在输入特征列表模块，由于策略开发需要，我们可能会构建很多复杂的特征，这些特征如果能够自定义名称，那么再后续的分析中就非常方便，因此给出一个示例。
本例中，我们构建5日收盘价移动平均值因子和按行业中性化的市值因子，将其重命名：
ma_5 # 5日收盘价移动平均值因子
neutral_market_cap #按行业中性化后的市值因子
我们看看抽取出来的特征时怎样的：
原来，ma_5和neutral_market_cap这两个自定义的特征已经构建好了。具体的策略源码在下方，大家可以克隆研究哦~
克隆策略
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In [2]:
# 本代码由可视化策略环境自动生成 2018年11月1日 22:30
# 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
# 多个特征，每行一个，可以包含基础特征和衍生特征
return_5
ma_5 = mean(close_0,5)
neutral_market_cap=group_mean(industry_sw_level1_0, market_cap_float_0)"""
)
m3 = M.instruments.v2(
start_date='2018-01-01',
end_date='2018-10-10',
market='CN_STOCK_A',
instrument_list='',
max_count=3
)
m1 = M.general_feature_extractor.v7(
instruments=m3.data,
features=m2.data,
start_date='',
end_date='',
before_start_days=90
)
m4 = M.derived_feature_extractor.v3(
input_data=m1.data,
features=m2.data,
date_col='date',
instrument_col='instrument',
user_functions={}
)
In [4]:
m4.data.read_df().tail()
Out[4]: