1) |
||||
rank(ts_mean(if_else(subtract(returns,ts_quantile(returns,60)) < 0,returns,0),60)) |
||||
ts_product(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
rank(ts_arg_min(returns, 60)) |
||||
normalize(abs(group_zscore(ts_mean(if_else(returns < ts_quantile(returns,65,uniform),returns,0),65),subindustry))) |
||||
ts_product(ts_mean(volume, 60), 60) |
||||
ts_av_diff(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
normalize(ts_mean(if_else(returns < ts_quantile(returns,70,cauchy),log(abs(returns)+1),0),70)) |
||||
ts_mean(returns, 60) |
||||
ts_sum(ts_mean(volume, 60), 60) |
||||
ts_backfill(divide(ts_mean(volume, 60), cap), 60) |
||||
reverse(rank(ts_std_dev(if_else(returns < subtract(ts_mean(returns, 60), multiply(ts_std_dev(returns, 60), 2)), returns, NaN), 60))) |
||||
ts_zscore(ts_mean(volume, 60), 60) |
||||
ts_max(ts_mean(volume, 60), 60) |
||||
quantile(ts_zscore(returns, 60), driver="gaussian", sigma=1.0) |
||||
ts_mean(volume, 60) |
||||
ts_arg_min(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
rank(ts_mean(if_else(returns < ts_quantile(returns,85,gaussian),multiply(returns,100),0),85)) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), max(0.1, abs(quarterly_return_on_assets_percent))) |
||||
ts_backfill(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60, 1, "NAN") |
||||
ts_delta(ts_mean(volume, 60), 1) |
||||
if_else(and(rank(ts_av_diff(daily_volume_percent_shares_out,60))<0.1,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
||||
group_neutralize(normalize(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),add(returns,2),0),60)),country) |
||||
if_else(and(rank(ts_zscore(daily_volume_percent_shares_out,60))<0.1,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
||||
rank(ts_sum(if_else(returns < ts_quantile(returns, 60, "cauchy"), returns, 0), 30)) |
||||
ts_count_nans(ts_mean(volume, 60), 60) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), quarterly_return_on_investment_percent) |
||||
ts_std_dev(returns, 60) |
||||
ts_quantile(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60, "gaussian") |
||||
reverse(rank(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60))) |
||||
zscore(ts_mean(if_else(returns < ts_delay(ts_mean(returns, 60), 1), returns, 0), 60)) |
||||
rank(ts_mean(if_else(returns < ts_delay(ts_quantile(returns, 30, "uniform"), 2), returns, 0), 60)) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), quarterly_return_on_equity_percent_3) |
||||
ts_target_tvr_decay(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 0, 1, 0.1) |
||||
normalize(rank(abs(ts_mean(if_else(returns < ts_quantile(returns,80,cauchy),ts_std_dev(returns,10),0),80)))) |
||||
ts_decay_linear(ts_mean(volume, 60), 60) |
||||
ts_covariance(returns, returns, 60) |
||||
ts_scale(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60, 0) |
||||
rank(ts_mean(if_else(returns < ts_quantile(returns,60,gaussian),multiply(signed_power(returns,2),0.5),0),60)) |
||||
normalize(abs(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),add(returns,1),0),60))) |
||||
rank(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60)) |
||||
reverse(rank(ts_std_dev(if_else(returns < 0, signed_power(returns, 2), NaN), 60))) |
||||
reverse(rank(ts_decay_linear(if_else(returns < 0, returns, NaN), 60))) |
||||
rank(ts_arg_max(returns, 60)) |
||||
zscore(ts_sum(multiply(returns, returns < ts_quantile(returns, 60, "gaussian")), 60)) |
||||
if_else(and(rank(ts_mean(divide(volume,public_float_shares),50))<0.05,ts_arg_max(ts_sum(group_mean(returns,1,market),5)<-0.03,200)<=15),ts_sum(returns,15),0) |
||||
ts_sum(divide(ts_mean(volume, 60), cap), 60) |
||||
