{"description":"实验创建于2018/6/27","graph":{"edges":[{"to_node_id":"-353:instruments","from_node_id":"-51:data"},{"to_node_id":"-353:features","from_node_id":"-59:data"},{"to_node_id":"-360:features","from_node_id":"-59:data"},{"to_node_id":"-1372:input_1","from_node_id":"-353:data"},{"to_node_id":"-220:instruments","from_node_id":"-226:data"},{"to_node_id":"-443:features","from_node_id":"-234:data"},{"to_node_id":"-220:features","from_node_id":"-234:data"},{"to_node_id":"-2883:input_1","from_node_id":"-443:data"},{"to_node_id":"-360:input_data","from_node_id":"-1372:data"},{"to_node_id":"-443:input_data","from_node_id":"-220:data"},{"to_node_id":"-2864:instruments","from_node_id":"-2870:data"},{"to_node_id":"-2864:features","from_node_id":"-2878:data"},{"to_node_id":"-2883:input_2","from_node_id":"-2864:data"},{"to_node_id":"-1372:input_2","from_node_id":"-2883:data"}],"nodes":[{"node_id":"-51","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2020-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-12-31","type":"Literal","bound_global_parameter":"交易日期"},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":" ","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-51"}],"output_ports":[{"name":"data","node_id":"-51"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-59","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"return0=return_0\nreturn1=shift(return_0,1)#return_1\nreturn3=return_3\nmax10=ts_max(close_0,10)\nmax10d=ts_argmax(close_0,10)\nmin10=ts_min(close_0,10)\nmin10d=ts_argmin(close_0,10)\npriceHighBl10=close_0/max10\npriceLowBl10=close_0/min10\nmax30=ts_max(close_0,30)\nmax30d=ts_argmax(close_0,30)\nmin30=ts_min(close_0,30)\nmin30d=ts_argmin(close_0,30)\npriceHighBl30=close_0/max30\npriceLowBl30=close_0/min30\n\nddzb=mf_net_pct_l_0+mf_net_pct_main_0+mf_net_pct_xl_0\ndbzb30=sum(ddzb,30)\ndbzs_return=return0-zs_return_0\n# dbzs_return=return_0 - zs_return_0\n#连续上涨天数\nisShangZ0=where(return_0>1,1,0)\nlxsz_1d=where(sum(isShangZ0,1)==1,1,0)\nlxsz_2d=where(sum(isShangZ0,2)==2,2,0)\nlxsz_3d=where(sum(isShangZ0,3)==3,3,0)\nlxsz_4d=where(sum(isShangZ0,4)==4,4,0)\nlxsz_5d=where(sum(isShangZ0,5)==5,5,0)\nlxsz_6d=where(sum(isShangZ0,6)==6,6,0)\nlxsz_8d=where(sum(isShangZ0,8)==8,8,0)\nlxsz_10d=where(sum(isShangZ0,10)==10,10,0)\nlxsz_days=max(lxsz_1d,lxsz_2d,lxsz_3d,lxsz_4d,lxsz_5d,lxsz_6d,lxsz_8d,lxsz_10d)\n\nmy3=where((abs(return0-1.02)<0.01)&(return0>return1)&(lxsz_days==2)&((return0+return1)<2.04)&(return3<1)&(priceHighBl10<0.93)&(priceLowBl10<1.12)&(priceHighBl30==priceHighBl10)&(priceLowBl30==priceLowBl10)&(dbzs_return>0),1,0)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-59"}],"output_ports":[{"name":"data","node_id":"-59"}],"cacheable":false,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-353","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"90","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-353"},{"name":"features","node_id":"-353"}],"output_ports":[{"name":"data","node_id":"-353"}],"cacheable":false,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-360","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"import numpy as np\nimport pandas as pd\ndef maxdrawdown(df, data, period):\n def getMaxDrawdown(x):\n x = x.to_numpy()\n i = np.argmax(x)\n if i == len(x) - 1:\n return 0\n low = np.min(x[i:])\n d = (x[i] - low) / x[i] * 100\n return d\n return data.rolling(period).apply(getMaxDrawdown)\n\nbigquant_run = {\n 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#号开始的表示注释，注释需单独一行\n# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征\nzs_zhangf=(close-open)/open\nzs_open=open/shift(close,1)\nzs_huiluo=(high-close)/close\nzs_huishen=(close-low)/low\nzs_zhenf=(high-low)/shift(close,1)\nzs_volume_zf=volume/shift(volume,1)\nzs_return_0=close/shift(close,1)\nzs_return_1=shift(zs_return_0,1)\nzs_return_2=shift(zs_return_0,2)\nzs_max10=ts_max(close,10)\nzs_max10d=ts_argmax(close,10)\nzs_max30=ts_max(close,30)\n#zs_max30d=ts_argmax(close,30)\nzs_min10=ts_min(close,10)\n#zs_min10d=ts_argmin(close,10)\nzs_min30=ts_min(close,30)\n#zs_min30d=ts_argmin(close,30)\nzs_priceHighBl10=close/zs_max10\nzs_priceLowBl10=close/zs_min10\nzs_priceHighBl30=close/zs_max30\nzs_priceLowBl30=close/zs_min30","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-234"}],"output_ports":[{"name":"data","node_id":"-234"}],"cacheable":true,"seq_num":12,"comment":"","comment_collapsed":true},{"node_id":"-443","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-443"},{"name":"features","node_id":"-443"}],"output_ports":[{"name":"data","node_id":"-443"}],"cacheable":true,"seq_num":13,"comment":"","comment_collapsed":true},{"node_id":"-1372","module_id":"BigQuantSpace.data_join.data_join-v3","parameters":[{"name":"on","value":"date","type":"Literal","bound_global_parameter":null},{"name":"how","value":"left","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-1372"},{"name":"input_2","node_id":"-1372"}],"output_ports":[{"name":"data","node_id":"-1372"}],"cacheable":true,"seq_num":14,"comment":"","comment_collapsed":true},{"node_id":"-220","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"bar1d_index_CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-220"},{"name":"features","node_id":"-220"}],"output_ports":[{"name":"data","node_id":"-220"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-2870","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2020-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-12-30","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"000002.HIX","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-2870"}],"output_ports":[{"name":"data","node_id":"-2870"}],"cacheable":true,"seq_num":15,"comment":"","comment_collapsed":true},{"node_id":"-2878","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"\n# 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# 本代码由可视化策略环境自动生成 2022年7月29日 13:56
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


