克隆策略

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    In [4]:
    # 本代码由可视化策略环境自动生成 2021年9月18日 16:10
# 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="""coefficient(mean(close_0,6),close_0,6)
"""
)

m2 = M.factorlens.v1(
    features=m1.data,
    title='因子分析: {factor_name}',
    start_date='2019-01-01',
    end_date='2021-09-07',
    rebalance_period=10,
    delay_rebalance_days=0,
    rebalance_price='close_0',
    stock_pool='全市场',
    quantile_count=10,
    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,
    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', '多因子相关性分析'],
    factor_coverage=0.5,
    user_data_merge='left'
)

    ---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-4-9c9e838d7262> in <module>
      7 )
      8 
----> 9 m2 = M.factorlens.v1(
     10     features=m1.data,
     11     title='因子分析: {factor_name}',

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