rank(ts_regression(returns, returns, 60, lag=0, rettype=0)) |
||||
divide(ts_mean(volume, 60), cap) |
||||
ts_delay(ts_mean(volume, 60), 1) |
||||
ts_std_dev(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_regression(returns, returns, 60, lag=0, rettype=0) |
||||
rank(zscore(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),signed_power(returns,1.2),0),60))) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), ttm_return_on_equity_percent_2) |
||||
ts_regression(returns, divide(ts_mean(volume, 60), cap), 60) |
||||
ts_sum(returns, 60) |
||||
ts_scale(ts_mean(volume, 60), 60) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), ts_step(1)) |
||||
zscore(ts_mean(if_else(returns < ts_quantile(returns,55,gaussian),sqrt(abs(returns)),0),55)) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), inverse(add(1, abs(ttm_return_on_equity_percent)))) |
||||
rank(ts_mean(if_else(returns < ts_quantile(returns,35,uniform),sign(returns)*power(abs(returns),2),0),35)) |
||||
ts_rank(returns, 60) |
||||
ts_arg_min(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_target_tvr_decay(returns, lambda_min=0, lambda_max=1, target_tvr=0.1) |
||||
reverse(rank(ts_std_dev(if_else(returns < -0.02, returns, NaN), 60))) |
||||
normalize(ts_mean(if_else(returns < ts_quantile(returns,80,gaussian),signed_power(returns,2),0),80)) |
||||
ts_rank(ts_mean(if_else(returns < ts_quantile(returns, 60, "uniform"), returns, 0), 60), 60) |
||||
quantile(ts_arg_min(returns, 60), driver="gaussian", sigma=1.0) |
||||
ts_covariance(ts_mean(volume, 60), returns, 60) |
||||
reverse(rank(ts_decay_linear(ts_std_dev(if_else(returns < 0, returns, NaN), 60), 20))) |
||||
ts_zscore(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_max(divide(ts_mean(volume, 60), cap), 60) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), log(add(1, abs(quarterly_return_on_assets_percent)))) |
||||
reverse(rank(ts_std_dev(if_else(returns < ts_mean(returns, 20), returns, NaN), 60))) |
||||
ts_arg_max(ts_mean(volume, 60), 60) |
||||
ts_delta(divide(ts_mean(volume, 60), cap), 1) |
||||
rank(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),max(returns,-0.5),0),60)) |
||||
if_else(and(rank(ts_product(daily_volume_percent_shares_out,60))<0.1,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
||||
normalize(group_zscore(ts_mean(if_else(returns < ts_quantile(returns,30,uniform),returns,0),30),industry)) |
||||
rank(ts_av_diff(returns, 60)) |
||||
group_rank(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), bucket(rank(returns), buckets="2,5,6,7,10")) |
||||
rank(group_zscore(abs(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),returns,0),60)),sector)) |
||||
if_else(and(rank(ts_mean(daily_volume_to_shares_outstanding,60))<0.15,ts_arg_max(ts_sum(vec_avg(returns),5)<-0.05,250)<=20),ts_sum(returns,20),0) |
||||
ts_av_diff(returns, 60) |
||||
group_zscore(zscore(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),returns,0),60)),sector) |
||||
group_backfill(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), bucket(rank(returns), buckets="2,5,6,7,10"), 60, 4.0) |
||||
group_mean(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), returns, bucket(rank(returns), buckets="2,5,6,7,10")) |
||||
rank(group_neutralize(ts_mean(if_else(returns < ts_quantile(returns,45,gaussian),returns,0),45),sector)) |
||||
ts_max(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
divide(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), add(ts_mean(abs(returns), 60), 0.001)) |
||||
group_rank(ts_mean(if_else(returns < ts_quantile(returns,75,gaussian),returns,0),75),subindustry) |
||||
multiply(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), sqrt(abs(quarterly_return_on_equity_percent))) |
