{"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":"-1771:input_data","from_node_id":"-360:data"},{"to_node_id":"-443:input_data","from_node_id":"-220:data"},{"to_node_id":"-220:instruments","from_node_id":"-226:data"},{"to_node_id":"-220:features","from_node_id":"-234:data"},{"to_node_id":"-443: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":"-2883:input_2","from_node_id":"-2864:data"},{"to_node_id":"-2864:instruments","from_node_id":"-2870:data"},{"to_node_id":"-2864:features","from_node_id":"-2878: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":"2021-12-21","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-01-04","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":"000001.SZA","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":"close_0\nclose_min=ts_min(close_0,10)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-59"}],"output_ports":[{"name":"data","node_id":"-59"}],"cacheable":true,"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":"0","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":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-360"},{"name":"features","node_id":"-360"}],"output_ports":[{"name":"data","node_id":"-360"}],"cacheable":true,"seq_num":7,"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":"-226","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2010-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2050-12-30","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":"-226"}],"output_ports":[{"name":"data","node_id":"-226"}],"cacheable":true,"seq_num":10,"comment":"","comment_collapsed":true},{"node_id":"-234","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"\n# #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_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":"False","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":"-2864","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":"-2864"},{"name":"features","node_id":"-2864"}],"output_ports":[{"name":"data","node_id":"-2864"}],"cacheable":true,"seq_num":17,"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":"2050-12-30","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":18,"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1月5日 00:42
# 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。


m1 = M.instruments.v2(
    start_date='2021-12-21',
    end_date=T.live_run_param('trading_date', '2022-01-04'),
    market='CN_STOCK_A',
    instrument_list='000001.SZA',
    max_count=0
)

m2 = M.input_features.v1(
    features="""close_0
close_min=ts_min(close_0,10)"""
)

m5 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=0,
    m_cached=False
)

m10 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2050-12-30',
    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_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={}
)

m18 = M.instruments.v2(
    start_date='2020-01-01',
    end_date='2050-12-30',
    market='CN_STOCK_A',
    instrument_list='000002.HIX',
    max_count=0
)

m19 = 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=m18.data,
    features=m19.data,
    datasource_id='bar1d_index_CN_STOCK_A',
    start_date='',
    end_date=''
)

m20 = 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=m20.data,
    on='date',
    how='left',
    sort=False
)

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={}
)

m23 = M.filter.v3(
    input_data=m7.data,
    expr='close_min>0',
    output_left_data=False
)

[2022-01-05 00:37:39.719630] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-01-05 00:37:39.733781] INFO: moduleinvoker: 命中缓存

[2022-01-05 00:37:39.735583] INFO: moduleinvoker: instruments.v2 运行完成[0.01596s].

[2022-01-05 00:37:39.739241] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-01-05 00:37:39.744717] INFO: moduleinvoker: 命中缓存

[2022-01-05 00:37:39.746207] INFO: moduleinvoker: input_features.v1 运行完成[0.006957s].

[2022-01-05 00:37:39.760897] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-01-05 00:37:41.855396] INFO: 基础特征抽取: 年份 2021, 特征行数=9

[2022-01-05 00:37:41.951491] INFO: 基础特征抽取: 年份 2022, 特征行数=1

[2022-01-05 00:37:41.991307] INFO: 基础特征抽取: 总行数: 10

[2022-01-05 00:37:41.993316] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[2.232448s].

[2022-01-05 00:37:41.998653] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-01-05 00:37:42.021213] INFO: moduleinvoker: instruments.v2 运行完成[0.022547s].

[2022-01-05 00:37:42.027602] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-01-05 00:37:42.035224] INFO: moduleinvoker: 命中缓存

[2022-01-05 00:37:42.037697] INFO: moduleinvoker: input_features.v1 运行完成[0.0101s].

[2022-01-05 00:37:42.044839] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-01-05 00:37:42.336603] INFO: moduleinvoker: use_datasource.v1 运行完成[0.291782s].