import numpy as np
import pandas as pd
def maxdrawdown(df, data, period):
    def getMaxDrawdown(x):
        x = x.to_numpy()
        i = np.argmax(x)
        if i == len(x) - 1:
            return 0
        low = np.min(x[i:])
        d = (x[i] - low) / x[i] * 100
        return d
    return data.rolling(period).apply(getMaxDrawdown)

m7_user_functions_bigquant_run = {
    'maxdrawdown':maxdrawdown
}

m1 = M.instruments.v2(
    start_date='2020-01-01',
    end_date=T.live_run_param('trading_date', '2020-12-31'),
    market='CN_STOCK_A',
    instrument_list='        ',
    max_count=0
)

m2 = M.input_features.v1(
    features="""return0=return_0
return1=shift(return_0,1)#return_1
return3=return_3
max10=ts_max(close_0,10)
max10d=ts_argmax(close_0,10)
min10=ts_min(close_0,10)
min10d=ts_argmin(close_0,10)
priceHighBl10=close_0/max10
priceLowBl10=close_0/min10
max30=ts_max(close_0,30)
max30d=ts_argmax(close_0,30)
min30=ts_min(close_0,30)
min30d=ts_argmin(close_0,30)
priceHighBl30=close_0/max30
priceLowBl30=close_0/min30

ddzb=mf_net_pct_l_0+mf_net_pct_main_0+mf_net_pct_xl_0
dbzb30=sum(ddzb,30)
dbzs_return=return0-zs_return_0
# dbzs_return=return_0 - zs_return_0
#连续上涨天数
isShangZ0=where(return_0>1,1,0)
lxsz_1d=where(sum(isShangZ0,1)==1,1,0)
lxsz_2d=where(sum(isShangZ0,2)==2,2,0)
lxsz_3d=where(sum(isShangZ0,3)==3,3,0)
lxsz_4d=where(sum(isShangZ0,4)==4,4,0)
lxsz_5d=where(sum(isShangZ0,5)==5,5,0)
lxsz_6d=where(sum(isShangZ0,6)==6,6,0)
lxsz_8d=where(sum(isShangZ0,8)==8,8,0)
lxsz_10d=where(sum(isShangZ0,10)==10,10,0)
lxsz_days=max(lxsz_1d,lxsz_2d,lxsz_3d,lxsz_4d,lxsz_5d,lxsz_6d,lxsz_8d,lxsz_10d)

my3=where((abs(return0-1.02)<0.01)&(return0>return1)&(lxsz_days==2)&((return0+return1)<2.04)&(return3<1)&(priceHighBl10<0.93)&(priceLowBl10<1.12)&(priceHighBl30==priceHighBl10)&(priceLowBl30==priceLowBl10)&(dbzs_return>0),1,0)""",
    m_cached=False
)

