{"description":"实验创建于2020/2/14","graph":{"edges":[{"to_node_id":"-8309:features","from_node_id":"-70:data"},{"to_node_id":"-2650:factors_info","from_node_id":"-8309:save_data"},{"to_node_id":"-8309:user_factor_data","from_node_id":"-298:data"}],"nodes":[{"node_id":"-70","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\nopen_0\nhigh_0\nclose_0\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-70"}],"output_ports":[{"name":"data","node_id":"-70"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-8309","module_id":"BigQuantSpace.factorlens.factorlens-v2","parameters":[{"name":"title","value":"因子分析: {factor_name}","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"2019-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2019-12-31","type":"Literal","bound_global_parameter":null},{"name":"rebalance_period","value":22,"type":"Literal","bound_global_parameter":null},{"name":"delay_rebalance_days","value":0,"type":"Literal","bound_global_parameter":null},{"name":"rebalance_price","value":"close_0","type":"Literal","bound_global_parameter":null},{"name":"stock_pool","value":"全市场","type":"Literal","bound_global_parameter":null},{"name":"quantile_count","value":5,"type":"Literal","bound_global_parameter":null},{"name":"commission_rate","value":0.0016,"type":"Literal","bound_global_parameter":null},{"name":"returns_calculation_method","value":"累乘","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"无","type":"Literal","bound_global_parameter":null},{"name":"drop_new_stocks","value":60,"type":"Literal","bound_global_parameter":null},{"name":"drop_price_limit_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"drop_st_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"drop_suspended_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cutoutliers","value":"True","type":"Literal","bound_global_parameter":null},{"name":"normalization","value":"True","type":"Literal","bound_global_parameter":null},{"name":"neutralization","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E8%A1%8C%E4%B8%9A%22%2C%22displayValue%22%3A%22%E8%A1%8C%E4%B8%9A%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%B8%82%E5%80%BC%22%2C%22displayValue%22%3A%22%E5%B8%82%E5%80%BC%22%2C%22selected%22%3Atrue%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"metrics","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%A8%E7%8E%B0%E6%A6%82%E8%A7%88%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%A8%E7%8E%B0%E6%A6%82%E8%A7%88%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%8C%E4%B8%9A%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%8C%E4%B8%9A%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%B8%82%E5%80%BC%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%B8%82%E5%80%BC%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22IC%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22IC%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B9%B0%E5%85%A5%E4%BF%A1%E5%8F%B7%E9%87%8D%E5%90%88%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E4%B9%B0%E5%85%A5%E4%BF%A1%E5%8F%B7%E9%87%8D%E5%90%88%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E4%BC%B0%E5%80%BC%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E4%BC%B0%E5%80%BC%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E6%8B%A5%E6%8C%A4%E5%BA%A6%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E6%8B%A5%E6%8C%A4%E5%BA%A6%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%80%BC%E6%9C%80%E5%A4%A7%2F%E6%9C%80%E5%B0%8F%E8%82%A1%E7%A5%A8%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%80%BC%E6%9C%80%E5%A4%A7%2F%E6%9C%80%E5%B0%8F%E8%82%A1%E7%A5%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E8%A1%A8%E8%BE%BE%E5%BC%8F%E5%9B%A0%E5%AD%90%E5%80%BC%22%2C%22displayValue%22%3A%22%E8%A1%A8%E8%BE%BE%E5%BC%8F%E5%9B%A0%E5%AD%90%E5%80%BC%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%A4%9A%E5%9B%A0%E5%AD%90%E7%9B%B8%E5%85%B3%E6%80%A7%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%A4%9A%E5%9B%A0%E5%AD%90%E7%9B%B8%E5%85%B3%E6%80%A7%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"factor_coverage","value":0.5,"type":"Literal","bound_global_parameter":null},{"name":"user_data_merge","value":"left","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features","node_id":"-8309"},{"name":"user_factor_data","node_id":"-8309"}],"output_ports":[{"name":"data","node_id":"-8309"},{"name":"save_data","node_id":"-8309"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-2650","module_id":"BigQuantSpace.factorlens_preservation.factorlens_preservation-v2","parameters":[{"name":"factor_fields","value":"# 定义因子名称\n# {\n# \"列名\": {'name': \"因子名\", 'desc': \"因子描述\"},\n# \"列名\": {'name': \"因子名\", 'desc': \"因子描述\"},\n# ... \n# }\n{}\n","type":"Literal","bound_global_parameter":null},{"name":"table","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"factors_info","node_id":"-2650"}],"output_ports":[{"name":"data","node_id":"-2650"}],"cacheable":false,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-298","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"300014.SZA\n002202.SZA\n600236.SHA\n600884.SHA\n300037.SZA\n002266.SZA\n300073.SZA\n600732.SHA\n002340.SZA\n002129.SZA\n601908.SHA\n300316.SZA\n601012.SHA\n603806.SHA\n601985.SHA\n603659.SHA\n300724.SZA\n300751.SZA\n300763.SZA\n688005.SHA\n003816.SZA\n688116.SHA\n688599.SHA\n300919.SZA\n600032.SHA\n600905.SHA\n600900.SHA\n600438.SHA\n600674.SHA\n002459.SZA\n002506.SZA\n300070.SZA\n002074.SZA\n600886.SHA\n000883.SZA\n300274.SZA\n002709.SZA\n300450.SZA\n603568.SHA\n603026.SHA\n002812.SZA\n603218.SHA\n600025.SHA\n300750.SZA\n603185.SHA\n601615.SHA\n601865.SHA\n688408.SHA\n688390.SHA\n003035.SZA\n","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-298"}],"output_ports":[{"name":"data","node_id":"-298"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-70' Position='93.46124267578125,167.1500244140625,200,200'/><node_position Node='-8309' Position='298,309,200,200'/><node_position Node='-2650' Position='243,457,200,200'/><node_position Node='-298' Position='410.85150146484375,149.46026611328125,200,200'/></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3月14日 08:59
# 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.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
open_0
high_0
close_0
"""
)

m4 = M.instruments.v2(
    start_date='',
    end_date='',
    market='CN_STOCK_A',
    instrument_list="""300014.SZA
002202.SZA
600236.SHA
600884.SHA
300037.SZA
002266.SZA
300073.SZA
600732.SHA
002340.SZA
002129.SZA
601908.SHA
300316.SZA
601012.SHA
603806.SHA
601985.SHA
603659.SHA
300724.SZA
300751.SZA
300763.SZA
688005.SHA
003816.SZA
688116.SHA
688599.SHA
300919.SZA
600032.SHA
600905.SHA
600900.SHA
600438.SHA
600674.SHA
002459.SZA
002506.SZA
300070.SZA
002074.SZA
600886.SHA
000883.SZA
300274.SZA
002709.SZA
300450.SZA
603568.SHA
603026.SHA
002812.SZA
603218.SHA
600025.SHA
300750.SZA
603185.SHA
601615.SHA
601865.SHA
688408.SHA
688390.SHA
003035.SZA
""",
    max_count=0
)

m2 = M.factorlens.v2(
    features=m1.data,
    user_factor_data=m4.data,
    title='因子分析: {factor_name}',
    start_date='2019-01-01',
    end_date='2019-12-31',
    rebalance_period=22,
    delay_rebalance_days=0,
    rebalance_price='close_0',
    stock_pool='全市场',
    quantile_count=5,
    commission_rate=0.0016,
    returns_calculation_method='累乘',
    benchmark='无',
    drop_new_stocks=60,
    drop_price_limit_stocks=True,
    drop_st_stocks=True,
    drop_suspended_stocks=True,
    cutoutliers=True,
    normalization=True,
    neutralization=['行业', '市值'],
    metrics=['因子表现概览', '因子分布', '因子行业分布', '因子市值分布', 'IC分析', '买入信号重合分析', '因子估值分析', '因子拥挤度分析', '因子值最大/最小股票', '表达式因子值', '多因子相关性分析'],
    factor_coverage=0.5,
    user_data_merge='left'
)

m3 = M.factorlens_preservation.v2(
    factors_info=m2.save_data,
    factor_fields=# 定义因子名称
# {
#     "列名": {'name': "因子名", 'desc': "因子描述"},
#     "列名": {'name': "因子名", 'desc': "因子描述"},
#     ... 
# }
{}
,
    table=''
)

[2022-03-14 08:58:35.460033] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-03-14 08:58:35.538080] INFO: moduleinvoker: input_features.v1 运行完成[0.078661s].

[2022-03-14 08:58:35.548879] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-03-14 08:58:35.639400] INFO: moduleinvoker: instruments.v2 运行完成[0.090517s].

[2022-03-14 08:58:35.658783] INFO: moduleinvoker: factorlens.v2 开始运行..

[2022-03-14 08:58:35.796168] INFO: 因子分析: batch_process start

[2022-03-14 08:58:35.800661] INFO: 因子分析: load_instruments 2019-01-01, 2019-12-31

[2022-03-14 08:58:36.145378] INFO: 因子分析: load_instruments, 3775 rows.

[2022-03-14 08:58:36.146946] INFO: 因子分析: load_benchmark_data 2019-01-01, 2019-12-31

[2022-03-14 08:58:36.199722] INFO: 因子分析: load_benchmark_data, 732 rows.

[2022-03-14 08:58:36.202104] INFO: 因子分析: StockPool.before_load_general_feature_data

[2022-03-14 08:58:36.204179] INFO: 因子分析: UserDataMerge.before_load_general_feature_data

[2022-03-14 08:58:36.206184] INFO: 因子分析: DropSTStocks.before_load_general_feature_data

[2022-03-14 08:58:36.207746] INFO: 因子分析: DropNewStocks.before_load_general_feature_data

[2022-03-14 08:58:36.209812] INFO: 因子分析: Neutralization.before_load_general_feature_data

[2022-03-14 08:58:36.211880] INFO: 因子分析: DelayRebalanceDays.before_load_general_feature_data

[2022-03-14 08:58:36.213580] INFO: 因子分析: RebalancePeriod.before_load_general_feature_data

[2022-03-14 08:58:36.215201] INFO: 因子分析: RebalancePrice.before_load_general_feature_data

[2022-03-14 08:58:36.216653] INFO: 因子分析: FactorCoverage.before_load_general_feature_data

[2022-03-14 08:58:36.218933] INFO: 因子分析: Industry.before_load_general_feature_data

[2022-03-14 08:58:36.220377] INFO: 因子分析: PBRatio.before_load_general_feature_data

[2022-03-14 08:58:36.221497] INFO: 因子分析: Turnover.before_load_general_feature_data

[2022-03-14 08:58:36.222532] INFO: 因子分析: MarketCap.before_load_general_feature_data

[2022-03-14 08:58:36.223683] INFO: 因子分析: load_general_feature_data, load data

[2022-03-14 08:59:05.116263] ERROR: moduleinvoker: module name: factorlens, module version: v2, trackeback: AttributeError: 'dict' object has no attribute 'columns'

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-1-00095a5e09f8> in <module>
     71 )
     72 
---> 73 m2 = M.factorlens.v2(
     74     features=m1.data,
     75     user_factor_data=m4.data,

AttributeError: 'dict' object has no attribute 'columns'