{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-215:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-3201:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-11425:features_ds","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-236:input_1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:model"},{"to_node_id":"-11430:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"to_node_id":"-329:data1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"to_node_id":"-231:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-175:input_1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-224:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-1918:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:training_ds","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:test_ds","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","from_node_id":"-86:data"},{"to_node_id":"-323:input_ds","from_node_id":"-86:data"},{"to_node_id":"-222:input_data","from_node_id":"-215:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","from_node_id":"-222:data"},{"to_node_id":"-238:input_data","from_node_id":"-231:data"},{"to_node_id":"-3234:data1","from_node_id":"-238:data"},{"to_node_id":"-9894:instruments","from_node_id":"-184:data"},{"to_node_id":"-9894:features","from_node_id":"-150:data"},{"to_node_id":"-242:columns_ds","from_node_id":"-150:data"},{"to_node_id":"-204:instruments","from_node_id":"-154:data"},{"to_node_id":"-266:columns_ds","from_node_id":"-131:data"},{"to_node_id":"-204:features","from_node_id":"-131:data"},{"to_node_id":"-228:input_data","from_node_id":"-9894:data"},{"to_node_id":"-228:features","from_node_id":"-271:data"},{"to_node_id":"-242:input_ds","from_node_id":"-228:data"},{"to_node_id":"-197:data1","from_node_id":"-242:data"},{"to_node_id":"-528:input_data","from_node_id":"-197:data"},{"to_node_id":"-6195:input_data","from_node_id":"-528:data"},{"to_node_id":"-528:features","from_node_id":"-546:data"},{"to_node_id":"-197:data2","from_node_id":"-266:data"},{"to_node_id":"-266:input_ds","from_node_id":"-248:data"},{"to_node_id":"-248:input_data","from_node_id":"-204:data"},{"to_node_id":"-248:features","from_node_id":"-256:data"},{"to_node_id":"-1162:input_ds","from_node_id":"-6195:data"},{"to_node_id":"-3194:data2","from_node_id":"-1162:data"},{"to_node_id":"-3325:input_data","from_node_id":"-3194:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","from_node_id":"-3325:data"},{"to_node_id":"-3194:data1","from_node_id":"-3201:data"},{"to_node_id":"-9978:instruments","from_node_id":"-9965:data"},{"to_node_id":"-9978:features","from_node_id":"-9973:data"},{"to_node_id":"-9998:columns_ds","from_node_id":"-9973:data"},{"to_node_id":"-9989:input_data","from_node_id":"-9978:data"},{"to_node_id":"-10019:instruments","from_node_id":"-10010:data"},{"to_node_id":"-9989:features","from_node_id":"-9984:data"},{"to_node_id":"-9998:input_ds","from_node_id":"-9989:data"},{"to_node_id":"-10004:data1","from_node_id":"-9998:data"},{"to_node_id":"-10053:input_data","from_node_id":"-10004:data"},{"to_node_id":"-10034:input_data","from_node_id":"-10019:data"},{"to_node_id":"-10019:features","from_node_id":"-10025:data"},{"to_node_id":"-10043:columns_ds","from_node_id":"-10025:data"},{"to_node_id":"-10034:features","from_node_id":"-10029:data"},{"to_node_id":"-10043:input_ds","from_node_id":"-10034:data"},{"to_node_id":"-10004:data2","from_node_id":"-10043:data"},{"to_node_id":"-10053:features","from_node_id":"-10048:data"},{"to_node_id":"-6189:input_data","from_node_id":"-10053:data"},{"to_node_id":"-3228:input_ds","from_node_id":"-6189:data"},{"to_node_id":"-3234:data2","from_node_id":"-3228:data"},{"to_node_id":"-11436:input_data","from_node_id":"-3234:data"},{"to_node_id":"-215:features","from_node_id":"-11425:data"},{"to_node_id":"-222:features","from_node_id":"-11425:data"},{"to_node_id":"-231:features","from_node_id":"-11425:data"},{"to_node_id":"-238:features","from_node_id":"-11425:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","from_node_id":"-11430:data"},{"to_node_id":"-86:input_data","from_node_id":"-11436:data"},{"to_node_id":"-175:input_2","from_node_id":"-12762:data"},{"to_node_id":"-196:input_ds","from_node_id":"-175:data_1"},{"to_node_id":"-189:data2","from_node_id":"-196:data"},{"to_node_id":"-202:data2","from_node_id":"-189:data"},{"to_node_id":"-189:data1","from_node_id":"-224:data"},{"to_node_id":"-224:features","from_node_id":"-148:data"},{"to_node_id":"-490:input_ds","from_node_id":"-202:data"},{"to_node_id":"-1918:options_data","from_node_id":"-490:sorted_data"},{"to_node_id":"-329:data2","from_node_id":"-323:data"},{"to_node_id":"-202:data1","from_node_id":"-329:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:features","from_node_id":"-236:data_1"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2010-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2018-01-01","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"002714.SZA\n000620.SZA\n600066.SHA\n600887.SHA\n002415.SZA\n300450.SZA\n000622.SZA\n002690.SZA\n300601.SZA\n000776.SZA\n600519.SHA\n000333.SZA\n002035.SZA\n002236.SZA\n300015.SZA\n600436.SHA\n600566.SHA\n600201.SHA\n600705.SHA\n000156.SZA\n002677.SZA\n002032.SZA\n600681.SHA\n603601.SHA\n002372.SZA\n603019.SHA\n300571.SZA\n002475.SZA\n300033.SZA\n600346.SHA\n601888.SHA\n600763.SHA\n603799.SHA\n603690.SHA\n002044.SZA\n300253.SZA\n000651.SZA\n002085.SZA\n600903.SHA\n600340.SHA\n300296.SZA\n002572.SZA\n300176.SZA\n000963.SZA\n000403.SZA\n002747.SZA\n000789.SZA\n002508.SZA\n300347.SZA\n300401.SZA\n300666.SZA\n000732.SZA\n\t\n000049.SZA\n300451.SZA\n603986.SHA\n300684.SZA\n000681.SZA\n000671.SZA\n002230.SZA\n600867.SHA\n300059.SZA\n002020.SZA\n300457.SZA\n300136.SZA\n300308.SZA\n000661.SZA\n002304.SZA\n600276.SHA\n600563.SHA\n601012.SHA\n000703.SZA\n002410.SZA\n002358.SZA\n300725.SZA\n300226.SZA\n300675.SZA\n300324.SZA\n600801.SHA\n000048.SZA\n300383.SZA\n300285.SZA\n300459.SZA\n002008.SZA\n603288.SHA\n002049.SZA\n002311.SZA\n600305.SHA\n300377.SZA\n002746.SZA\n603638.SHA\n002507.SZA\n002252.SZA\n002271.SZA\n300482.SZA\n000002.SZA\n600612.SHA\n002081.SZA\n002013.SZA\n300357.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":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"# #号开始的表示注释\n# 多个特征，每行一个，可以包含基础特征和衍生特征\n\n\navg_turn_15/turn_0\nmf_net_amount_xl_0\n\nalpha4=close_0*avg_turn_0+close_1*avg_turn_1+close_2*avg_turn_2\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43","module_id":"BigQuantSpace.stock_ranker_train.stock_ranker_train-v5","parameters":[{"name":"learning_algorithm","value":"排序","type":"Literal","bound_global_parameter":null},{"name":"number_of_leaves","value":"20","type":"Literal","bound_global_parameter":null},{"name":"minimum_docs_per_leaf","value":1000,"type":"Literal","bound_global_parameter":null},{"name":"number_of_trees","value":"70","type":"Literal","bound_global_parameter":null},{"name":"learning_rate","value":0.1,"type":"Literal","bound_global_parameter":null},{"name":"max_bins","value":1023,"type":"Literal","bound_global_parameter":null},{"name":"feature_fraction","value":1,"type":"Literal","bound_global_parameter":null},{"name":"m_lazy_run","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"training_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"features","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"test_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"base_model","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"}],"output_ports":[{"name":"model","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"feature_gains","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"m_lazy_run","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"},{"name":"data2","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","module_id":"BigQuantSpace.stock_ranker_predict.stock_ranker_predict-v5","parameters":[{"name":"m_lazy_run","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"model","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"},{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"}],"output_ports":[{"name":"predictions","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"},{"name":"m_lazy_run","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"}],"cacheable":true,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2018-01-01","type":"Literal","bound_global_parameter":"交易日期"},{"name":"end_date","value":"2021-10-30","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":"300782.SZA\n605358.SHA\n603290.SHA\n603392.SHA\n601865.SHA\n300759.SZA\n300750.SZA\n300677.SZA\n002607.SZA\n603259.SHA\n300751.SZA\n603613.SHA\n601100.SHA\n300763.SZA\n002568.SZA\n300724.SZA\n603345.SHA\n600763.SHA\n603713.SHA\n300595.SZA\n300014.SZA\n603712.SHA\n300760.SZA\n603317.SHA\n002791.SZA\n601066.SHA\n002985.SZA\n300661.SZA\n300347.SZA\n300777.SZA\n603129.SHA\n300454.SZA\n601888.SHA\n605111.SHA\n603638.SHA\n300850.SZA\n600809.SHA\n002414.SZA\n603893.SHA\n002967.SZA\n600132.SHA\n603605.SHA\n300015.SZA\n603267.SHA\n300012.SZA\n600882.SHA\n300684.SZA\n300390.SZA\n300769.SZA\n\t\n300748.SZA\n000799.SZA\n300767.SZA\n300775.SZA\n603737.SHA\n300601.SZA\n601698.SHA\n300841.SZA\n002975.SZA\n603501.SHA\n300122.SZA\n300677.SZA\n603392.SHA\n002791.SZA\n601865.SHA\n300759.SZA\n002568.SZA\n603613.SHA\n300014.SZA\n601100.SHA\n300763.SZA\n300274.SZA\n601633.SHA\n603501.SHA\n002709.SZA\n603317.SHA\n300661.SZA\n002985.SZA\n600882.SHA\n300598.SZA\n300777.SZA\n300552.SZA\n300346.SZA\n002475.SZA\n605111.SHA\n300850.SZA\n300526.SZA\n601012.SHA\n603893.SHA\n002967.SZA\n000858.SZA\n603267.SHA\n000568.SZA\n603638.SHA\n000708.SZA\n603456.SHA\n000995.SZA\n600399.SHA\n300767.SZA\n300595.SZA\n300347.SZA\n600763.SHA\n300751.SZA\n600316.SHA\n300775.SZA\n603208.SHA\n600862.SHA\n002241.SZA\n002706.SZA\n300390.SZA\n601698.SHA\n002541.SZA\n002607.SZA\n000733.SZA\n000596.SZA\n603345.SHA\n300151.SZA\n300496.SZA\n002705.SZA\n002756.SZA\n603185.SHA\n002850.SZA\n000661.SZA\n002600.SZA\n300724.SZA\n600584.SHA\n002414.SZA\n300223.SZA\n002920.SZA\n603906.SHA\n002714.SZA\n600966.SHA\n300083.SZA\n300601.SZA\n600438.SHA\n002812.SZA\n002459.SZA\n603027.SHA\n300015.SZA\n300763.SZA\n002709.SZA\n688202.SHA\n000422.SZA\n300769.SZA\n300751.SZA\n300343.SZA\n605117.SHA\n300827.SZA\n601633.SHA\n603026.SHA\n002240.SZA\n002326.SZA\n002487.SZA\n000762.SZA\n300432.SZA\n603396.SHA\n300363.SZA\n603985.SHA\n000155.SZA\n002594.SZA\n600399.SHA\n600702.SHA\n300171.SZA\n002176.SZA\n000733.SZA\n300750.SZA\n601127.SHA\n002812.SZA\n603260.SHA\n600610.SHA\n601012.SHA\n003022.SZA\n603127.SHA\n000301.SZA\n002585.SZA\n688198.SHA\n002245.SZA\n300693.SZA\n600096.SHA\n300568.SZA\n300382.SZA\n300443.SZA\n003031.SZA\n605376.SHA\n603806.SHA\n603223.SHA\n688116.SHA\n002529.SZA\n600141.SHA\n600956.SHA\n300035.SZA\n300316.SZA\n002460.SZA\n600110.SHA\n300671.SZA\n002407.SZA\n600532.SHA\n688599.SHA\n002472.SZA\n600499.SHA\n600111.SHA\n600884.SHA\n300696.SZA\n603267.SHA","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"cacheable":true,"seq_num":9,"comment":"预测数据，用于回测和模拟","comment_collapsed":false},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84","module_id":"BigQuantSpace.dropnan.dropnan-v1","parameters":[],"input_ports":[{"name":"input_data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84"}],"cacheable":true,"seq_num":13,"comment":"","comment_collapsed":true},{"node_id":"-86","module_id":"BigQuantSpace.dropnan.dropnan-v1","parameters":[],"input_ports":[{"name":"input_data","node_id":"-86"}],"output_ports":[{"name":"data","node_id":"-86"}],"cacheable":true,"seq_num":14,"comment":"","comment_collapsed":true},{"node_id":"-215","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":"-215"},{"name":"features","node_id":"-215"}],"output_ports":[{"name":"data","node_id":"-215"}],"cacheable":true,"seq_num":15,"comment":"","comment_collapsed":true},{"node_id":"-222","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":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-222"},{"name":"features","node_id":"-222"}],"output_ports":[{"name":"data","node_id":"-222"}],"cacheable":true,"seq_num":16,"comment":"","comment_collapsed":true},{"node_id":"-231","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":"-231"},{"name":"features","node_id":"-231"}],"output_ports":[{"name":"data","node_id":"-231"}],"cacheable":true,"seq_num":17,"comment":"","comment_collapsed":true},{"node_id":"-238","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":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-238"},{"name":"features","node_id":"-238"}],"output_ports":[{"name":"data","node_id":"-238"}],"cacheable":true,"seq_num":18,"comment":"","comment_collapsed":true},{"node_id":"-184","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2010-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2019-01-01","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"000300.