{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-52:input_data","from_node_id":"-62:data"}],"nodes":[{"node_id":"-52","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)\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":"drop_na_label","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cast_label_int","value":"True","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":"-52"}],"output_ports":[{"name":"data","node_id":"-52"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-62","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"bar1d_CN_FUTURE","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"2021-12-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-12-09","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-62"},{"name":"features","node_id":"-62"}],"output_ports":[{"name":"data","node_id":"-62"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-52' Position='211.9209747314453,-203.85848999023438,200,200'/><node_position Node='-62' Position='148.56947326660156,-287.95989990234375,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2021年12月10日 17:39
# 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。


m4 = M.use_datasource.v1(
    datasource_id='bar1d_CN_FUTURE',
    start_date='2021-12-01',
    end_date='2021-12-09'
)

m3 = M.auto_labeler_on_datasource.v1(
    input_data=m4.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
# 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)
""",
    drop_na_label=True,
    cast_label_int=True,
    date_col='date',
    instrument_col='instrument',
    user_functions={}
)

[2021-12-10 17:39:06.578671] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2021-12-10 17:39:18.523721] INFO: moduleinvoker: use_datasource.v1 运行完成[11.945087s].

[2021-12-10 17:39:18.544151] INFO: moduleinvoker: auto_labeler_on_datasource.v1 开始运行..

[2021-12-10 17:39:18.670720] INFO: 自动标注(任意数据源): 开始标注 ..

[2021-12-10 17:39:18.922557] INFO: moduleinvoker: auto_labeler_on_datasource.v1 运行完成[0.378386s].