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# 本代码由可视化策略环境自动生成 2022年2月21日 14:20
# 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
mean(close_0, 44) / close_0 - 1"""
)

m4 = M.factorlens.v2(
    features=m1.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=[],
    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=m4.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=''
)