克隆策略

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    In [1]:
    # 本代码由可视化策略环境自动生成 2021年9月9日 17:54
# 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="""((close_0-low_0)-(high_0-close_0))/(high_0-close_0)

"""
)

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=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,
    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'
)

    ---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-1-aa4cb5d22c9a> in <module>
      9 )
     10 
---> 11 m2 = M.factorlens.v1(
     12     features=m1.data,
     13     title='因子分析: {factor_name}',

Exception: 原始因子值覆盖率小于 50.0%，无法进行后续指标计算