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    {"description":"实验创建于2022/8/7","graph":{"edges":[{"to_node_id":"-85:features","from_node_id":"-118:data"}],"nodes":[{"node_id":"-85","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":"2021-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-03-31","type":"Literal","bound_global_parameter":null},{"name":"rebalance_period","value":"5","type":"Literal","bound_global_parameter":null},{"name":"delay_rebalance_days","value":"0","type":"Literal","bound_global_parameter":null},{"name":"rebalance_price","value":"vwap","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":"沪深300","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%3Afalse%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%3Afalse%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%3Afalse%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":"inner","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features","node_id":"-85"},{"name":"user_factor_data","node_id":"-85"}],"output_ports":[{"name":"data","node_id":"-85"},{"name":"save_data","node_id":"-85"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-118","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# rank_avg_amount_0\n# rank_avg_amount_5\n# rank_avg_amount_10\n# rank_return_0\n# rank_return_5\n# rank_return_10\n# rank_avg_turn_0\n# rank_avg_turn_5\n# rank_avg_turn_10\n# mf_net_pct_l_0\n# mf_net_pct_m_0\n# mf_net_pct_main_0\n# mf_net_pct_s_0\n# mf_net_pct_xl_0\n# rank_volatility_5_0\n# rank_volatility_10_0\n# pe_ttm_0\n# mean(close_0, 5)/close_0\n# mean(close_0, 10)/close_0\n# mean(close_0, 20)/close_0\n# mean(close_0, 30)/close_0\n# mean(close_0, 60)/close_0\ncorrelation(turn_0, return_0, 5)\ncorrelation(turn_0, return_0, 10)\ncorrelation(turn_0, return_0, 15)\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-118"}],"output_ports":[{"name":"data","node_id":"-118"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-85' Position='20,141,200,200'/><node_position Node='-118' Position='-155,-99,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}
    In [2]:
    # 本代码由可视化策略环境自动生成 2022年8月10日 19:43
# 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。


m2 = 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
# rank_avg_amount_0
# rank_avg_amount_5
# rank_avg_amount_10
# rank_return_0
# rank_return_5
# rank_return_10
# rank_avg_turn_0
# rank_avg_turn_5
# rank_avg_turn_10
# mf_net_pct_l_0
# mf_net_pct_m_0
# mf_net_pct_main_0
# mf_net_pct_s_0
# mf_net_pct_xl_0
# rank_volatility_5_0
# rank_volatility_10_0
# pe_ttm_0
# mean(close_0, 5)/close_0
# mean(close_0, 10)/close_0
# mean(close_0, 20)/close_0
# mean(close_0, 30)/close_0
# mean(close_0, 60)/close_0
correlation(turn_0, return_0, 5)
correlation(turn_0, return_0, 10)
correlation(turn_0, return_0, 15)
"""
)

m1 = M.factorlens.v2(
    features=m2.data,
    title='因子分析: {factor_name}',
    start_date='2021-01-01',
    end_date='2021-03-31',
    rebalance_period=5,
    delay_rebalance_days=0,
    rebalance_price='vwap',
    stock_pool='全市场',
    quantile_count=5,
    commission_rate=0.0016,
    returns_calculation_method='累乘',
    benchmark='沪深300',
    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', 'IC分析', '买入信号重合分析', '因子估值分析', '因子拥挤度分析', '表达式因子值', '多因子相关性分析'],
    factor_coverage=0.5,
    user_data_merge='inner'
)

    ---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-2-8567cc69fe71> in <module>
     35 )
     36 
---> 37 m1 = M.factorlens.v2(
     38     features=m2.data,
     39     title='因子分析: {factor_name}',

KeyError: "['ADJUSTED_BENCHMARK_RETURN_0'] not in index"