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":"-184"}],"output_ports":[{"name":"data","node_id":"-184"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-150","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\nclose\ninstrument\n\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-150"}],"output_ports":[{"name":"data","node_id":"-150"}],"cacheable":true,"seq_num":10,"comment":"","comment_collapsed":true},{"node_id":"-154","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2010-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2019-01-01","type":"Literal","bound_global_parameter":null},{"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":"-154"}],"output_ports":[{"name":"data","node_id":"-154"}],"cacheable":true,"seq_num":11,"comment":"","comment_collapsed":true},{"node_id":"-131","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\nclose\nopen\nlow\nhigh\nadjust_factor","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-131"}],"output_ports":[{"name":"data","node_id":"-131"}],"cacheable":true,"seq_num":12,"comment":"","comment_collapsed":true},{"node_id":"-9894","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":"-9894"},{"name":"features","node_id":"-9894"}],"output_ports":[{"name":"data","node_id":"-9894"}],"cacheable":false,"seq_num":20,"comment":"bar1d_index_CN_STOCK_A表中抽取指数close数据","comment_collapsed":true},{"node_id":"-271","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\nbmret=close/shift(close,1)-1\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-271"}],"output_ports":[{"name":"data","node_id":"-271"}],"cacheable":true,"seq_num":21,"comment":"表达式引擎因子计算5日收益率","comment_collapsed":true},{"node_id":"-228","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":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-228"},{"name":"features","node_id":"-228"}],"output_ports":[{"name":"data","node_id":"-228"}],"cacheable":true,"seq_num":22,"comment":"计算指数5日收益率","comment_collapsed":true},{"node_id":"-242","module_id":"BigQuantSpace.select_columns.select_columns-v3","parameters":[{"name":"columns","value":"","type":"Literal","bound_global_parameter":null},{"name":"reverse_select","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-242"},{"name":"columns_ds","node_id":"-242"}],"output_ports":[{"name":"data","node_id":"-242"}],"cacheable":true,"seq_num":23,"comment":"过滤指定的列","comment_collapsed":true},{"node_id":"-197","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"-197"},{"name":"data2","node_id":"-197"}],"output_ports":[{"name":"data","node_id":"-197"}],"cacheable":true,"seq_num":24,"comment":"合并股票真实价格和大盘5日收益率","comment_collapsed":true},{"node_id":"-528","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":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-528"},{"name":"features","node_id":"-528"}],"output_ports":[{"name":"data","node_id":"-528"}],"cacheable":true,"seq_num":25,"comment":"抽取相对收益率","comment_collapsed":true},{"node_id":"-546","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\nrelative_ret=stockret-bmret\nrelative_ret_5=sum(relative_ret,5)\nrelative_ret_30=sum(relative_ret,30)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-546"}],"output_ports":[{"name":"data","node_id":"-546"}],"cacheable":true,"seq_num":26,"comment":"","comment_collapsed":true},{"node_id":"-266","module_id":"BigQuantSpace.select_columns.select_columns-v3","parameters":[{"name":"columns","value":"","type":"Literal","bound_global_parameter":null},{"name":"reverse_select","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-266"},{"name":"columns_ds","node_id":"-266"}],"output_ports":[{"name":"data","node_id":"-266"}],"cacheable":true,"seq_num":27,"comment":"过滤指定的列","comment_collapsed":true},{"node_id":"-248","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":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-248"},{"name":"features","node_id":"-248"}],"output_ports":[{"name":"data","node_id":"-248"}],"cacheable":true,"seq_num":28,"comment":"抽取真实价格","comment_collapsed":true},{"node_id":"-204","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"bar1d_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":"-204"},{"name":"features","node_id":"-204"}],"output_ports":[{"name":"data","node_id":"-204"}],"cacheable":true,"seq_num":29,"comment":"bar1d_CN_STOCK_A表中抽取全市场股票的基础行情数据","comment_collapsed":true},{"node_id":"-256","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\nstockret=close/shift(close,1)-1","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-256"}],"output_ports":[{"name":"data","node_id":"-256"}],"cacheable":true,"seq_num":30,"comment":"表达式引擎计算个股过去5日收益率","comment_collapsed":true},{"node_id":"-6195","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"(relative_ret_5>0)&(relative_ret_30>0)&(rank(relative_ret_30)>0.8)","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-6195"}],"output_ports":[{"name":"data","node_id":"-6195"},{"name":"left_data","node_id":"-6195"}],"cacheable":true,"seq_num":31,"comment":"","comment_collapsed":true},{"node_id":"-1162","module_id":"BigQuantSpace.select_columns.select_columns-v3","parameters":[{"name":"columns","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"reverse_select","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-1162"},{"name":"columns_ds","node_id":"-1162"}],"output_ports":[{"name":"data","node_id":"-1162"}],"cacheable":true,"seq_num":32,"comment":"","comment_collapsed":true},{"node_id":"-3194","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"-3194"},{"name":"data2","node_id":"-3194"}],"output_ports":[{"name":"data","node_id":"-3194"}],"cacheable":true,"seq_num":33,"comment":"","comment_collapsed":true},{"node_id":"-3325","module_id":"BigQuantSpace.auto_labeler_on_datasource.auto_labeler_on_datasource-v1","parameters":[{"name":"label_expr","value":"# #号开始的表示注释\n# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_\n\n# 计算收益：5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)\n#shift(close, -5) / shift(open, -1)\n\n# 极值处理：用1%和99%分位的值做clip\n#clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))\n\n# 将分数映射到分类，这里使用20个分类\n#all_wbins(label, 20)\n\n# 过滤掉一字涨停的情况 (设置label为NaN，在后续处理和训练中会忽略NaN的label)\n#where(shift(high, -1) == shift(low, -1), NaN, label)\n# 计算收益：2日开盘价(作为卖出价格)除以明日开盘价(作为买入价格)\n(shift(close, -5) / shift(open, -1)-1)\n\n# 极值处理：用1%和99%分位的值做clip\nclip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))\nall_wbins(label, 20)\n# 过滤掉一字涨停的情况 (设置label为NaN，在后续处理和训练中会忽略NaN的label)\nwhere(shift(high, -1) == shift(low, -1), NaN, label)\n#where(label>0.5, NaN, label)\n#where(label<-0.5, NaN, label)\n","type":"Literal","bound_global_parameter":null},{"name":"drop_na_label","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cast_label_int","value":"False","type":"Literal","bound_global_parameter":null},{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-3325"}],"output_ports":[{"name":"data","node_id":"-3325"}],"cacheable":true,"seq_num":34,"comment":"","comment_collapsed":true},{"node_id":"-3201","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"bar1d_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":"-3201"},{"name":"features","node_id":"-3201"}],"output_ports":[{"name":"data","node_id":"-3201"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-9965","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2018-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-10-30","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":"000300.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":"-9965"}],"output_ports":[{"name":"data","node_id":"-9965"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-9973","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\nclose\ninstrument\n\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-9973"}],"output_ports":[{"name":"data","node_id":"-9973"}],"cacheable":true,"seq_num":35,"comment":"","comment_collapsed":true},{"node_id":"-9978","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":"-9978"},{"name":"features","node_id":"-9978"}],"output_ports":[{"name":"data","node_id":"-9978"}],"cacheable":false,"seq_num":36,"comment":"bar1d_index_CN_STOCK_A表中抽取指数close数据","comment_collapsed":true},{"node_id":"-10010","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2018-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-10-30","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":"-10010"}],"output_ports":[{"name":"data","node_id":"-10010"}],"cacheable":true,"seq_num":37,"comment":"","comment_collapsed":true},{"node_id":"-9984","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\nbmret=close/shift(close,1)-1\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-9984"}],"output_ports":[{"name":"data","node_id":"-9984"}],"cacheable":true,"seq_num":38,"comment":"表达式引擎因子计算5日收益率","comment_collapsed":true},{"node_id":"-9989","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":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-9989"},{"name":"features","node_id":"-9989"}],"output_ports":[{"name":"data","node_id":"-9989"}],"cacheable":true,"seq_num":39,"comment":"计算指数5日收益率","comment_collapsed":true},{"node_id":"-9998","module_id":"BigQuantSpace.select_columns.select_columns-v3","parameters":[{"name":"columns","value":"","type":"Literal","bound_global_parameter":null},{"name":"reverse_select","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-9998"},{"name":"columns_ds","node_id":"-9998"}],"output_ports":[{"name":"data","node_id":"-9998"}],"cacheable":true,"seq_num":40,"comment":"过滤指定的列","comment_collapsed":true},{"node_id":"-10004","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"-10004"},{"name":"data2","node_id":"-10004"}],"output_ports":[{"name":"data","node_id":"-10004"}],"cacheable":true,"seq_num":41,"comment":"合并股票真实价格和大盘5日收益率","comment_collapsed":true},{"node_id":"-10019","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"bar1d_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":"-10019"},{"name":"features","node_id":"-10019"}],"output_ports":[{"name":"data","node_id":"-10019"}],"cacheable":true,"seq_num":42,"comment":"bar1d_CN_STOCK_A表中抽取全市场股票的基础行情数据","comment_collapsed":true},{"node_id":"-10025","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\nclose\nopen\nlow\nhigh\nadjust_factor","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-10025"}],"output_ports":[{"name":"data","node_id":"-10025"}],"cacheable":true,"seq_num":43,"comment":"","comment_collapsed":true},{"node_id":"-10029","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\nstockret=close/shift(close,1)-1","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-10029"}],"output_ports":[{"name":"data","node_id":"-10029"}],"cacheable":true,"seq_num":44,"comment":"表达式引擎计算个股过去5日收益率","comment_collapsed":true},{"node_id":"-10034","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":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-10034"},{"name":"features","node_id":"-10034"}],"output_ports":[{"name":"data","node_id":"-10034"}],"cacheable":true,"seq_num":45,"comment":"抽取真实价格","comment_collapsed":true},{"node_id":"-10043","module_id":"BigQuantSpace.select_columns.select_columns-v3","parameters":[{"name":"columns","value":"","type":"Literal","bound_global_parameter":null},{"name":"reverse_select","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-10043"},{"name":"columns_ds","node_id":"-10043"}],"output_ports":[{"name":"data","node_id":"-10043"}],"cacheable":true,"seq_num":46,"comment":"过滤指定的列","comment_collapsed":true},{"node_id":"-10048","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\nrelative_ret=stockret-bmret\nrelative_ret_5=sum(relative_ret,5)\nrelative_ret_30=sum(relative_ret,30)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-10048"}],"output_ports":[{"name":"data","node_id":"-10048"}],"cacheable":true,"seq_num":47,"comment":"","comment_collapsed":true},{"node_id":"-10053","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":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-10053"},{"name":"features","node_id":"-10053"}],"output_ports":[{"name":"data","node_id":"-10053"}],"cacheable":true,"seq_num":48,"comment":"抽取相对收益率","comment_collapsed":true},{"node_id":"-6189","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"(relative_ret_5>0)&(relative_ret_30>0)&(rank(relative_ret_30)>0.8)","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-6189"}],"output_ports":[{"name":"data","node_id":"-6189"},{"name":"left_data","node_id":"-6189"}],"cacheable":true,"seq_num":49,"comment":"","comment_collapsed":true},{"node_id":"-3228","module_id":"BigQuantSpace.select_columns.select_columns-v3","parameters":[{"name":"columns","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"reverse_select","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-3228"},{"name":"columns_ds","node_id":"-3228"}],"output_ports":[{"name":"data","node_id":"-3228"}],"cacheable":true,"seq_num":50,"comment":"","comment_collapsed":true},{"node_id":"-3234","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"-3234"},{"name":"data2","node_id":"-3234"}],"output_ports":[{"name":"data","node_id":"-3234"}],"cacheable":true,"seq_num":51,"comment":"","comment_collapsed":true},{"node_id":"-11425","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"# #号开始的表示注释\n# 多个特征，每行一个，可以包含基础特征和衍生特征\n#周线金叉\ncond1=sum(ta_macd_dif(close_0,2,4,4),5)>sum(ta_macd_dea(close_0,2,4,4),5)\ncond2=close_0>mean(close_0, 25)\ncond3=sum(ta_macd_dea(close_0,2,4,4),5)>0.2\nprice_limit_status_0\ncond4=st_status_0<1","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-11425"}],"output_ports":[{"name":"data","node_id":"-11425"}],"cacheable":true,"seq_num":52,"comment":"","comment_collapsed":true},{"node_id":"-11430","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"cond1&cond2&cond3","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-11430"}],"output_ports":[{"name":"data","node_id":"-11430"},{"name":"left_data","node_id":"-11430"}],"cacheable":true,"seq_num":53,"comment":"","comment_collapsed":true},{"node_id":"-11436","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"cond1&cond2&cond3&cond4","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-11436"}],"output_ports":[{"name":"data","node_id":"-11436"},{"name":"left_data","node_id":"-11436"}],"cacheable":true,"seq_num":54,"comment":"","comment_collapsed":true},{"node_id":"-12762","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\n#bm_0 = where(close/shift(close,5)-1<-0.05,1,0)\n\nbm_0=where(ta_macd_dif(close,2,4,4)-ta_macd_dea(close,2,4,4)<0,1,0)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-12762"}],"output_ports":[{"name":"data","node_id":"-12762"}],"cacheable":true,"seq_num":55,"comment":"","comment_collapsed":true},{"node_id":"-175","module_id":"BigQuantSpace.index_feature_extract.index_feature_extract-v3","parameters":[{"name":"before_days","value":100,"type":"Literal","bound_global_parameter":null},{"name":"index","value":"000300.HIX","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-175"},{"name":"input_2","node_id":"-175"}],"output_ports":[{"name":"data_1","node_id":"-175"},{"name":"data_2","node_id":"-175"}],"cacheable":true,"seq_num":56,"comment":"","comment_collapsed":true},{"node_id":"-196","module_id":"BigQuantSpace.select_columns.select_columns-v3","parameters":[{"name":"columns","value":"date,bm_0","type":"Literal","bound_global_parameter":null},{"name":"reverse_select","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-196"},{"name":"columns_ds","node_id":"-196"}],"output_ports":[{"name":"data","node_id":"-196"}],"cacheable":true,"seq_num":57,"comment":"","comment_collapsed":true},{"node_id":"-189","module_id":"BigQuantSpace.join.