克隆策略

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    In [34]:
    # 本代码由可视化策略环境自动生成 2021年6月22日14:32
# 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。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m4_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read().reset_index(drop=True)
    data_1 = DataSource.write_df(df)
    return Outputs(data_1=data_1)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m4_post_run_bigquant_run(outputs):
    return outputs


m1 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2018-09-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m6 = 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
close_0
open_0
high_0
low_0
industry_sw_level1_0"""
)

m2 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m6.data,
    start_date='',
    end_date='',
    before_start_days=30
)

m4 = M.cached.v3(
    input_1=m2.data,
    run=m4_run_bigquant_run,
    post_run=m4_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m15 = M.auto_labeler_on_datasource.v1(
    input_data=m4.data_1,
    label_expr="""# #号开始的表示注释
# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段
# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_

# 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)
group_rank(industry_sw_level1_0,shift(close_0,-5)/shift(open_0,-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))

# 将分数映射到分类，这里使用20个分类
all_wbins(label, 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_0, -1) == shift(low_0, -1), NaN, label)
""",
    drop_na_label=True,
    cast_label_int=True,
    date_col='date',
    instrument_col='instrument',
    user_functions={}
)
    In [35]:
    m15.plot_label_counts()
    In [ ]: