{"description":"实验创建于2022/2/10","graph":{"edges":[{"to_node_id":"-20:instruments","from_node_id":"-4:data"},{"to_node_id":"-31:instruments","from_node_id":"-4:data"},{"to_node_id":"-47:features","from_node_id":"-12:data"},{"to_node_id":"-71:data1","from_node_id":"-20:data"},{"to_node_id":"-38:input_data","from_node_id":"-31:data"},{"to_node_id":"-71:data2","from_node_id":"-38:data"},{"to_node_id":"-54:features","from_node_id":"-47:data"},{"to_node_id":"-82:input_data","from_node_id":"-54:data"},{"to_node_id":"-31:features","from_node_id":"-66:data"},{"to_node_id":"-38:features","from_node_id":"-66:data"},{"to_node_id":"-47:instruments","from_node_id":"-66:data"},{"to_node_id":"-54:input_data","from_node_id":"-66:data"},{"to_node_id":"-90:test_ds","from_node_id":"-66:data"},{"to_node_id":"-78:input_data","from_node_id":"-71:data"},{"to_node_id":"-90:features","from_node_id":"-78:data"}],"nodes":[{"node_id":"-4","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2019-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-12-31","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":"-4"}],"output_ports":[{"name":"data","node_id":"-4"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-12","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2021-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-12-31","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":"-12"}],"output_ports":[{"name":"data","node_id":"-12"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-20","module_id":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","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# 添加benchmark_前缀，可使用对应的benchmark数据\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)\nshift(close, -5) / shift(open, -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))\n\n# 将分数映射到分类，这里使用20个分类\nall_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)\nwhere(shift(high, -1) == shift(low, -1), NaN, label)\n","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},{"name":"benchmark","value":"000300.SHA","type":"Literal","bound_global_parameter":null},{"name":"drop_na_label","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cast_label_int","value":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-20"}],"output_ports":[{"name":"data","node_id":"-20"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-31","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":"-31"},{"name":"features","node_id":"-31"}],"output_ports":[{"name":"data","node_id":"-31"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-38","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":"-38"},{"name":"features","node_id":"-38"}],"output_ports":[{"name":"data","node_id":"-38"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-47","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":"-47"},{"name":"features","node_id":"-47"}],"output_ports":[{"name":"data","node_id":"-47"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"-54","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":"-54"},{"name":"features","node_id":"-54"}],"output_ports":[{"name":"data","node_id":"-54"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"-66","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\nreturn_5\nreturn_10\nreturn_20\navg_amount_0/avg_amount_5\navg_amount_5/avg_amount_20\nrank_avg_amount_0/rank_avg_amount_5\nrank_avg_amount_5/rank_avg_amount_10\nrank_return_0\nrank_return_5\nrank_return_10\nrank_return_0/rank_return_5\nrank_return_5/rank_return_10\npe_ttm_0\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-66"}],"output_ports":[{"name":"data","node_id":"-66"}],"cacheable":true,"seq_num":9,"comment":"","comment_collapsed":true},{"node_id":"-71","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":"-71"},{"name":"data2","node_id":"-71"}],"output_ports":[{"name":"data","node_id":"-71"}],"cacheable":true,"seq_num":10,"comment":"","comment_collapsed":true},{"node_id":"-78","module_id":"BigQuantSpace.dropnan.dropnan-v2","parameters":[],"input_ports":[{"name":"input_data","node_id":"-78"},{"name":"features","node_id":"-78"}],"output_ports":[{"name":"data","node_id":"-78"}],"cacheable":true,"seq_num":11,"comment":"","comment_collapsed":true},{"node_id":"-82","module_id":"BigQuantSpace.dropnan.dropnan-v2","parameters":[],"input_ports":[{"name":"input_data","node_id":"-82"},{"name":"features","node_id":"-82"}],"output_ports":[{"name":"data","node_id":"-82"}],"cacheable":true,"seq_num":12,"comment":"","comment_collapsed":true},{"node_id":"-90","module_id":"BigQuantSpace.stock_ranker_train.stock_ranker_train-v6","parameters":[{"name":"learning_algorithm","value":"排序","type":"Literal","bound_global_parameter":null},{"name":"number_of_leaves","value":30,"type":"Literal","bound_global_parameter":null},{"name":"minimum_docs_per_leaf","value":1000,"type":"Literal","bound_global_parameter":null},{"name":"number_of_trees","value":20,"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":"data_row_fraction","value":1,"type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"ndcg_discount_base","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":"-90"},{"name":"features","node_id":"-90"},{"name":"test_ds","node_id":"-90"},{"name":"base_model","node_id":"-90"}],"output_ports":[{"name":"model","node_id":"-90"},{"name":"feature_gains","node_id":"-90"},{"name":"m_lazy_run","node_id":"-90"}],"cacheable":true,"seq_num":13,"comment":"","comment_collapsed":true},{"node_id":"-106","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":"-106"},{"name":"data","node_id":"-106"}],"output_ports":[{"name":"predictions","node_id":"-106"},{"name":"m_lazy_run","node_id":"-106"}],"cacheable":true,"seq_num":14,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-4' Position='95,121,200,200'/><node_position Node='-12' Position='502,123,200,200'/><node_position Node='-20' Position='-81,250,200,200'/><node_position Node='-31' Position='124,354,200,200'/><node_position Node='-38' Position='129,465,200,200'/><node_position Node='-47' Position='536,342,200,200'/><node_position Node='-54' Position='516,468,200,200'/><node_position Node='-66' Position='360,13,200,200'/><node_position Node='-71' Position='126,601,200,200'/><node_position Node='-78' Position='122,709,200,200'/><node_position Node='-82' Position='522,710,200,200'/><node_position Node='-90' Position='218,806.9732055664062,200,200'/><node_position Node='-106' Position='691,801,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2022年2月10日 15:49
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