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":"data1","node_id":"-189"},{"name":"data2","node_id":"-189"}],"output_ports":[{"name":"data","node_id":"-189"}],"cacheable":true,"seq_num":58,"comment":"","comment_collapsed":true},{"node_id":"-224","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"instruments_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":"-224"},{"name":"features","node_id":"-224"}],"output_ports":[{"name":"data","node_id":"-224"}],"cacheable":true,"seq_num":59,"comment":"","comment_collapsed":true},{"node_id":"-148","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\nname","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-148"}],"output_ports":[{"name":"data","node_id":"-148"}],"cacheable":true,"seq_num":60,"comment":"","comment_collapsed":true},{"node_id":"-202","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date,instrument","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":"data1","node_id":"-202"},{"name":"data2","node_id":"-202"}],"output_ports":[{"name":"data","node_id":"-202"}],"cacheable":true,"seq_num":61,"comment":"","comment_collapsed":true},{"node_id":"-490","module_id":"BigQuantSpace.sort.sort-v4","parameters":[{"name":"sort_by","value":"score","type":"Literal","bound_global_parameter":null},{"name":"group_by","value":"date","type":"Literal","bound_global_parameter":null},{"name":"keep_columns","value":"--","type":"Literal","bound_global_parameter":null},{"name":"ascending","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-490"},{"name":"sort_by_ds","node_id":"-490"}],"output_ports":[{"name":"sorted_data","node_id":"-490"}],"cacheable":true,"seq_num":62,"comment":"","comment_collapsed":true},{"node_id":"-1918","module_id":"BigQuantSpace.trade.trade-v4","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"initialize","value":"# 回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 系统已经设置了默认的交易手续费和滑点，要修改手续费可使用如下函数\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)\n # 设置买入的股票数量，这里买入预测股票列表排名靠前的5只\n stock_count = 5\n # 每只的股票的权重，如下的权重分配会使得靠前的股票分配多一点的资金，[0.339160, 0.213986, 0.169580, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument =0.1\n context.options['hold_days'] = 5\n\n from zipline.finance.slippage import SlippageModel\n class FixedPriceSlippage(SlippageModel):\n def process_order(self, data, order, bar_volume=0, trigger_check_price=0):\n if order.limit is None:\n price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell\n price = data.current(order.asset, price_field)\n else:\n price = data.current(order.asset, self._price_field_buy)\n # 返回希望成交的价格和数量\n return (price, order.amount)\n # 设置price_field,默认是开盘买入，收盘卖出\n context.fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close')\n context.set_slippage(us_equities=context.fix_slippage)","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n # 获取当前持仓\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\n \n today = data.current_dt.strftime('%Y-%m-%d')\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == today]\n try:\n #大盘风控模块，读取风控数据 \n benckmark_risk=ranker_prediction['bm_0'].values[0]\n if benckmark_risk > 0:\n for instrument in positions.keys():\n context.order_target(context.symbol(instrument), 0)\n print(today,'大盘风控止损触发,全仓卖出')\n return\n except:\n print('缺失风控数据！')\n \n #当risk为1时，市场有风险，全部平仓，不再执行其它操作\n \n \n # 1. 资金分配\n # 平均持仓时间是hold_days，每日都将买入股票，每日预期使用 1/hold_days 的资金\n # 实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n \n \n # 2. 根据需要加入移动止赢止损模块、固定天数卖出模块、ST或退市股卖出模块\n stock_sold = [] # 记录卖出的股票，防止多次卖出出现空单\n \n #------------------------START:止赢止损模块(含建仓期)---------------\n current_stopwin_stock=[]\n current_stoploss_stock = [] \n positions_cost={e.symbol:p.cost_basis for e,p in context.portfolio.positions.items()}\n if len(positions)>0:\n for instrument in positions.keys():\n stock_cost=positions_cost[instrument] \n stock_market_price=data.current(context.symbol(instrument),'price') \n volume_since_buy = data.history(context.symbol(instrument), 'volume', 6, '1d')\n # 赚9%且为可交易状态就止盈\n if stock_market_price/stock_cost-1>=0.6 and data.can_trade(context.symbol(instrument)):\n context.order_target_percent(context.symbol(instrument),0)\n cash_for_sell -= positions[instrument]\n current_stopwin_stock.append(instrument)\n # 亏5%并且为可交易状态就止损\n if stock_market_price/stock_cost-1 <= -0.05 and data.can_trade(context.symbol(instrument)): \n context.order_target_percent(context.symbol(instrument),0)\n cash_for_sell -= positions[instrument]\n current_stoploss_stock.append(instrument)\n # 放天量 止损：\n# if (volume_since_buy[0]>1.5*volume_since_buy[1]) |(volume_since_buy[0]>1.5*(volume_since_buy[1]+volume_since_buy[2]+volume_since_buy[3]+volume_since_buy[4]+volume_since_buy[5])/5):\n# context.order_target_percent(context.symbol(instrument),0)\n# cash_for_sell -= positions[instrument]\n# current_stoploss_stock.append(instrument)\n if len(current_stopwin_stock)>0:\n print(today,'止盈股票列表',current_stopwin_stock)\n stock_sold += current_stopwin_stock\n if len(current_stoploss_stock)>0:\n print(today,'止损股票列表',current_stoploss_stock)\n stock_sold += current_stoploss_stock\n #--------------------------END: 止赢止损模块--------------------------\n \n #--------------------------START：持有固定天数卖出(不含建仓期)-----------\n current_stopdays_stock = []\n positions_lastdate = {e.symbol:p.last_sale_date for e,p in context.portfolio.positions.items()}\n # 不是建仓期（在前hold_days属于建仓期）\n if not is_staging:\n for instrument in positions.keys():\n #如果上面的止盈止损已经卖出过了，就不要重复卖出以防止产生空单\n if instrument in stock_sold:\n continue\n # 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数，比如datetime.timedelta(5)\n if data.current_dt - positions_lastdate[instrument]>=datetime.timedelta(22) and data.can_trade(context.symbol(instrument)):\n context.order_target_percent(context.symbol(instrument), 0)\n current_stopdays_stock.append(instrument)\n cash_for_sell -= positions[instrument]\n if len(current_stopdays_stock)>0: \n print(today,'固定天数卖出列表',current_stopdays_stock)\n stock_sold += current_stopdays_stock\n #------------------------- END:持有固定天数卖出-----------------------\n \n #-------------------------- START: ST和退市股卖出 --------------------- \n st_stock_list = []\n for instrument in positions.keys():\n try:\n instrument_name = ranker_prediction[ranker_prediction.instrument==instrument].name.values[0]\n # 如果股票状态变为了st或者退市 则卖出\n if 'ST' in instrument_name or '退' in instrument_name:\n if instrument in stock_sold:\n continue\n if data.can_trade(context.symbol(instrument)):\n context.order_target(context.symbol(instrument), 0)\n st_stock_list.append(instrument)\n cash_for_sell -= positions[instrument]\n except:\n continue\n if st_stock_list!=[]:\n print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理') \n stock_sold += st_stock_list\n\n #-------------------------- END: ST和退市股卖出 --------------------- \n \n \n # 3. 生成轮仓卖出订单：hold_days天之后才开始卖出；对持仓的股票，按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in positions)])))\n for instrument in instruments:\n # 如果资金够了就不卖出了\n if cash_for_sell <= 0:\n break\n #防止多个止损条件同时满足，出现多次卖出产生空单\n if instrument in stock_sold:\n continue\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n stock_sold.append(instrument)\n\n # 4. 生成轮仓买入订单：按机器学习算法预测的排序，买入前面的stock_count只股票\n # 计算今日跌停的股票\n dt_list = list(ranker_prediction[ranker_prediction.price_limit_status_0==1].instrument)\n # 计算今日ST/退市的股票\n st_list = list(ranker_prediction[ranker_prediction.name.str.contains('ST')|ranker_prediction.name.str.contains('退')].instrument)\n # 计算所有禁止买入的股票池\n banned_list = stock_sold+dt_list+st_list\n buy_cash_weights = context.stock_weights\n buy_instruments=[k for k in list(ranker_prediction.instrument) if k not in banned_list][:len(buy_cash_weights)]\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n \n\n","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n context.status_df = D.features(instruments =context.instruments,start_date = context.start_date, end_date = context.end_date, \n fields=['st_status_0','price_limit_status_0','price_limit_status_1'])\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"def bigquant_run(context, data):\n # 获取涨跌停状态数据\n df_price_limit_status = context.ranker_prediction.set_index('date')\n today=data.current_dt.strftime('%Y-%m-%d')\n # 得到当前未完成订单\n for orders in get_open_orders().values():\n # 循环，撤销订单\n for _order in orders:\n ins=str(_order.sid.symbol)\n try:\n #判断一下如果当日涨停，则取消卖单\n if df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status_0.ix[today]>2 and _order.amount<0:\n cancel_order(_order)\n print(today,'尾盘涨停取消卖单',ins) \n except:\n continue","type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":0.025,"type":"Literal","bound_global_parameter":null},{"name":"order_price_field_buy","value":"open","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"close","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":"1000001","type":"Literal","bound_global_parameter":null},{"name":"auto_cancel_non_tradable_orders","value":"True","type":"Literal","bound_global_parameter":null},{"name":"data_frequency","value":"daily","type":"Literal","bound_global_parameter":null},{"name":"price_type","value":"后复权","type":"Literal","bound_global_parameter":null},{"name":"product_type","value":"股票","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-1918"},{"name":"options_data","node_id":"-1918"},{"name":"history_ds","node_id":"-1918"},{"name":"benchmark_ds","node_id":"-1918"},{"name":"trading_calendar","node_id":"-1918"}],"output_ports":[{"name":"raw_perf","node_id":"-1918"}],"cacheable":false,"seq_num":63,"comment":"","comment_collapsed":true},{"node_id":"-323","module_id":"BigQuantSpace.select_columns.select_columns-v3","parameters":[{"name":"columns","value":"date,instrument,price_limit_status_0","type":"Literal","bound_global_parameter":null},{"name":"reverse_select","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-323"},{"name":"columns_ds","node_id":"-323"}],"output_ports":[{"name":"data","node_id":"-323"}],"cacheable":true,"seq_num":64,"comment":"","comment_collapsed":true},{"node_id":"-329","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"-329"},{"name":"data2","node_id":"-329"}],"output_ports":[{"name":"data","node_id":"-329"}],"cacheable":true,"seq_num":65,"comment":"","comment_collapsed":true},{"node_id":"-236","module_id":"BigQuantSpace.features_short.features_short-v1","parameters":[],"input_ports":[{"name":"input_1","node_id":"-236"}],"output_ports":[{"name":"data_1","node_id":"-236"}],"cacheable":true,"seq_num":67,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='121,-20,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='663,29,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-43' Position='675,530,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='249,375,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-60' Position='794,637,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1159,117,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-84' Position='508,457,200,200'/><node_position Node='-86' Position='1091,546,200,200'/><node_position Node='-215' Position='381,188,200,200'/><node_position Node='-222' Position='385,280,200,200'/><node_position Node='-231' Position='1078,236,200,200'/><node_position Node='-238' Position='1081,327,200,200'/><node_position Node='-184' Position='-1462,-1098,200,200'/><node_position Node='-150' Position='-1119,-1100,200,200'/><node_position Node='-154' Position='-810,-1096,200,200'/><node_position Node='-131' Position='-467,-1087,200,200'/><node_position Node='-9894' Position='-1312,-992,200,200'/><node_position Node='-271' Position='-988,-953,200,200'/><node_position Node='-228' Position='-1096,-853,200,200'/><node_position Node='-242' Position='-1052,-740,200,200'/><node_position Node='-197' Position='-741,-616,200,200'/><node_position Node='-528' Position='-511,-563,200,200'/><node_position Node='-546' Position='-339,-628,200,200'/><node_position Node='-266' Position='-375,-742,200,200'/><node_position Node='-248' Position='-524,-856,200,200'/><node_position Node='-204' Position='-661,-971,200,200'/><node_position Node='-256' Position='-361,-974,200,200'/><node_position Node='-6195' Position='-498,-200,200,200'/><node_position Node='-1162' Position='-454,-103,200,200'/><node_position Node='-3194' Position='-82,196,200,200'/><node_position Node='-3325' Position='-42,290,200,200'/><node_position Node='-3201' Position='46,114,200,200'/><node_position Node='-9965' Position='665,-942,200,200'/><node_position Node='-9973' Position='894,-890,200,200'/><node_position Node='-9978' Position='801,-776,200,200'/><node_position Node='-10010' Position='1221,-864,200,200'/><node_position Node='-9984' Position='1028,-743,200,200'/><node_position Node='-9989' Position='918,-643,200,200'/><node_position Node='-9998' Position='963,-528,200,200'/><node_position Node='-10004' Position='1266,-402,200,200'/><node_position Node='-10019' Position='1355,-761,200,200'/><node_position Node='-10025' Position='1550,-877,200,200'/><node_position Node='-10029' Position='1656,-762,200,200'/><node_position Node='-10034' Position='1492,-644,200,200'/><node_position Node='-10043' Position='1641,-530,200,200'/><node_position Node='-10048' Position='1679,-415,200,200'/><node_position Node='-10053' Position='1490,-325,200,200'/><node_position Node='-6189' Position='1373,-50,200,200'/><node_position Node='-3228' Position='1944,331,200,200'/><node_position Node='-3234' Position='1415,337,200,200'/><node_position Node='-11425' Position='719,145,200,200'/><node_position Node='-11430' Position='673,380,200,200'/><node_position Node='-11436' Position='1309,479,200,200'/><node_position Node='-12762' Position='1927,435,200,200'/><node_position Node='-175' Position='1928,542,200,200'/><node_position Node='-196' Position='1909,642,200,200'/><node_position Node='-189' Position='1725,760,200,200'/><node_position Node='-224' Position='1615,640,200,200'/><node_position Node='-148' Position='1618,542,200,200'/><node_position Node='-202' Position='1459,876,200,200'/><node_position Node='-490' Position='1566,979,200,200'/><node_position Node='-1918' Position='1478,1087,200,200'/><node_position Node='-323' Position='1342,676,200,200'/><node_position Node='-329' Position='1346,782,200,200'/><node_position Node='-236' Position='844,340,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}
[2021-10-31 17:50:42.072523] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-10-31 17:50:42.081327] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.083765] INFO: moduleinvoker: instruments.v2 运行完成[0.011233s].