m5 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90,
    m_cached=False
)

m10 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2050-01-01',
    market='CN_STOCK_A',
    instrument_list='000002.HIX',
    max_count=0
)

m12 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
zs_zhangf=(close-open)/open
zs_open=open/shift(close,1)
zs_huiluo=(high-close)/close
zs_huishen=(close-low)/low
zs_zhenf=(high-low)/shift(close,1)
zs_volume_zf=volume/shift(volume,1)
zs_return_0=close/shift(close,1)
zs_return_1=shift(zs_return_0,1)
zs_return_2=shift(zs_return_0,2)
zs_max10=ts_max(close,10)
zs_max10d=ts_argmax(close,10)
zs_max30=ts_max(close,30)
#zs_max30d=ts_argmax(close,30)
zs_min10=ts_min(close,10)
#zs_min10d=ts_argmin(close,10)
zs_min30=ts_min(close,30)
#zs_min30d=ts_argmin(close,30)
zs_priceHighBl10=close/zs_max10
zs_priceLowBl10=close/zs_min10
zs_priceHighBl30=close/zs_max30
zs_priceLowBl30=close/zs_min30"""
)

m4 = M.use_datasource.v1(
    instruments=m10.data,
    features=m12.data,
    datasource_id='bar1d_index_CN_STOCK_A',
    start_date='',
    end_date=''
)

m13 = M.derived_feature_extractor.v3(
    input_data=m4.data,
    features=m12.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=True,
    user_functions={}
)

m15 = M.instruments.v2(
    start_date='2020-01-01',
    end_date='2020-12-30',
    market='CN_STOCK_A',
    instrument_list='000002.HIX',
    max_count=0
)

m16 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
close
"""
)

m17 = M.use_datasource.v1(
    instruments=m15.data,
    features=m16.data,
    datasource_id='bar1d_index_CN_STOCK_A',
    start_date='',
    end_date=''
)

m18 = M.data_join.v3(
    input_1=m13.data,
    input_2=m17.data,
    on='date',
    how='left',
    sort=False
)

m14 = M.data_join.v3(
    input_1=m5.data,
    input_2=m18.data,
    on='date',
    how='left',
    sort=True
)

m7 = M.derived_feature_extractor.v3(
    input_data=m14.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=True,
    user_functions=m7_user_functions_bigquant_run,
    m_cached=False
)

[2022-07-29 13:48:34.946255] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-07-29 13:48:34.955119] INFO: moduleinvoker: 命中缓存

[2022-07-29 13:48:34.957982] INFO: moduleinvoker: instruments.v2 运行完成[0.011726s].

[2022-07-29 13:48:34.971124] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-07-29 13:48:35.003600] INFO: moduleinvoker: input_features.v1 运行完成[0.032489s].

[2022-07-29 13:48:35.033704] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-07-29 13:48:35.076285] WARNING: bigdatasource: cannot find filed [zs_return_0] table in field_table_map!

[2022-07-29 13:48:37.365920] WARNING: bigdatasource: unknown fields: ['zs_return_0']

[2022-07-29 13:48:37.595926] INFO: 基础特征抽取: 年份 2019, 特征行数=225729

[2022-07-29 13:48:37.605347] WARNING: bigdatasource: cannot find filed [zs_return_0] table in field_table_map!

[2022-07-29 13:48:43.102261] WARNING: bigdatasource: unknown fields: ['zs_return_0']

[2022-07-29 13:48:44.258940] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2022-07-29 13:48:44.405679] INFO: 基础特征抽取: 总行数: 1171690

[2022-07-29 13:48:44.413420] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[9.379759s].

[2022-07-29 13:48:44.456127] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-07-29 13:48:44.506196] INFO: moduleinvoker: instruments.v2 运行完成[0.050075s].

[2022-07-29 13:48:44.532639] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-07-29 13:48:44.586700] INFO: moduleinvoker: 命中缓存

[2022-07-29 13:48:44.589798] INFO: moduleinvoker: input_features.v1 运行完成[0.057166s].

[2022-07-29 13:48:44.607782] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-07-29 13:48:45.556612] INFO: moduleinvoker: use_datasource.v1 运行完成[0.948844s].