[2022-01-05 00:37:42.344458] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-01-05 00:37:42.392439] INFO: derived_feature_extractor: 提取完成 zs_zhangf=(close-open)/open, 0.001s

[2022-01-05 00:37:42.395043] INFO: derived_feature_extractor: 提取完成 zs_huiluo=(high-close)/close, 0.001s

[2022-01-05 00:37:42.397552] INFO: derived_feature_extractor: 提取完成 zs_huishen=(close-low)/low, 0.001s

[2022-01-05 00:37:42.403868] INFO: derived_feature_extractor: 提取完成 zs_zhenf=(high-low)/shift(close,1), 0.005s

[2022-01-05 00:37:42.409190] INFO: derived_feature_extractor: 提取完成 zs_volume_zf=volume/shift(volume,1), 0.004s

[2022-01-05 00:37:42.414583] INFO: derived_feature_extractor: 提取完成 zs_return_0=close/shift(close,1), 0.004s

[2022-01-05 00:37:42.420147] INFO: derived_feature_extractor: 提取完成 zs_return_1=shift(zs_return_0,1), 0.004s

[2022-01-05 00:37:42.424649] INFO: derived_feature_extractor: 提取完成 zs_return_2=shift(zs_return_0,2), 0.003s

[2022-01-05 00:37:42.430595] INFO: derived_feature_extractor: 提取完成 zs_max10=ts_max(close,10), 0.005s

[2022-01-05 00:37:42.445297] INFO: derived_feature_extractor: 提取完成 zs_max10d=ts_argmax(close,10), 0.013s

[2022-01-05 00:37:42.451421] INFO: derived_feature_extractor: 提取完成 zs_max30=ts_max(close,30), 0.005s

[2022-01-05 00:37:42.457178] INFO: derived_feature_extractor: 提取完成 zs_min10=ts_min(close,10), 0.004s

[2022-01-05 00:37:42.463614] INFO: derived_feature_extractor: 提取完成 zs_min30=ts_min(close,30), 0.005s

[2022-01-05 00:37:42.465968] INFO: derived_feature_extractor: 提取完成 zs_priceHighBl10=close/zs_max10, 0.001s

[2022-01-05 00:37:42.468029] INFO: derived_feature_extractor: 提取完成 zs_priceLowBl10=close/zs_min10, 0.001s

[2022-01-05 00:37:42.469902] INFO: derived_feature_extractor: 提取完成 zs_priceHighBl30=close/zs_max30, 0.001s

[2022-01-05 00:37:42.471910] INFO: derived_feature_extractor: 提取完成 zs_priceLowBl30=close/zs_min30, 0.001s

[2022-01-05 00:37:42.540706] INFO: derived_feature_extractor: /data, 2918

[2022-01-05 00:37:42.607234] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.262769s].

[2022-01-05 00:37:42.613523] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-01-05 00:37:42.633708] INFO: moduleinvoker: instruments.v2 运行完成[0.020188s].

[2022-01-05 00:37:42.638476] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-01-05 00:37:42.646341] INFO: moduleinvoker: 命中缓存

[2022-01-05 00:37:42.648217] INFO: moduleinvoker: input_features.v1 运行完成[0.009756s].

[2022-01-05 00:37:42.653678] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-01-05 00:37:42.811667] INFO: moduleinvoker: use_datasource.v1 运行完成[0.157994s].

[2022-01-05 00:37:42.823149] INFO: moduleinvoker: data_join.v3 开始运行..

[2022-01-05 00:37:43.032338] INFO: moduleinvoker: data_join.v3 运行完成[0.209196s].

[2022-01-05 00:37:43.041996] INFO: moduleinvoker: data_join.v3 开始运行..

[2022-01-05 00:37:43.243418] INFO: moduleinvoker: data_join.v3 运行完成[0.201426s].

[2022-01-05 00:37:43.252447] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-01-05 00:37:43.301539] INFO: derived_feature_extractor: 提取完成 close_min=ts_min(close_0,10), 0.004s

[2022-01-05 00:37:43.341217] INFO: derived_feature_extractor: /data, 10

[2022-01-05 00:37:43.395781] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.143322s].

[2022-01-05 00:37:43.410175] INFO: moduleinvoker: filter.v3 开始运行..

[2022-01-05 00:37:43.426658] INFO: filter: 使用表达式 close_min>0 过滤

[2022-01-05 00:37:43.502168] INFO: filter: 过滤 /data, 1/0/10

[2022-01-05 00:37:43.523521] INFO: moduleinvoker: filter.v3 运行完成[0.11335s].