[2021-10-31 17:50:42.089307] INFO: moduleinvoker: use_datasource.v1 开始运行..
[2021-10-31 17:50:42.098854] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.100435] INFO: moduleinvoker: use_datasource.v1 运行完成[0.011142s].
[2021-10-31 17:50:42.104049] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:50:42.114345] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.117042] INFO: moduleinvoker: input_features.v1 运行完成[0.012963s].
[2021-10-31 17:50:42.123026] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:50:42.133861] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.135484] INFO: moduleinvoker: input_features.v1 运行完成[0.012489s].
[2021-10-31 17:50:42.148132] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-10-31 17:50:42.157657] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.159066] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.010937s].
[2021-10-31 17:50:42.165308] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-10-31 17:50:42.174851] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.177971] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.012627s].
[2021-10-31 17:50:42.203338] INFO: moduleinvoker: features_short.v1 开始运行..
[2021-10-31 17:50:42.211733] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.213196] INFO: moduleinvoker: features_short.v1 运行完成[0.009879s].
[2021-10-31 17:50:42.217857] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-10-31 17:50:42.226048] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.227261] INFO: moduleinvoker: instruments.v2 运行完成[0.009404s].
[2021-10-31 17:50:42.236803] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-10-31 17:50:42.242555] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.244197] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.007411s].
[2021-10-31 17:50:42.250690] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-10-31 17:50:42.259337] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.260912] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.010221s].
[2021-10-31 17:50:42.265697] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-10-31 17:50:42.273601] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.275869] INFO: moduleinvoker: instruments.v2 运行完成[0.010171s].
[2021-10-31 17:50:42.281665] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:50:42.299360] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.301341] INFO: moduleinvoker: input_features.v1 运行完成[0.019706s].
[2021-10-31 17:50:42.306176] INFO: moduleinvoker: use_datasource.v1 开始运行..
[2021-10-31 17:50:42.595424] INFO: moduleinvoker: use_datasource.v1 运行完成[0.289244s].
[2021-10-31 17:50:42.608032] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-10-31 17:50:42.615483] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.616949] INFO: moduleinvoker: instruments.v2 运行完成[0.008933s].
[2021-10-31 17:50:42.620799] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:50:42.628900] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.630268] INFO: moduleinvoker: input_features.v1 运行完成[0.00947s].
[2021-10-31 17:50:42.634804] INFO: moduleinvoker: use_datasource.v1 开始运行..
[2021-10-31 17:50:42.642022] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.643265] INFO: moduleinvoker: use_datasource.v1 运行完成[0.008461s].
[2021-10-31 17:50:42.646874] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:50:42.652574] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.653727] INFO: moduleinvoker: input_features.v1 运行完成[0.006854s].
[2021-10-31 17:50:42.660137] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-10-31 17:50:42.711152] INFO: derived_feature_extractor: 提取完成 bmret=close/shift(close,1)-1, 0.003s
[2021-10-31 17:50:42.748281] INFO: derived_feature_extractor: /data, 2187
[2021-10-31 17:50:42.787923] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.127736s].
[2021-10-31 17:50:42.799844] INFO: moduleinvoker: select_columns.v3 开始运行..
[2021-10-31 17:50:42.943879] INFO: moduleinvoker: select_columns.v3 运行完成[0.144035s].
[2021-10-31 17:50:42.948136] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:50:42.957053] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.958269] INFO: moduleinvoker: input_features.v1 运行完成[0.010139s].
[2021-10-31 17:50:42.961731] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:50:42.966136] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.967306] INFO: moduleinvoker: input_features.v1 运行完成[0.005576s].
[2021-10-31 17:50:42.975408] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-10-31 17:50:42.984560] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:42.987216] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.011799s].
[2021-10-31 17:50:42.996901] INFO: moduleinvoker: select_columns.v3 开始运行..
[2021-10-31 17:50:43.008817] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:50:43.010102] INFO: moduleinvoker: select_columns.v3 运行完成[0.013207s].
[2021-10-31 17:50:43.017392] INFO: moduleinvoker: join.v3 开始运行..
[2021-10-31 17:50:52.582492] INFO: join: /data, 行数=5818641/5818641, 耗时=9.512812s
[2021-10-31 17:50:52.719920] INFO: join: 最终行数: 5818641
[2021-10-31 17:50:52.726535] INFO: moduleinvoker: join.v3 运行完成[9.709131s].
[2021-10-31 17:50:52.733072] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-10-31 17:51:01.413451] INFO: derived_feature_extractor: 提取完成 relative_ret=stockret-bmret, 0.008s
[2021-10-31 17:51:09.208945] INFO: derived_feature_extractor: 提取完成 relative_ret_5=sum(relative_ret,5), 7.794s
[2021-10-31 17:51:16.907332] INFO: derived_feature_extractor: 提取完成 relative_ret_30=sum(relative_ret,30), 7.697s
[2021-10-31 17:51:25.590300] INFO: derived_feature_extractor: /data, 5818641
[2021-10-31 17:51:27.267771] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[34.534687s].
[2021-10-31 17:51:27.281204] INFO: moduleinvoker: filter.v3 开始运行..
[2021-10-31 17:51:27.297473] INFO: filter: 使用表达式 (relative_ret_5>0)&(relative_ret_30>0)&(rank(relative_ret_30)>0.8) 过滤
[2021-10-31 17:51:32.916720] INFO: filter: 过滤 /data, 739045/0/5818641
[2021-10-31 17:51:32.956502] INFO: moduleinvoker: filter.v3 运行完成[5.675325s].
[2021-10-31 17:51:32.967722] INFO: moduleinvoker: select_columns.v3 开始运行..
[2021-10-31 17:51:33.256013] INFO: moduleinvoker: select_columns.v3 运行完成[0.288279s].
[2021-10-31 17:51:33.264676] INFO: moduleinvoker: join.v3 开始运行..
[2021-10-31 17:51:34.919311] INFO: join: /data, 行数=26633/739045, 耗时=1.339222s
[2021-10-31 17:51:34.954400] INFO: join: 最终行数: 26633
[2021-10-31 17:51:34.961252] INFO: moduleinvoker: join.v3 运行完成[1.696567s].
[2021-10-31 17:51:34.968580] INFO: moduleinvoker: auto_labeler_on_datasource.v1 开始运行..
[2021-10-31 17:51:35.037540] INFO: 自动标注(任意数据源): 开始标注 ..
[2021-10-31 17:51:35.145656] INFO: moduleinvoker: auto_labeler_on_datasource.v1 运行完成[0.177082s].
[2021-10-31 17:51:35.153171] INFO: moduleinvoker: join.v3 开始运行..
[2021-10-31 17:51:35.393814] INFO: join: /y_2009, 行数=0/2362, 耗时=0.066978s
[2021-10-31 17:51:35.466696] INFO: join: /y_2010, 行数=2204/11237, 耗时=0.071274s
[2021-10-31 17:51:35.556743] INFO: join: /y_2011, 行数=2459/13462, 耗时=0.088375s
[2021-10-31 17:51:35.644381] INFO: join: /y_2012, 行数=2826/15403, 耗时=0.08591s
[2021-10-31 17:51:35.762934] INFO: join: /y_2013, 行数=3657/16610, 耗时=0.116895s
[2021-10-31 17:51:35.861325] INFO: join: /y_2014, 行数=2619/18140, 耗时=0.096276s
[2021-10-31 17:51:35.949539] INFO: join: /y_2015, 行数=2664/18862, 耗时=0.086603s
[2021-10-31 17:51:36.043752] INFO: join: /y_2016, 行数=2835/20211, 耗时=0.0926s
[2021-10-31 17:51:36.139224] INFO: join: /y_2017, 行数=5049/22151, 耗时=0.093707s
[2021-10-31 17:51:36.184485] INFO: join: 最终行数: 24313
[2021-10-31 17:51:36.198305] INFO: moduleinvoker: join.v3 运行完成[1.045095s].
[2021-10-31 17:51:36.207496] INFO: moduleinvoker: filter.v3 开始运行..
[2021-10-31 17:51:36.218199] INFO: filter: 使用表达式 cond1&cond2&cond3 过滤
[2021-10-31 17:51:36.298722] INFO: filter: 过滤 /y_2009, 0/0/0
[2021-10-31 17:51:36.364967] INFO: filter: 过滤 /y_2010, 1171/0/2204
[2021-10-31 17:51:36.433827] INFO: filter: 过滤 /y_2011, 1108/0/2459
[2021-10-31 17:51:36.502390] INFO: filter: 过滤 /y_2012, 1382/0/2826
[2021-10-31 17:51:36.562884] INFO: filter: 过滤 /y_2013, 1997/0/3657
[2021-10-31 17:51:36.631200] INFO: filter: 过滤 /y_2014, 1539/0/2619
[2021-10-31 17:51:36.692089] INFO: filter: 过滤 /y_2015, 1478/0/2664
[2021-10-31 17:51:36.759974] INFO: filter: 过滤 /y_2016, 1476/0/2835
[2021-10-31 17:51:36.831342] INFO: filter: 过滤 /y_2017, 3019/0/5049
[2021-10-31 17:51:36.854586] INFO: moduleinvoker: filter.v3 运行完成[0.647083s].
[2021-10-31 17:51:36.861980] INFO: moduleinvoker: dropnan.v1 开始运行..
[2021-10-31 17:51:37.008054] INFO: dropnan: /y_2010, 1171/1171
[2021-10-31 17:51:37.051630] INFO: dropnan: /y_2011, 1108/1108
[2021-10-31 17:51:37.104216] INFO: dropnan: /y_2012, 1382/1382
[2021-10-31 17:51:37.150410] INFO: dropnan: /y_2013, 1997/1997
[2021-10-31 17:51:37.241943] INFO: dropnan: /y_2014, 1539/1539
[2021-10-31 17:51:37.297110] INFO: dropnan: /y_2015, 1478/1478
[2021-10-31 17:51:37.341516] INFO: dropnan: /y_2016, 1476/1476
[2021-10-31 17:51:37.403290] INFO: dropnan: /y_2017, 3019/3019
[2021-10-31 17:51:37.451978] INFO: dropnan: 行数: 13170/13170
[2021-10-31 17:51:37.456463] INFO: moduleinvoker: dropnan.v1 运行完成[0.594473s].
[2021-10-31 17:51:37.463947] INFO: moduleinvoker: stock_ranker_train.v5 开始运行..
[2021-10-31 17:51:38.192542] INFO: StockRanker: 特征预处理 ..
[2021-10-31 17:51:38.237612] INFO: StockRanker: prepare data: training ..
[2021-10-31 17:51:38.253020] INFO: StockRanker: sort ..
[2021-10-31 17:51:38.420910] INFO: StockRanker: prepare data: test ..
[2021-10-31 17:51:38.436453] INFO: StockRanker: sort ..
[2021-10-31 17:51:38.595630] INFO: StockRanker训练: 23c812f4 准备训练: 13170 行数, test: 13170 rows
[2021-10-31 17:51:38.866976] INFO: StockRanker训练: 正在训练 ..