m1 = M.instruments.v2(
    start_date='2019-01-01',
    end_date='2020-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    label_expr="""# #号开始的表示注释
# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段
# 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
#   添加benchmark_前缀，可使用对应的benchmark数据
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_

# 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)
shift(close, -5) / shift(open, -1)

# 极值处理：用1%和99%分位的值做clip
clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))

# 将分数映射到分类，这里使用20个分类
all_wbins(label, 20)

# 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)
where(shift(high, -1) == shift(low, -1), NaN, label)
""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=True,
    user_functions={}
)

m2 = M.instruments.v2(
    start_date='2021-01-01',
    end_date='2021-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m9 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
return_5
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_20
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_0/rank_return_5
rank_return_5/rank_return_10
pe_ttm_0
"""
)

m4 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m9.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m5 = M.derived_feature_extractor.v3(
    input_data=m4.data,
    features=m9.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m10 = M.join.v3(
    data1=m3.data,
    data2=m5.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m11 = M.dropnan.v2(
    input_data=m10.data
)

m6 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m7 = M.derived_feature_extractor.v3(
    input_data=m9.data,
    features=m6.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m12 = M.dropnan.v2(
    input_data=m7.data
)

m13 = M.stock_ranker_train.v6(
    features=m11.data,
    test_ds=m9.data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=1000,
    number_of_trees=20,
    learning_rate=0.1,
    max_bins=1023,
    feature_fraction=1,
    data_row_fraction=1,
    plot_charts=True,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m14 = M.stock_ranker_predict.v5(
    m_lazy_run=False
)

[2022-02-10 15:47:35.096742] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-02-10 15:47:35.322433] INFO: moduleinvoker: 命中缓存

[2022-02-10 15:47:35.387026] INFO: moduleinvoker: instruments.v2 运行完成[0.290292s].

[2022-02-10 15:47:35.395761] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-02-10 15:47:35.485424] INFO: moduleinvoker: 命中缓存

[2022-02-10 15:47:35.487307] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.091548s].

[2022-02-10 15:47:35.491499] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-02-10 15:47:35.499364] INFO: moduleinvoker: 命中缓存

[2022-02-10 15:47:35.500714] INFO: moduleinvoker: input_features.v1 运行完成[0.00921s].

[2022-02-10 15:47:35.512815] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-02-10 15:47:35.601005] INFO: moduleinvoker: 命中缓存

[2022-02-10 15:47:35.602513] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.089698s].

[2022-02-10 15:47:35.609500] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-02-10 15:47:35.699483] INFO: moduleinvoker: 命中缓存

[2022-02-10 15:47:35.701120] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.091613s].

[2022-02-10 15:47:35.709146] INFO: moduleinvoker: join.v3 开始运行..

[2022-02-10 15:47:35.793843] INFO: moduleinvoker: 命中缓存

[2022-02-10 15:47:35.795489] INFO: moduleinvoker: join.v3 运行完成[0.086338s].

[2022-02-10 15:47:35.803236] INFO: moduleinvoker: dropnan.v2 开始运行..

[2022-02-10 15:47:35.813099] INFO: moduleinvoker: 命中缓存

[2022-02-10 15:47:35.886323] INFO: moduleinvoker: dropnan.v2 运行完成[0.083077s].

[2022-02-10 15:47:35.893451] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2022-02-10 15:47:35.902171] ERROR: moduleinvoker: module name: stock_ranker_train, module version: v6, trackeback: TypeError: __init__() takes at least 3 positional arguments (1 given)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-4-a66ee86ff4eb> in <module>
     89 )
     90 
---> 91 m13 = M.stock_ranker_train.v6(
     92     features=m11.data,
     93     test_ds=m9.data,

TypeError: __init__() takes at least 3 positional arguments (1 given)