[2022-07-29 13:48:45.582828] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-07-29 13:48:45.654136] INFO: derived_feature_extractor: 提取完成 zs_zhangf=(close-open)/open, 0.002s

[2022-07-29 13:48:45.662158] INFO: derived_feature_extractor: 提取完成 zs_open=open/shift(close,1), 0.006s

[2022-07-29 13:48:45.665750] INFO: derived_feature_extractor: 提取完成 zs_huiluo=(high-close)/close, 0.002s

[2022-07-29 13:48:45.669249] INFO: derived_feature_extractor: 提取完成 zs_huishen=(close-low)/low, 0.002s

[2022-07-29 13:48:45.676798] INFO: derived_feature_extractor: 提取完成 zs_zhenf=(high-low)/shift(close,1), 0.006s

[2022-07-29 13:48:45.683889] INFO: derived_feature_extractor: 提取完成 zs_volume_zf=volume/shift(volume,1), 0.005s

[2022-07-29 13:48:45.710769] INFO: derived_feature_extractor: 提取完成 zs_return_0=close/shift(close,1), 0.007s

[2022-07-29 13:48:45.718734] INFO: derived_feature_extractor: 提取完成 zs_return_1=shift(zs_return_0,1), 0.005s

[2022-07-29 13:48:45.725268] INFO: derived_feature_extractor: 提取完成 zs_return_2=shift(zs_return_0,2), 0.005s

[2022-07-29 13:48:45.734248] INFO: derived_feature_extractor: 提取完成 zs_max10=ts_max(close,10), 0.007s

[2022-07-29 13:48:45.756970] INFO: derived_feature_extractor: 提取完成 zs_max10d=ts_argmax(close,10), 0.020s

[2022-07-29 13:48:45.765872] INFO: derived_feature_extractor: 提取完成 zs_max30=ts_max(close,30), 0.007s

[2022-07-29 13:48:45.805673] INFO: derived_feature_extractor: 提取完成 zs_min10=ts_min(close,10), 0.037s

[2022-07-29 13:48:45.815633] INFO: derived_feature_extractor: 提取完成 zs_min30=ts_min(close,30), 0.007s

[2022-07-29 13:48:45.821295] INFO: derived_feature_extractor: 提取完成 zs_priceHighBl10=close/zs_max10, 0.003s

[2022-07-29 13:48:45.825206] INFO: derived_feature_extractor: 提取完成 zs_priceLowBl10=close/zs_min10, 0.002s

[2022-07-29 13:48:45.829057] INFO: derived_feature_extractor: 提取完成 zs_priceHighBl30=close/zs_max30, 0.002s

[2022-07-29 13:48:45.832780] INFO: derived_feature_extractor: 提取完成 zs_priceLowBl30=close/zs_min30, 0.002s

[2022-07-29 13:48:45.900837] INFO: derived_feature_extractor: /data, 3054

[2022-07-29 13:48:45.967517] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.384673s].

[2022-07-29 13:48:45.979577] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-07-29 13:48:45.987344] INFO: moduleinvoker: 命中缓存

[2022-07-29 13:48:45.989091] INFO: moduleinvoker: instruments.v2 运行完成[0.009538s].

[2022-07-29 13:48:45.994231] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-07-29 13:48:46.000544] INFO: moduleinvoker: 命中缓存

[2022-07-29 13:48:46.002021] INFO: moduleinvoker: input_features.v1 运行完成[0.007793s].

[2022-07-29 13:48:46.008964] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-07-29 13:48:46.076120] INFO: moduleinvoker: 命中缓存

[2022-07-29 13:48:46.078563] INFO: moduleinvoker: use_datasource.v1 运行完成[0.069609s].

[2022-07-29 13:48:46.095857] INFO: moduleinvoker: data_join.v3 开始运行..

[2022-07-29 13:48:46.407749] INFO: moduleinvoker: data_join.v3 运行完成[0.311892s].

[2022-07-29 13:48:46.424263] INFO: moduleinvoker: data_join.v3 开始运行..

[2022-07-29 13:48:48.926080] INFO: moduleinvoker: data_join.v3 运行完成[2.501823s].

[2022-07-29 13:48:48.945031] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-07-29 13:48:57.258400] INFO: derived_feature_extractor: 提取完成 return0=return_0, 0.003s

[2022-07-29 13:48:57.504667] INFO: derived_feature_extractor: 提取完成 return1=shift(return_0,1)#return_1, 0.241s

[2022-07-29 13:48:57.509963] INFO: derived_feature_extractor: 提取完成 return3=return_3, 0.002s

[2022-07-29 13:48:58.972527] INFO: derived_feature_extractor: 提取完成 max10=ts_max(close_0,10), 1.460s

[2022-07-29 13:49:04.907738] INFO: derived_feature_extractor: 提取完成 max10d=ts_argmax(close_0,10), 5.933s

[2022-07-29 13:49:06.420224] INFO: derived_feature_extractor: 提取完成 min10=ts_min(close_0,10), 1.510s

[2022-07-29 13:49:13.472029] INFO: derived_feature_extractor: 提取完成 min10d=ts_argmin(close_0,10), 7.050s

[2022-07-29 13:49:13.482385] INFO: derived_feature_extractor: 提取完成 priceHighBl10=close_0/max10, 0.008s

[2022-07-29 13:49:13.505812] INFO: derived_feature_extractor: 提取完成 priceLowBl10=close_0/min10, 0.021s

[2022-07-29 13:49:15.154849] INFO: derived_feature_extractor: 提取完成 max30=ts_max(close_0,30), 1.647s

[2022-07-29 13:49:21.399986] INFO: derived_feature_extractor: 提取完成 max30d=ts_argmax(close_0,30), 6.241s

[2022-07-29 13:49:23.062926] INFO: derived_feature_extractor: 提取完成 min30=ts_min(close_0,30), 1.659s

[2022-07-29 13:49:28.993740] INFO: derived_feature_extractor: 提取完成 min30d=ts_argmin(close_0,30), 5.927s

[2022-07-29 13:49:29.006155] INFO: derived_feature_extractor: 提取完成 priceHighBl30=close_0/max30, 0.011s

[2022-07-29 13:49:29.017787] INFO: derived_feature_extractor: 提取完成 priceLowBl30=close_0/min30, 0.009s

[2022-07-29 13:49:29.029702] INFO: derived_feature_extractor: 提取完成 ddzb=mf_net_pct_l_0+mf_net_pct_main_0+mf_net_pct_xl_0, 0.008s

[2022-07-29 13:49:30.506152] INFO: derived_feature_extractor: 提取完成 dbzb30=sum(ddzb,30), 1.468s

[2022-07-29 13:49:30.512517] INFO: derived_feature_extractor: 提取完成 dbzs_return=return0-zs_return_0, 0.004s

[2022-07-29 13:49:30.519406] INFO: derived_feature_extractor: 提取完成 isShangZ0=where(return_0>1,1,0), 0.005s

[2022-07-29 13:49:32.037535] INFO: derived_feature_extractor: 提取完成 lxsz_1d=where(sum(isShangZ0,1)==1,1,0), 1.516s

[2022-07-29 13:49:33.576637] INFO: derived_feature_extractor: 提取完成 lxsz_2d=where(sum(isShangZ0,2)==2,2,0), 1.537s

[2022-07-29 13:49:35.053777] INFO: derived_feature_extractor: 提取完成 lxsz_3d=where(sum(isShangZ0,3)==3,3,0), 1.475s

[2022-07-29 13:49:36.827421] INFO: derived_feature_extractor: 提取完成 lxsz_4d=where(sum(isShangZ0,4)==4,4,0), 1.771s

[2022-07-29 13:49:38.404873] INFO: derived_feature_extractor: 提取完成 lxsz_5d=where(sum(isShangZ0,5)==5,5,0), 1.575s

[2022-07-29 13:49:39.909454] INFO: derived_feature_extractor: 提取完成 lxsz_6d=where(sum(isShangZ0,6)==6,6,0), 1.502s

[2022-07-29 13:49:41.490751] INFO: derived_feature_extractor: 提取完成 lxsz_8d=where(sum(isShangZ0,8)==8,8,0), 1.579s

[2022-07-29 13:49:42.922559] INFO: derived_feature_extractor: 提取完成 lxsz_10d=where(sum(isShangZ0,10)==10,10,0), 1.430s

[2022-07-29 13:49:42.945342] INFO: derived_feature_extractor: 提取完成 lxsz_days=max(lxsz_1d,lxsz_2d,lxsz_3d,lxsz_4d,lxsz_5d,lxsz_6d,lxsz_8d,lxsz_10d), 0.020s

[2022-07-29 13:49:43.007266] INFO: derived_feature_extractor: 提取完成 my3=where((abs(return0-1.02)<0.01)&(return0>return1)&(lxsz_days==2)&((return0+return1)<2.04)&(return3<1)&(priceHighBl10<0.93)&(priceLowBl10<1.12)&(priceHighBl30==priceHighBl10)&(priceLowBl30==priceLowBl10)&(dbzs_return>0),1,0), 0.060s

[2022-07-29 13:49:51.551516] INFO: derived_feature_extractor: /data, 1171690

[2022-07-29 13:49:53.550679] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[64.605628s].