[2021-10-31 17:51:49.183940] INFO: moduleinvoker: stock_ranker_train.v5 运行完成[11.71998s].
[2021-10-31 17:51:49.194836] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-10-31 17:51:49.202201] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:51:49.204361] INFO: moduleinvoker: instruments.v2 运行完成[0.009527s].
[2021-10-31 17:51:49.209519] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:51:49.221126] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:51:49.223273] INFO: moduleinvoker: input_features.v1 运行完成[0.013759s].
[2021-10-31 17:51:49.229377] INFO: moduleinvoker: use_datasource.v1 开始运行..
[2021-10-31 17:51:49.417198] INFO: moduleinvoker: use_datasource.v1 运行完成[0.187831s].
[2021-10-31 17:51:49.430181] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-10-31 17:51:49.449776] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:51:49.451824] INFO: moduleinvoker: instruments.v2 运行完成[0.021653s].
[2021-10-31 17:51:49.455558] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:51:49.463706] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:51:49.465309] INFO: moduleinvoker: input_features.v1 运行完成[0.009759s].
[2021-10-31 17:51:49.474466] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-10-31 17:51:49.526313] INFO: derived_feature_extractor: 提取完成 bmret=close/shift(close,1)-1, 0.003s
[2021-10-31 17:51:49.587170] INFO: derived_feature_extractor: /data, 928
[2021-10-31 17:51:49.644078] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.169604s].
[2021-10-31 17:51:49.656739] INFO: moduleinvoker: select_columns.v3 开始运行..
[2021-10-31 17:51:49.765261] INFO: moduleinvoker: select_columns.v3 运行完成[0.10851s].
[2021-10-31 17:51:49.772494] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:51:49.782333] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:51:49.784540] INFO: moduleinvoker: input_features.v1 运行完成[0.012069s].
[2021-10-31 17:51:49.794720] INFO: moduleinvoker: use_datasource.v1 开始运行..
[2021-10-31 17:51:49.807035] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:51:49.809000] INFO: moduleinvoker: use_datasource.v1 运行完成[0.01429s].
[2021-10-31 17:51:49.812770] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:51:49.817263] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:51:49.818474] INFO: moduleinvoker: input_features.v1 运行完成[0.005705s].
[2021-10-31 17:51:49.824767] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-10-31 17:51:49.829001] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:51:49.830184] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.005414s].
[2021-10-31 17:51:49.837353] INFO: moduleinvoker: select_columns.v3 开始运行..
[2021-10-31 17:51:49.843216] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:51:49.844421] INFO: moduleinvoker: select_columns.v3 运行完成[0.007069s].
[2021-10-31 17:51:49.851696] INFO: moduleinvoker: join.v3 开始运行..
[2021-10-31 17:51:55.753942] INFO: join: /data, 行数=3556097/3556097, 耗时=5.841248s
[2021-10-31 17:51:55.844934] INFO: join: 最终行数: 3556097
[2021-10-31 17:51:55.851757] INFO: moduleinvoker: join.v3 运行完成[6.000055s].
[2021-10-31 17:51:55.855526] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:51:55.860061] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:51:55.861224] INFO: moduleinvoker: input_features.v1 运行完成[0.005699s].
[2021-10-31 17:51:55.866906] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-10-31 17:52:01.091403] INFO: derived_feature_extractor: 提取完成 relative_ret=stockret-bmret, 0.007s
[2021-10-31 17:52:05.678293] INFO: derived_feature_extractor: 提取完成 relative_ret_5=sum(relative_ret,5), 4.585s
[2021-10-31 17:52:10.214725] INFO: derived_feature_extractor: 提取完成 relative_ret_30=sum(relative_ret,30), 4.533s
[2021-10-31 17:52:15.452758] INFO: derived_feature_extractor: /data, 3556097
[2021-10-31 17:52:16.336639] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[20.469714s].
[2021-10-31 17:52:16.344227] INFO: moduleinvoker: filter.v3 开始运行..
[2021-10-31 17:52:16.360382] INFO: filter: 使用表达式 (relative_ret_5>0)&(relative_ret_30>0)&(rank(relative_ret_30)>0.8) 过滤
[2021-10-31 17:52:19.436426] INFO: filter: 过滤 /data, 441944/0/3556097
[2021-10-31 17:52:19.477559] INFO: moduleinvoker: filter.v3 运行完成[3.13326s].
[2021-10-31 17:52:19.490304] INFO: moduleinvoker: select_columns.v3 开始运行..
[2021-10-31 17:52:19.781068] INFO: moduleinvoker: select_columns.v3 运行完成[0.290727s].
[2021-10-31 17:52:19.805012] INFO: moduleinvoker: join.v3 开始运行..
[2021-10-31 17:52:20.728044] INFO: join: /y_2017, 行数=0/6415, 耗时=0.180558s
[2021-10-31 17:52:20.938514] INFO: join: /y_2018, 行数=4281/27902, 耗时=0.208725s
[2021-10-31 17:52:21.157545] INFO: join: /y_2019, 行数=7477/32051, 耗时=0.217317s
[2021-10-31 17:52:21.418646] INFO: join: /y_2020, 行数=11006/35474, 耗时=0.259249s
[2021-10-31 17:52:21.619382] INFO: join: /y_2021, 行数=8804/30753, 耗时=0.199049s
[2021-10-31 17:52:21.666539] INFO: join: 最终行数: 31568
[2021-10-31 17:52:21.682424] INFO: moduleinvoker: join.v3 运行完成[1.87741s].
[2021-10-31 17:52:21.696071] INFO: moduleinvoker: filter.v3 开始运行..
[2021-10-31 17:52:21.716413] INFO: filter: 使用表达式 cond1&cond2&cond3&cond4 过滤
[2021-10-31 17:52:21.749200] INFO: filter: 过滤 /y_2017, 0/0/0
[2021-10-31 17:52:21.819384] INFO: filter: 过滤 /y_2018, 1920/0/4281
[2021-10-31 17:52:21.899772] INFO: filter: 过滤 /y_2019, 4204/0/7477
[2021-10-31 17:52:21.960983] INFO: filter: 过滤 /y_2020, 6425/0/11006
[2021-10-31 17:52:22.053086] INFO: filter: 过滤 /y_2021, 5061/0/8804
[2021-10-31 17:52:22.079089] INFO: moduleinvoker: filter.v3 运行完成[0.383068s].
[2021-10-31 17:52:22.098937] INFO: moduleinvoker: dropnan.v1 开始运行..
[2021-10-31 17:52:22.240922] INFO: dropnan: /y_2018, 1920/1920
[2021-10-31 17:52:22.302231] INFO: dropnan: /y_2019, 4204/4204
[2021-10-31 17:52:22.356495] INFO: dropnan: /y_2020, 6424/6425
[2021-10-31 17:52:22.422538] INFO: dropnan: /y_2021, 5061/5061
[2021-10-31 17:52:22.497107] INFO: dropnan: 行数: 17609/17610
[2021-10-31 17:52:22.502727] INFO: moduleinvoker: dropnan.v1 运行完成[0.403787s].
[2021-10-31 17:52:22.514473] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..
[2021-10-31 17:52:23.021861] INFO: StockRanker预测: /y_2018 ..
[2021-10-31 17:52:23.608513] INFO: StockRanker预测: /y_2019 ..
[2021-10-31 17:52:24.413799] INFO: StockRanker预测: /y_2020 ..
[2021-10-31 17:52:25.015474] INFO: StockRanker预测: /y_2021 ..
[2021-10-31 17:52:25.740914] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[3.226425s].
[2021-10-31 17:52:25.757789] INFO: moduleinvoker: select_columns.v3 开始运行..
[2021-10-31 17:52:26.109395] INFO: moduleinvoker: select_columns.v3 运行完成[0.351602s].
[2021-10-31 17:52:26.126310] INFO: moduleinvoker: join.v3 开始运行..
[2021-10-31 17:52:26.335515] INFO: join: /y_2018, 行数=1920/1920, 耗时=0.058099s
[2021-10-31 17:52:26.405238] INFO: join: /y_2019, 行数=4204/4204, 耗时=0.06791s
[2021-10-31 17:52:26.452586] INFO: join: /y_2020, 行数=6424/6424, 耗时=0.045693s
[2021-10-31 17:52:26.514883] INFO: join: /y_2021, 行数=5061/5061, 耗时=0.060242s
[2021-10-31 17:52:26.575060] INFO: join: 最终行数: 17609
[2021-10-31 17:52:26.590192] INFO: moduleinvoker: join.v3 运行完成[0.463872s].
[2021-10-31 17:52:26.598030] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:52:26.607871] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:52:26.609539] INFO: moduleinvoker: input_features.v1 运行完成[0.011569s].
[2021-10-31 17:52:26.620717] INFO: moduleinvoker: index_feature_extract.v3 开始运行..
[2021-10-31 17:52:26.630434] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:52:26.631985] INFO: moduleinvoker: index_feature_extract.v3 运行完成[0.011282s].
[2021-10-31 17:52:26.642894] INFO: moduleinvoker: select_columns.v3 开始运行..
[2021-10-31 17:52:26.648130] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:52:26.649603] INFO: moduleinvoker: select_columns.v3 运行完成[0.006723s].
[2021-10-31 17:52:26.653042] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-10-31 17:52:26.659691] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:52:26.660968] INFO: moduleinvoker: input_features.v1 运行完成[0.007928s].
[2021-10-31 17:52:26.665137] INFO: moduleinvoker: use_datasource.v1 开始运行..
[2021-10-31 17:52:26.672370] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:52:26.675526] INFO: moduleinvoker: use_datasource.v1 运行完成[0.01038s].
[2021-10-31 17:52:26.686288] INFO: moduleinvoker: join.v3 开始运行..
[2021-10-31 17:52:26.692203] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:52:26.695359] INFO: moduleinvoker: join.v3 运行完成[0.009054s].
[2021-10-31 17:52:26.704669] INFO: moduleinvoker: join.v3 开始运行..
[2021-10-31 17:52:26.990529] INFO: join: /y_2018, 行数=1920/1920, 耗时=0.101372s
[2021-10-31 17:52:27.075942] INFO: join: /y_2019, 行数=4204/4204, 耗时=0.082988s
[2021-10-31 17:52:27.182846] INFO: join: /y_2020, 行数=6424/6424, 耗时=0.103241s
[2021-10-31 17:52:27.281755] INFO: join: /y_2021, 行数=5061/5061, 耗时=0.095948s
[2021-10-31 17:52:27.320140] INFO: join: 最终行数: 17609
[2021-10-31 17:52:27.329670] INFO: moduleinvoker: join.v3 运行完成[0.624999s].
[2021-10-31 17:52:27.334937] INFO: moduleinvoker: sort.v4 开始运行..
[2021-10-31 17:52:28.683365] INFO: moduleinvoker: sort.v4 运行完成[1.348401s].
[2021-10-31 17:52:28.770910] INFO: moduleinvoker: backtest.v8 开始运行..
[2021-10-31 17:52:28.784499] INFO: backtest: biglearning backtest:V8.5.0
[2021-10-31 17:52:37.189659] INFO: backtest: product_type:stock by specified
[2021-10-31 17:52:37.312825] INFO: moduleinvoker: cached.v2 开始运行..
[2021-10-31 17:52:37.327423] INFO: moduleinvoker: 命中缓存
[2021-10-31 17:52:37.330092] INFO: moduleinvoker: cached.v2 运行完成[0.017283s].
[2021-10-31 17:52:38.267096] INFO: algo: TradingAlgorithm V1.8.5
[2021-10-31 17:52:39.598659] INFO: algo: trading transform...
[2021-10-31 17:53:07.658990] INFO: Performance: Simulated 928 trading days out of 928.
[2021-10-31 17:53:07.660719] INFO: Performance: first open: 2018-01-02 09:30:00+00:00
[2021-10-31 17:53:07.661760] INFO: Performance: last close: 2021-10-29 15:00:00+00:00
[2021-10-31 17:53:11.977241] INFO: moduleinvoker: backtest.v8 运行完成[43.206317s].
[2021-10-31 17:53:11.980352] INFO: moduleinvoker: trade.v4 运行完成[43.286862s].