||||
ts_mean(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 30) |
||||
ts_min(ts_mean(volume, 60), 60) |
||||
rank(subtract(ts_mean(returns, 90), ts_mean(if_else(returns < ts_quantile(returns, 90, "gaussian"), returns, 0), 90))) |
||||
zscore(ts_mean(if_else(returns < ts_quantile(returns,90,cauchy),returns,0),90)) |
||||
ts_rank(ts_mean(volume, 60), 60) |
||||
reverse(rank(ts_delay(ts_std_dev(if_else(returns < 0, returns, NaN), 60), 5))) |
||||
reverse(rank(ts_std_dev(if_else(abs(returns) > add(ts_mean(abs(returns), 60), ts_std_dev(abs(returns), 60)), returns, NaN), 60))) |
||||
reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 30))) |
||||
reverse(rank(ts_std_dev(if_else(returns < -0.05, returns, NaN), 60))) |
||||
rank(normalize(ts_mean(if_else(returns < ts_quantile(returns,90,gaussian),log(abs(returns)+3),0),90))) |
||||
if_else(and(rank(ts_decay_linear(divide(volume,cap),70))<0.1,ts_arg_max(ts_sum(vec_avg(returns),10)<-0.07,300)<=25),ts_sum(returns,25),0) |
||||
rank(ts_mean(if_else(returns < ts_quantile(returns,50,cauchy),power(abs(returns),0.8),0),50)) |
||||
ts_rank(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
add(reverse(rank(ts_std_dev(if_else(returns < 0, returns, NaN), 60))), quarterly_return_on_equity_percent) |
||||
zscore(group_neutralize(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),sqrt(abs(returns))*signed_power(returns,1),0),60),sector)) |
||||
ts_zscore(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 60) |
||||
ts_scale(divide(ts_mean(volume, 60), cap), 60) |
||||
ts_covariance(divide(ts_mean(volume, 60), cap), returns, 60) |
||||
ts_delta(ts_std_dev(if_else(returns < ts_quantile(returns, 60, "gaussian"), returns, 0), 60), 5) |
||||
ts_rank(ts_sum(if_else(returns < ts_quantile(returns, 90, "cauchy"), returns, 0), 90), 60) |
||||
group_rank(zscore(ts_mean(if_else(returns < ts_quantile(returns,60,cauchy),inverse(abs(returns)+3),0),60)),industry) |
||||
zscore(abs(ts_mean(if_else(returns < ts_quantile(returns,45,cauchy),signed_power(returns,0.5),0),45))) |
||||
rank(group_neutralize(abs(ts_mean(if_else(returns < ts_quantile(returns,60,uniform),returns,0),60)),subindustry)) |
||||
ts_mean(divide(ts_mean(volume, 60), cap), 60) |
||||
|
||||
@ -1,7 +1 @@ |
||||
['fund', 'holding', 'portfolio', 'consensus', 'eps', 'earnings', 'estimate', 'revision', 'change', 'expectation', 'momentum', 'institutional', 'analyst', 'weight', 'quarterly', 'report', 'outperformance', 'forecast'] |
||||
|
||||
Research Factor Name |
||||
Fund Quarterly Consensus Surprise Momentum |
||||
|
||||
Hypothesis |
||||
A fund manager's skill is reflected not only in the persistence of past performance but, more importantly, in the ability to anticipate and position ahead of changes in corporate earnings. When a fund's portfolio of top holdings exhibits a collective upward revision in future earnings estimates (e.g., next-quarter consensus EPS) that is significantly stronger than the market average following the quarterly report disclosure, it signals that the manager's stock selection is receiving immediate validation from professional sell-side analysts. This "consensus surprise," derived from institutional judgment, contains positive information not yet fully priced by the market. The gradual digestion of this information and subsequent capital inflows create a momentum effect, allowing funds with this characteristic to continue outperforming in the subsequent period. |
||||
['tail', 'risk', 'var', 'es', 'shortfall', 'loss', 'extreme', 'downside', 'volatility', 'return', 'drawdown', 'crash'] |
||||
Loading…
Reference in new issue