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# 本代码由可视化策略环境自动生成 2021年6月21日11:08
# 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。


def my_wbins(self,rets,bins):
    result = pd.cut(rets, bins=bins,labels=range(bins))
    print(result)
    return result

m2_user_functions_bigquant_run = {
    'my_wbins':  my_wbins
}

m1 = M.instruments.v2(
    start_date='2016-01-01',
    end_date='2017-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    label_expr=""" 
# 计算收益：5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
shift(close, -5) / shift(open, -1) - 1

# 极值处理：用1%和99%分位的值做clip
clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))

# 通过自定义函数将分数映射到分类，这里使用20个分类
my_wbins(rets=label, bins=20) 

# 过滤掉一字涨停的情况 (设置label为NaN，在后续处理和训练中会忽略NaN的label)
#where(shift(high, -1) == shift(low, -1), NaN, label)
""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=True,
    user_functions=m2_user_functions_bigquant_run
)

[2021-06-21 11:06:18.290548] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-06-21 11:06:18.296458] INFO: moduleinvoker: 命中缓存

[2021-06-21 11:06:18.297431] INFO: moduleinvoker: instruments.v2 运行完成[0.006886s].

[2021-06-21 11:06:18.300741] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-06-21 11:06:18.319001] INFO: moduleinvoker: 命中缓存

[2021-06-21 11:06:18.319888] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.019149s].