列： ['close', 'instrument']
/data: 2187
列： ['date', 'instrument']
/data: 739045
bigcharts-data-start/{"__type":"tabs","__id":"bigchart-f756758bc9a44e67a995b5f029083815"}/bigcharts-data-end
列： ['close', 'instrument']
/data: 928
列： ['date', 'instrument']
/data: 441944
列： ['date', 'instrument', 'price_limit_status_0']
/y_2018: 1920
/y_2019: 4204
/y_2020: 6424
/y_2021: 5061
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
缺失风控数据！
2018-02-27 大盘风控止损触发,全仓卖出
2018-02-28 大盘风控止损触发,全仓卖出
2018-03-01 大盘风控止损触发,全仓卖出
2018-03-02 大盘风控止损触发,全仓卖出
2018-03-05 大盘风控止损触发,全仓卖出
2018-03-07 大盘风控止损触发,全仓卖出
2018-03-13 大盘风控止损触发,全仓卖出
2018-03-14 大盘风控止损触发,全仓卖出
2018-03-15 大盘风控止损触发,全仓卖出
2018-03-16 大盘风控止损触发,全仓卖出
2018-03-19 大盘风控止损触发,全仓卖出
2018-03-21 大盘风控止损触发,全仓卖出
2018-03-22 大盘风控止损触发,全仓卖出
2018-03-23 大盘风控止损触发,全仓卖出
2018-03-26 大盘风控止损触发,全仓卖出
2018-03-28 大盘风控止损触发,全仓卖出
2018-04-03 止损股票列表 ['603605.SHA']
2018-04-04 大盘风控止损触发,全仓卖出
2018-04-09 止损股票列表 ['603223.SHA']
2018-04-10 止损股票列表 ['300661.SZA']
2018-04-12 大盘风控止损触发,全仓卖出
2018-04-13 大盘风控止损触发,全仓卖出
2018-04-16 大盘风控止损触发,全仓卖出
2018-04-17 大盘风控止损触发,全仓卖出
2018-04-18 大盘风控止损触发,全仓卖出
2018-04-19 止损股票列表 ['603985.SHA', '603712.SHA', '300598.SZA']
2018-04-20 止损股票列表 ['603129.SHA', '603260.SHA']
2018-04-23 止损股票列表 ['300568.SZA', '603501.SHA']
2018-04-26 大盘风控止损触发,全仓卖出
2018-04-27 大盘风控止损触发,全仓卖出
2018-05-02 大盘风控止损触发,全仓卖出
2018-05-04 止损股票列表 ['300671.SZA']
2018-05-11 大盘风控止损触发,全仓卖出
2018-05-14 止损股票列表 ['300347.SZA', '603605.SHA', '300595.SZA']
2018-05-16 大盘风控止损触发,全仓卖出
2018-05-17 大盘风控止损触发,全仓卖出
2018-05-18 大盘风控止损触发,全仓卖出
2018-05-21 止损股票列表 ['600438.SHA']
2018-05-22 大盘风控止损触发,全仓卖出
2018-05-23 大盘风控止损触发,全仓卖出
2018-05-24 大盘风控止损触发,全仓卖出
2018-05-25 大盘风控止损触发,全仓卖出
2018-05-28 止损股票列表 ['300014.SZA', '601012.SHA']
2018-05-29 大盘风控止损触发,全仓卖出
2018-05-30 大盘风控止损触发,全仓卖出
2018-06-01 止损股票列表 ['600763.SHA', '601100.SHA']
2018-06-07 大盘风控止损触发,全仓卖出
2018-06-08 大盘风控止损触发,全仓卖出
2018-06-11 大盘风控止损触发,全仓卖出
2018-06-12 止损股票列表 ['300363.SZA']
2018-06-13 大盘风控止损触发,全仓卖出
2018-06-14 大盘风控止损触发,全仓卖出
2018-06-15 大盘风控止损触发,全仓卖出
2018-06-19 大盘风控止损触发,全仓卖出
2018-06-20 大盘风控止损触发,全仓卖出
2018-06-21 大盘风控止损触发,全仓卖出
2018-06-22 止损股票列表 ['002475.SZA', '603605.SHA', '603027.SHA', '002706.SZA', '002756.SZA']
2018-06-26 大盘风控止损触发,全仓卖出
2018-06-27 大盘风控止损触发,全仓卖出
2018-06-28 大盘风控止损触发,全仓卖出
2018-07-02 大盘风控止损触发,全仓卖出
2018-07-03 止损股票列表 ['300347.SZA']
2018-07-04 大盘风控止损触发,全仓卖出
2018-07-05 大盘风控止损触发,全仓卖出
2018-07-06 止损股票列表 ['300015.SZA', '300568.SZA']
2018-07-11 大盘风控止损触发,全仓卖出
2018-07-16 大盘风控止损触发,全仓卖出
2018-07-17 大盘风控止损触发,全仓卖出
2018-07-18 大盘风控止损触发,全仓卖出
2018-07-19 大盘风控止损触发,全仓卖出
2018-07-20 止损股票列表 ['300454.SZA']
2018-07-23 止损股票列表 ['600763.SHA', '000596.SZA']
2018-07-25 止损股票列表 ['300750.SZA']
2018-07-26 大盘风控止损触发,全仓卖出
2018-07-27 大盘风控止损触发,全仓卖出
2018-07-30 大盘风控止损触发,全仓卖出
2018-07-31 大盘风控止损触发,全仓卖出
2018-08-01 大盘风控止损触发,全仓卖出
缺失风控数据！
2018-08-02 止损股票列表 ['600132.SHA', '002176.SZA']
缺失风控数据！
缺失风控数据！
2018-08-06 止损股票列表 ['600882.SHA']
缺失风控数据！
2018-08-14 大盘风控止损触发,全仓卖出
2018-08-15 大盘风控止损触发,全仓卖出
2018-08-16 大盘风控止损触发,全仓卖出
2018-08-17 大盘风控止损触发,全仓卖出
2018-08-22 止损股票列表 ['002326.SZA']
2018-08-29 大盘风控止损触发,全仓卖出
2018-08-30 大盘风控止损触发,全仓卖出
2018-08-31 大盘风控止损触发,全仓卖出
2018-09-03 大盘风控止损触发,全仓卖出
2018-09-04 止损股票列表 ['601127.SHA', '300684.SZA']
2018-09-05 大盘风控止损触发,全仓卖出
2018-09-06 大盘风控止损触发,全仓卖出
2018-09-07 大盘风控止损触发,全仓卖出
2018-09-10 大盘风控止损触发,全仓卖出
2018-09-11 大盘风控止损触发,全仓卖出
2018-09-12 大盘风控止损触发,全仓卖出
2018-09-14 止损股票列表 ['603501.SHA']
2018-09-17 大盘风控止损触发,全仓卖出
2018-09-18 止损股票列表 ['002585.SZA']
2018-09-27 大盘风控止损触发,全仓卖出
2018-09-28 大盘风控止损触发,全仓卖出
2018-10-08 大盘风控止损触发,全仓卖出
2018-10-09 大盘风控止损触发,全仓卖出
2018-10-10 大盘风控止损触发,全仓卖出
2018-10-11 大盘风控止损触发,全仓卖出
2018-10-12 大盘风控止损触发,全仓卖出
2018-10-15 大盘风控止损触发,全仓卖出
2018-10-16 止损股票列表 ['601633.SHA', '002594.SZA', '603737.SHA', '000708.SZA']
缺失风控数据！
缺失风控数据！
2018-10-18 止损股票列表 ['603223.SHA']
2018-10-24 大盘风控止损触发,全仓卖出
2018-10-25 大盘风控止损触发,全仓卖出
2018-10-26 大盘风控止损触发,全仓卖出
2018-10-29 大盘风控止损触发,全仓卖出
2018-10-30 大盘风控止损触发,全仓卖出
2018-10-31 止损股票列表 ['300552.SZA']
2018-11-01 止损股票列表 ['002594.SZA']
2018-11-05 止损股票列表 ['002475.SZA']
2018-11-06 大盘风控止损触发,全仓卖出
2018-11-07 大盘风控止损触发,全仓卖出
2018-11-08 大盘风控止损触发,全仓卖出
2018-11-09 大盘风控止损触发,全仓卖出
2018-11-12 大盘风控止损触发,全仓卖出
2018-11-14 大盘风控止损触发,全仓卖出
2018-11-20 大盘风控止损触发,全仓卖出
2018-11-21 大盘风控止损触发,全仓卖出
2018-11-22 大盘风控止损触发,全仓卖出
2018-11-23 大盘风控止损触发,全仓卖出
2018-11-26 大盘风控止损触发,全仓卖出
2018-11-27 大盘风控止损触发,全仓卖出
2018-11-28 止损股票列表 ['300316.SZA']
2018-11-29 止损股票列表 ['300677.SZA', '603456.SHA', '002326.SZA']
2018-12-05 大盘风控止损触发,全仓卖出
2018-12-06 大盘风控止损触发,全仓卖出
2018-12-07 大盘风控止损触发,全仓卖出
2018-12-10 大盘风控止损触发,全仓卖出
2018-12-11 大盘风控止损触发,全仓卖出
2018-12-14 大盘风控止损触发,全仓卖出
2018-12-17 大盘风控止损触发,全仓卖出
2018-12-18 大盘风控止损触发,全仓卖出
2018-12-19 大盘风控止损触发,全仓卖出
2018-12-20 大盘风控止损触发,全仓卖出
2018-12-21 大盘风控止损触发,全仓卖出
2018-12-24 止损股票列表 ['002407.SZA', '002594.SZA']
2018-12-27 止损股票列表 ['300274.SZA']
2019-01-02 大盘风控止损触发,全仓卖出
2019-01-03 止损股票列表 ['600132.SHA']
2019-01-08 止损股票列表 ['300035.SZA']
2019-01-14 大盘风控止损触发,全仓卖出
2019-01-15 止损股票列表 ['300595.SZA']
2019-01-17 大盘风控止损触发,全仓卖出
2019-01-18 止损股票列表 ['002850.SZA']
2019-01-22 大盘风控止损触发,全仓卖出
2019-01-23 大盘风控止损触发,全仓卖出
2019-01-24 大盘风控止损触发,全仓卖出
2019-01-25 止损股票列表 ['601888.SHA']
2019-01-28 止损股票列表 ['300035.SZA']
2019-01-29 止损股票列表 ['300274.SZA']
2019-01-30 大盘风控止损触发,全仓卖出
2019-01-31 止损股票列表 ['002850.SZA']
2019-02-15 大盘风控止损触发,全仓卖出
2019-02-19 大盘风控止损触发,全仓卖出
2019-02-20 大盘风控止损触发,全仓卖出
2019-02-21 大盘风控止损触发,全仓卖出
2019-02-27 大盘风控止损触发,全仓卖出
2019-02-28 大盘风控止损触发,全仓卖出
2019-03-01 大盘风控止损触发,全仓卖出
2019-03-04 止损股票列表 ['600438.SHA']
2019-03-05 大盘风控止损触发,全仓卖出
2019-03-06 大盘风控止损触发,全仓卖出
2019-03-07 大盘风控止损触发,全仓卖出
2019-03-08 大盘风控止损触发,全仓卖出
2019-03-11 大盘风控止损触发,全仓卖出
2019-03-13 大盘风控止损触发,全仓卖出
2019-03-14 大盘风控止损触发,全仓卖出
2019-03-15 止损股票列表 ['002920.SZA', '002487.SZA', '603985.SHA', '603396.SHA', '600499.SHA']
2019-03-18 止损股票列表 ['300083.SZA']
2019-03-21 大盘风控止损触发,全仓卖出
2019-03-22 大盘风控止损触发,全仓卖出
2019-03-25 大盘风控止损触发,全仓卖出
2019-03-26 大盘风控止损触发,全仓卖出
2019-03-27 大盘风控止损触发,全仓卖出
2019-03-28 止损股票列表 ['601100.SHA']
2019-04-08 大盘风控止损触发,全仓卖出
2019-04-09 大盘风控止损触发,全仓卖出
2019-04-10 大盘风控止损触发,全仓卖出
2019-04-11 大盘风控止损触发,全仓卖出
缺失风控数据！
2019-04-12 止损股票列表 ['601066.SHA', '603208.SHA']
2019-04-15 大盘风控止损触发,全仓卖出
2019-04-17 止损股票列表 ['300598.SZA']
2019-04-18 止损股票列表 ['300598.SZA', '603906.SHA']
2019-04-22 大盘风控止损触发,全仓卖出
2019-04-23 大盘风控止损触发,全仓卖出
2019-04-24 大盘风控止损触发,全仓卖出
2019-04-25 大盘风控止损触发,全仓卖出
2019-04-26 大盘风控止损触发,全仓卖出
2019-04-29 大盘风控止损触发,全仓卖出
2019-04-30 止损股票列表 ['002529.SZA', '601012.SHA', '600141.SHA']
2019-05-06 大盘风控止损触发,全仓卖出
2019-05-07 大盘风控止损触发,全仓卖出
2019-05-08 大盘风控止损触发,全仓卖出
2019-05-09 大盘风控止损触发,全仓卖出
2019-05-17 大盘风控止损触发,全仓卖出
2019-05-20 大盘风控止损触发,全仓卖出
2019-05-21 止损股票列表 ['600966.SHA']
2019-05-22 止损股票列表 ['000708.SZA']
2019-05-23 大盘风控止损触发,全仓卖出
2019-05-24 大盘风控止损触发,全仓卖出
2019-05-29 止损股票列表 ['603026.SHA']
2019-05-30 大盘风控止损触发,全仓卖出
2019-05-31 大盘风控止损触发,全仓卖出
2019-06-03 大盘风控止损触发,全仓卖出
2019-06-04 大盘风控止损触发,全仓卖出
2019-06-05 大盘风控止损触发,全仓卖出
2019-06-06 大盘风控止损触发,全仓卖出
2019-06-10 止损股票列表 ['603712.SHA', '603605.SHA', '300601.SZA']
2019-06-12 止损股票列表 ['603317.SHA']
2019-06-13 大盘风控止损触发,全仓卖出
2019-06-14 大盘风控止损触发,全仓卖出
2019-06-17 大盘风控止损触发,全仓卖出
2019-06-18 大盘风控止损触发,全仓卖出
2019-06-19 止损股票列表 ['601633.SHA']
2019-06-25 大盘风控止损触发,全仓卖出
2019-06-26 大盘风控止损触发,全仓卖出
2019-06-27 大盘风控止损触发,全仓卖出
2019-06-28 大盘风控止损触发,全仓卖出
2019-07-03 大盘风控止损触发,全仓卖出
2019-07-04 大盘风控止损触发,全仓卖出
2019-07-05 大盘风控止损触发,全仓卖出
2019-07-08 大盘风控止损触发,全仓卖出
2019-07-09 大盘风控止损触发,全仓卖出
2019-07-10 大盘风控止损触发,全仓卖出
2019-07-11 止损股票列表 ['002705.SZA', '002475.SZA']
2019-07-15 止损股票列表 ['002709.SZA']
2019-07-18 大盘风控止损触发,全仓卖出
2019-07-22 大盘风控止损触发,全仓卖出
2019-07-23 止损股票列表 ['300677.SZA', '300363.SZA']
2019-07-25 止损股票列表 ['600141.SHA']
2019-07-29 大盘风控止损触发,全仓卖出
2019-07-31 大盘风控止损触发,全仓卖出
2019-08-01 大盘风控止损触发,全仓卖出
2019-08-02 大盘风控止损触发,全仓卖出
2019-08-05 大盘风控止损触发,全仓卖出
2019-08-06 大盘风控止损触发,全仓卖出
2019-08-07 大盘风控止损触发,全仓卖出
2019-08-08 止损股票列表 ['300568.SZA', '600966.SHA']
2019-08-16 止损股票列表 ['603345.SHA']
2019-08-21 大盘风控止损触发,全仓卖出
2019-08-22 大盘风控止损触发,全仓卖出
2019-08-26 大盘风控止损触发,全仓卖出
2019-08-27 大盘风控止损触发,全仓卖出
2019-08-28 大盘风控止损触发,全仓卖出
2019-08-29 大盘风控止损触发,全仓卖出
2019-08-30 大盘风控止损触发,全仓卖出
2019-09-05 止损股票列表 ['300601.SZA']
2019-09-10 大盘风控止损触发,全仓卖出
2019-09-11 大盘风控止损触发,全仓卖出
2019-09-12 大盘风控止损触发,全仓卖出
2019-09-16 大盘风控止损触发,全仓卖出
2019-09-17 大盘风控止损触发,全仓卖出
2019-09-18 大盘风控止损触发,全仓卖出
2019-09-19 止损股票列表 ['600882.SHA']
2019-09-23 大盘风控止损触发,全仓卖出
2019-09-24 大盘风控止损触发,全仓卖出
2019-09-25 大盘风控止损触发,全仓卖出
2019-09-26 大盘风控止损触发,全仓卖出
2019-09-27 止损股票列表 ['300363.SZA']
2019-09-30 大盘风控止损触发,全仓卖出
2019-10-11 止损股票列表 ['300751.SZA', '300526.SZA']
2019-10-15 固定天数卖出列表 ['000799.SZA']
2019-10-16 大盘风控止损触发,全仓卖出
2019-10-17 大盘风控止损触发,全仓卖出
2019-10-18 大盘风控止损触发,全仓卖出
2019-10-21 大盘风控止损触发,全仓卖出
2019-10-22 止损股票列表 ['002326.SZA', '300363.SZA']
2019-10-23 大盘风控止损触发,全仓卖出
2019-10-24 大盘风控止损触发,全仓卖出
2019-10-25 止损股票列表 ['300595.SZA', '603027.SHA', '603345.SHA']
2019-10-29 止损股票列表 ['300012.SZA', '300496.SZA']
2019-10-30 大盘风控止损触发,全仓卖出
2019-10-31 大盘风控止损触发,全仓卖出
2019-11-06 大盘风控止损触发,全仓卖出
2019-11-07 大盘风控止损触发,全仓卖出
2019-11-08 大盘风控止损触发,全仓卖出
2019-11-11 大盘风控止损触发,全仓卖出
2019-11-12 大盘风控止损触发,全仓卖出
2019-11-13 大盘风控止损触发,全仓卖出
2019-11-15 大盘风控止损触发,全仓卖出
2019-11-18 止损股票列表 ['002791.SZA']
2019-11-21 大盘风控止损触发,全仓卖出
2019-11-22 大盘风控止损触发,全仓卖出
2019-11-26 止损股票列表 ['603713.SHA']
2019-11-28 大盘风控止损触发,全仓卖出
2019-11-29 大盘风控止损触发,全仓卖出
2019-12-02 大盘风控止损触发,全仓卖出
2019-12-11 大盘风控止损触发,全仓卖出
2019-12-12 大盘风控止损触发,全仓卖出
2019-12-16 止损股票列表 ['600132.SHA']
2019-12-19 大盘风控止损触发,全仓卖出
2019-12-20 大盘风控止损触发,全仓卖出
2019-12-23 大盘风控止损触发,全仓卖出
2019-12-24 大盘风控止损触发,全仓卖出
2019-12-25 大盘风控止损触发,全仓卖出
2019-12-27 止损股票列表 ['300151.SZA', '600110.SHA']
2019-12-30 止损股票列表 ['603501.SHA']
2020-01-03 止损股票列表 ['300767.SZA']
2020-01-06 大盘风控止损触发,全仓卖出
2020-01-07 大盘风控止损触发,全仓卖出
2020-01-08 大盘风控止损触发,全仓卖出
2020-01-10 大盘风控止损触发,全仓卖出
2020-01-14 大盘风控止损触发,全仓卖出
2020-01-15 大盘风控止损触发,全仓卖出
2020-01-16 大盘风控止损触发,全仓卖出
2020-01-17 大盘风控止损触发,全仓卖出
2020-01-21 大盘风控止损触发,全仓卖出
2020-01-22 大盘风控止损触发,全仓卖出
2020-01-23 大盘风控止损触发,全仓卖出
2020-02-03 大盘风控止损触发,全仓卖出
2020-02-04 大盘风控止损触发,全仓卖出
2020-02-05 止损股票列表 ['002920.SZA', '002245.SZA', '000155.SZA', '603605.SHA']
2020-02-05 固定天数卖出列表 ['300363.SZA', '600882.SHA']
2020-02-07 止损股票列表 ['300760.SZA', '002812.SZA']
2020-02-10 止损股票列表 ['603501.SHA']
2020-02-13 大盘风控止损触发,全仓卖出
2020-02-19 大盘风控止损触发,全仓卖出
2020-02-20 止损股票列表 ['300122.SZA', '002459.SZA']
2020-02-24 大盘风控止损触发,全仓卖出
2020-02-25 大盘风控止损触发,全仓卖出
2020-02-26 大盘风控止损触发,全仓卖出
2020-02-27 大盘风控止损触发,全仓卖出
2020-02-28 大盘风控止损触发,全仓卖出
2020-03-02 止损股票列表 ['600882.SHA', '600966.SHA']
2020-03-03 止损股票列表 ['300382.SZA']
2020-03-04 止损股票列表 ['300363.SZA']
2020-03-05 止损股票列表 ['300454.SZA']
2020-03-06 大盘风控止损触发,全仓卖出
2020-03-09 大盘风控止损触发,全仓卖出
2020-03-10 大盘风控止损触发,全仓卖出
2020-03-11 大盘风控止损触发,全仓卖出
2020-03-12 大盘风控止损触发,全仓卖出
2020-03-13 大盘风控止损触发,全仓卖出
2020-03-16 大盘风控止损触发,全仓卖出
2020-03-17 大盘风控止损触发,全仓卖出
2020-03-18 大盘风控止损触发,全仓卖出
2020-03-19 大盘风控止损触发,全仓卖出
2020-03-20 止损股票列表 ['002568.SZA']
2020-03-23 止损股票列表 ['300316.SZA', '002541.SZA', '300346.SZA']
2020-03-24 止损股票列表 ['603712.SHA']
2020-03-25 止损股票列表 ['002585.SZA']
2020-03-30 大盘风控止损触发,全仓卖出
2020-03-31 大盘风控止损触发,全仓卖出
2020-04-01 大盘风控止损触发,全仓卖出
2020-04-02 止损股票列表 ['300696.SZA']
2020-04-09 大盘风控止损触发,全仓卖出
2020-04-10 大盘风控止损触发,全仓卖出
2020-04-13 大盘风控止损触发,全仓卖出
2020-04-14 止损股票列表 ['300363.SZA']
2020-04-15 大盘风控止损触发,全仓卖出
2020-04-16 大盘风控止损触发,全仓卖出
2020-04-21 大盘风控止损触发,全仓卖出
2020-04-22 大盘风控止损触发,全仓卖出
2020-04-23 大盘风控止损触发,全仓卖出
2020-04-24 大盘风控止损触发,全仓卖出
2020-04-27 大盘风控止损触发,全仓卖出
2020-04-28 止损股票列表 ['603712.SHA']
2020-05-07 大盘风控止损触发,全仓卖出
2020-05-08 止损股票列表 ['603456.SHA']
2020-05-11 大盘风控止损触发,全仓卖出
2020-05-12 大盘风控止损触发,全仓卖出
2020-05-13 大盘风控止损触发,全仓卖出
2020-05-14 大盘风控止损触发,全仓卖出
2020-05-15 大盘风控止损触发,全仓卖出
2020-05-18 大盘风控止损触发,全仓卖出
2020-05-20 止损股票列表 ['002709.SZA']
2020-05-21 大盘风控止损触发,全仓卖出
2020-05-22 大盘风控止损触发,全仓卖出
2020-05-25 大盘风控止损触发,全仓卖出
2020-05-26 止损股票列表 ['688116.SHA']
2020-05-27 止损股票列表 ['300454.SZA']
2020-05-28 止损股票列表 ['600966.SHA']
2020-05-29 止损股票列表 ['600532.SHA']
2020-06-02 止损股票列表 ['002568.SZA', '603129.SHA']
2020-06-03 止损股票列表 ['603345.SHA', '603208.SHA']
2020-06-04 大盘风控止损触发,全仓卖出
2020-06-05 大盘风控止损触发,全仓卖出
2020-06-08 大盘风控止损触发,全仓卖出
2020-06-09 止损股票列表 ['600882.SHA', '300677.SZA']
2020-06-10 大盘风控止损触发,全仓卖出
2020-06-11 大盘风控止损触发,全仓卖出
2020-06-12 大盘风控止损触发,全仓卖出
2020-06-15 大盘风控止损触发,全仓卖出
2020-06-16 止损股票列表 ['600966.SHA', '002812.SZA']
2020-06-22 止损股票列表 ['603208.SHA']
2020-06-29 大盘风控止损触发,全仓卖出
2020-07-02 止损股票列表 ['002541.SZA']
2020-07-03 止损股票列表 ['002705.SZA']
2020-07-06 止损股票列表 ['600763.SHA']
2020-07-08 止损股票列表 ['300151.SZA']
2020-07-09 大盘风控止损触发,全仓卖出
2020-07-10 大盘风控止损触发,全仓卖出
2020-07-13 大盘风控止损触发,全仓卖出
2020-07-14 大盘风控止损触发,全仓卖出
2020-07-15 大盘风控止损触发,全仓卖出
2020-07-16 大盘风控止损触发,全仓卖出
2020-07-17 大盘风控止损触发,全仓卖出
2020-07-20 止损股票列表 ['002850.SZA']
2020-07-21 止损股票列表 ['600862.SHA']
2020-07-24 大盘风控止损触发,全仓卖出
2020-07-27 大盘风控止损触发,全仓卖出
2020-07-28 止损股票列表 ['603260.SHA', '300552.SZA']
2020-07-30 止损股票列表 ['603456.SHA']
2020-07-31 固定天数卖出列表 ['002240.SZA', '002594.SZA']
2020-08-04 止损股票列表 ['000708.SZA']
2020-08-05 大盘风控止损触发,全仓卖出
2020-08-06 大盘风控止损触发,全仓卖出
2020-08-07 大盘风控止损触发,全仓卖出
2020-08-10 大盘风控止损触发,全仓卖出
2020-08-11 大盘风控止损触发,全仓卖出
2020-08-12 大盘风控止损触发,全仓卖出
2020-08-13 大盘风控止损触发,全仓卖出
2020-08-17 止损股票列表 ['600316.SHA']
2020-08-19 大盘风控止损触发,全仓卖出
2020-08-20 大盘风控止损触发,全仓卖出
2020-08-21 大盘风控止损触发,全仓卖出
2020-08-24 止损股票列表 ['603806.SHA', '300671.SZA']
2020-08-25 止损股票列表 ['600966.SHA']
2020-08-26 大盘风控止损触发,全仓卖出
2020-08-27 大盘风控止损触发,全仓卖出
2020-08-28 止损股票列表 ['600096.SHA']
2020-08-31 止损股票列表 ['002791.SZA']
2020-09-01 止损股票列表 ['603906.SHA']
2020-09-02 大盘风控止损触发,全仓卖出
2020-09-03 大盘风控止损触发,全仓卖出
2020-09-04 大盘风控止损触发,全仓卖出
2020-09-07 大盘风控止损触发,全仓卖出
2020-09-08 大盘风控止损触发,全仓卖出
2020-09-09 大盘风控止损触发,全仓卖出
2020-09-10 大盘风控止损触发,全仓卖出
2020-09-11 止损股票列表 ['000596.SZA', '300767.SZA', '002967.SZA', '688202.SHA', '603267.SHA']
2020-09-17 大盘风控止损触发,全仓卖出
2020-09-21 止损股票列表 ['300432.SZA', '002487.SZA']
2020-09-22 大盘风控止损触发,全仓卖出
2020-09-23 大盘风控止损触发,全仓卖出
2020-09-24 大盘风控止损触发,全仓卖出
2020-09-25 大盘风控止损触发,全仓卖出
2020-09-28 止损股票列表 ['600110.SHA', '300763.SZA', '300035.SZA']
2020-09-29 止损股票列表 ['603129.SHA']
2020-10-09 止损股票列表 ['601888.SHA']
2020-10-14 大盘风控止损触发,全仓卖出
2020-10-15 大盘风控止损触发,全仓卖出
2020-10-16 大盘风控止损触发,全仓卖出
2020-10-19 大盘风控止损触发,全仓卖出
2020-10-20 大盘风控止损触发,全仓卖出
2020-10-21 大盘风控止损触发,全仓卖出
2020-10-22 大盘风控止损触发,全仓卖出
2020-10-23 大盘风控止损触发,全仓卖出
2020-10-26 大盘风控止损触发,全仓卖出
2020-10-27 大盘风控止损触发,全仓卖出
2020-10-28 止损股票列表 ['600316.SHA']
2020-10-30 大盘风控止损触发,全仓卖出
2020-11-02 大盘风控止损触发,全仓卖出
2020-11-04 止损股票列表 ['300763.SZA', '002459.SZA']
2020-11-06 止损股票列表 ['688198.SHA']
2020-11-09 止损股票列表 ['300696.SZA', '600532.SHA']
2020-11-10 大盘风控止损触发,全仓卖出
2020-11-11 大盘风控止损触发,全仓卖出
2020-11-12 大盘风控止损触发,全仓卖出
2020-11-13 大盘风控止损触发,全仓卖出
2020-11-16 大盘风控止损触发,全仓卖出
2020-11-17 大盘风控止损触发,全仓卖出
2020-11-18 大盘风控止损触发,全仓卖出
2020-11-19 止损股票列表 ['300693.SZA']
2020-11-23 止盈股票列表 ['601127.SHA']
2020-11-24 大盘风控止损触发,全仓卖出
2020-11-25 大盘风控止损触发,全仓卖出
2020-11-26 大盘风控止损触发,全仓卖出
2020-11-27 止损股票列表 ['300432.SZA', '002568.SZA']
2020-12-01 止损股票列表 ['603223.SHA', '002709.SZA']
2020-12-02 止损股票列表 ['002245.SZA', '601633.SHA']
2020-12-03 大盘风控止损触发,全仓卖出
2020-12-04 大盘风控止损触发,全仓卖出
2020-12-07 大盘风控止损触发,全仓卖出
2020-12-08 大盘风控止损触发,全仓卖出
2020-12-09 大盘风控止损触发,全仓卖出
2020-12-10 大盘风控止损触发,全仓卖出
2020-12-11 大盘风控止损触发,全仓卖出
2020-12-14 止损股票列表 ['600111.SHA', '300661.SZA']
2020-12-16 止损股票列表 ['002600.SZA']
2020-12-21 止损股票列表 ['603713.SHA']
2020-12-22 大盘风控止损触发,全仓卖出
2020-12-23 大盘风控止损触发,全仓卖出
2020-12-24 大盘风控止损触发,全仓卖出
2020-12-25 止损股票列表 ['603906.SHA']
2020-12-28 止损股票列表 ['300568.SZA']
2020-12-29 大盘风控止损触发,全仓卖出
2020-12-30 止损股票列表 ['688599.SHA', '002240.SZA']
2021-01-06 止损股票列表 ['688116.SHA', '603906.SHA']
2021-01-07 止损股票列表 ['600096.SHA']
2021-01-08 大盘风控止损触发,全仓卖出
2021-01-11 大盘风控止损触发,全仓卖出
2021-01-12 止损股票列表 ['603713.SHA', '600532.SHA', '601865.SHA', '002985.SZA']
2021-01-13 大盘风控止损触发,全仓卖出
2021-01-14 大盘风控止损触发,全仓卖出
2021-01-15 大盘风控止损触发,全仓卖出
2021-01-18 大盘风控止损触发,全仓卖出
2021-01-19 大盘风控止损触发,全仓卖出
2021-01-20 大盘风控止损触发,全仓卖出
2021-01-26 大盘风控止损触发,全仓卖出
2021-01-27 大盘风控止损触发,全仓卖出
2021-01-28 大盘风控止损触发,全仓卖出
2021-01-29 大盘风控止损触发,全仓卖出
2021-02-01 止损股票列表 ['000301.SZA', '002705.SZA', '300750.SZA']
2021-02-03 止损股票列表 ['600532.SHA']
2021-02-05 止损股票列表 ['603906.SHA', '300382.SZA']
2021-02-08 止损股票列表 ['688202.SHA']
2021-02-18 止损股票列表 ['002607.SZA', '600763.SHA', '688202.SHA', '300750.SZA']
2021-02-18 固定天数卖出列表 ['688116.SHA']
2021-02-19 大盘风控止损触发,全仓卖出
2021-02-22 大盘风控止损触发,全仓卖出
2021-02-23 大盘风控止损触发,全仓卖出
2021-02-24 大盘风控止损触发,全仓卖出
2021-02-25 大盘风控止损触发,全仓卖出
2021-02-26 大盘风控止损触发,全仓卖出
2021-03-01 止损股票列表 ['002568.SZA', '003022.SZA', '603638.SHA', '601888.SHA']
2021-03-02 止损股票列表 ['002967.SZA', '600141.SHA']
2021-03-04 大盘风控止损触发,全仓卖出
2021-03-05 大盘风控止损触发,全仓卖出
2021-03-08 大盘风控止损触发,全仓卖出
2021-03-09 大盘风控止损触发,全仓卖出
2021-03-10 止损股票列表 ['300083.SZA', '002472.SZA', '300363.SZA', '300390.SZA']
2021-03-19 大盘风控止损触发,全仓卖出
2021-03-22 大盘风控止损触发,全仓卖出
2021-03-23 大盘风控止损触发,全仓卖出
2021-03-24 大盘风控止损触发,全仓卖出
2021-03-25 大盘风控止损触发,全仓卖出
2021-03-31 大盘风控止损触发,全仓卖出
2021-04-01 止损股票列表 ['000995.SZA', '300390.SZA']
2021-04-06 大盘风控止损触发,全仓卖出
2021-04-07 大盘风控止损触发,全仓卖出
2021-04-08 大盘风控止损触发,全仓卖出
2021-04-09 大盘风控止损触发,全仓卖出
2021-04-12 大盘风控止损触发,全仓卖出
2021-04-13 大盘风控止损触发,全仓卖出
2021-04-21 止损股票列表 ['600399.SHA']
2021-04-22 大盘风控止损触发,全仓卖出
2021-04-26 大盘风控止损触发,全仓卖出
2021-04-27 大盘风控止损触发,全仓卖出
2021-04-30 大盘风控止损触发,全仓卖出
2021-05-06 大盘风控止损触发,全仓卖出
2021-05-07 大盘风控止损触发,全仓卖出
2021-05-10 大盘风控止损触发,全仓卖出
2021-05-11 止损股票列表 ['300724.SZA', '300496.SZA', '300035.SZA', '603208.SHA', '603345.SHA', '300671.SZA']
2021-05-13 大盘风控止损触发,全仓卖出
2021-05-18 止损股票列表 ['600532.SHA', '300601.SZA']
2021-05-19 大盘风控止损触发,全仓卖出
2021-05-20 大盘风控止损触发,全仓卖出
2021-05-21 大盘风控止损触发,全仓卖出
2021-05-24 大盘风控止损触发,全仓卖出
2021-05-26 止损股票列表 ['002568.SZA', '002920.SZA']
2021-05-28 大盘风控止损触发,全仓卖出
2021-05-31 大盘风控止损触发,全仓卖出
2021-06-01 大盘风控止损触发,全仓卖出
2021-06-02 大盘风控止损触发,全仓卖出
2021-06-03 大盘风控止损触发,全仓卖出
2021-06-04 大盘风控止损触发,全仓卖出
2021-06-07 大盘风控止损触发,全仓卖出
2021-06-08 大盘风控止损触发,全仓卖出
2021-06-09 大盘风控止损触发,全仓卖出
2021-06-10 止损股票列表 ['601066.SHA']
2021-06-11 大盘风控止损触发,全仓卖出
2021-06-15 大盘风控止损触发,全仓卖出
2021-06-16 大盘风控止损触发,全仓卖出
2021-06-17 大盘风控止损触发,全仓卖出
2021-06-24 止损股票列表 ['603456.SHA']
2021-06-29 大盘风控止损触发,全仓卖出
2021-06-30 大盘风控止损触发,全仓卖出
2021-07-01 大盘风控止损触发,全仓卖出
2021-07-02 大盘风控止损触发,全仓卖出
2021-07-05 大盘风控止损触发,全仓卖出
2021-07-06 大盘风控止损触发,全仓卖出
2021-07-07 止损股票列表 ['300777.SZA', '600763.SHA', '600862.SHA', '300684.SZA']
2021-07-08 止盈股票列表 ['300432.SZA']
2021-07-08 止损股票列表 ['600096.SHA']
2021-07-09 大盘风控止损触发,全仓卖出
2021-07-13 止损股票列表 ['300526.SZA']
2021-07-14 大盘风控止损触发,全仓卖出
2021-07-15 止损股票列表 ['600584.SHA', '601633.SHA', '002594.SZA']
2021-07-16 大盘风控止损触发,全仓卖出
2021-07-19 大盘风控止损触发,全仓卖出
2021-07-20 大盘风控止损触发,全仓卖出
2021-07-23 大盘风控止损触发,全仓卖出
2021-07-26 大盘风控止损触发,全仓卖出
2021-07-27 大盘风控止损触发,全仓卖出
2021-07-28 大盘风控止损触发,全仓卖出
2021-07-29 止损股票列表 ['002791.SZA', '688198.SHA', '300748.SZA']
2021-08-02 止损股票列表 ['300432.SZA']
2021-08-03 止损股票列表 ['603806.SHA', '300850.SZA']
2021-08-06 大盘风控止损触发,全仓卖出
2021-08-10 止损股票列表 ['603127.SHA']
2021-08-11 大盘风控止损触发,全仓卖出
2021-08-12 大盘风控止损触发,全仓卖出
2021-08-13 大盘风控止损触发,全仓卖出
2021-08-16 大盘风控止损触发,全仓卖出
2021-08-17 大盘风控止损触发,全仓卖出
2021-08-18 止损股票列表 ['603906.SHA', '002985.SZA', '002459.SZA']
2021-08-20 大盘风控止损触发,全仓卖出
2021-08-25 止盈股票列表 ['603260.SHA']
2021-08-26 大盘风控止损触发,全仓卖出
2021-08-27 大盘风控止损触发,全仓卖出
2021-08-30 大盘风控止损触发,全仓卖出
2021-08-31 大盘风控止损触发,全仓卖出
2021-09-01 止损股票列表 ['603712.SHA', '300724.SZA', '002985.SZA', '002529.SZA']
2021-09-02 止损股票列表 ['002472.SZA', '300696.SZA', '300171.SZA']
2021-09-03 大盘风控止损触发,全仓卖出
2021-09-06 止损股票列表 ['601865.SHA', '600141.SHA']
2021-09-08 止损股票列表 ['300850.SZA']
2021-09-09 大盘风控止损触发,全仓卖出
2021-09-10 止损股票列表 ['601633.SHA', '603985.SHA', '002487.SZA']
2021-09-13 大盘风控止损触发,全仓卖出
2021-09-14 大盘风控止损触发,全仓卖出
2021-09-15 大盘风控止损触发,全仓卖出
2021-09-16 大盘风控止损触发,全仓卖出
2021-09-17 止损股票列表 ['000708.SZA']
2021-09-23 止损股票列表 ['300568.SZA', '688116.SHA', '300432.SZA']
2021-09-24 止损股票列表 ['300850.SZA']
2021-09-27 止损股票列表 ['300382.SZA', '002487.SZA', '300443.SZA']
2021-09-29 大盘风控止损触发,全仓卖出
2021-09-30 止损股票列表 ['603127.SHA']
2021-10-08 止损股票列表 ['603985.SHA', '000733.SZA']
2021-10-11 止损股票列表 ['688202.SHA', '601066.SHA', '603345.SHA', '300274.SZA']
2021-10-12 大盘风控止损触发,全仓卖出
2021-10-13 止损股票列表 ['600956.SHA', '300598.SZA', '603906.SHA']
2021-10-14 大盘风控止损触发,全仓卖出
2021-10-15 大盘风控止损触发,全仓卖出
2021-10-18 大盘风控止损触发,全仓卖出
2021-10-19 止损股票列表 ['000858.SZA']
2021-10-20 大盘风控止损触发,全仓卖出
2021-10-22 止损股票列表 ['002529.SZA']
2021-10-26 大盘风控止损触发,全仓卖出
2021-10-27 大盘风控止损触发,全仓卖出
2021-10-28 大盘风控止损触发,全仓卖出
- 收益率848.15%
- 年化收益率84.19%
- 基准收益率21.78%
- 阿尔法0.79
- 贝塔0.5
- 夏普比率2.9
- 胜率0.57
- 盈亏比1.65
- 收益波动率20.8%
- 信息比率0.17
- 最大回撤18.99%
bigcharts-data-start/{"__type":"tabs","__id":"bigchart-4ff87c7edcd54acfa9994b5363fa28af"}/bigcharts-data-end