克隆策略

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    In [52]:
    # 本代码由可视化策略环境自动生成 2019年3月13日 17:16
# 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 m2_run_bigquant_run(input_1):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read()
    df.set_index('date', inplace=True)
    g1 = df['label'].groupby(pd.Grouper(freq='M')).apply(lambda x: x.sum() / len(x))
    g2 = df['label'][df['cond']].groupby(pd.Grouper(freq='M')).apply(lambda x: x.sum() / len(x))
    ds = DataSource.write_df(pd.DataFrame({'全市场上涨比例': g1, '连续上涨三天后上涨比例': g2}))
    return Outputs(data_1=ds)

# 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 m2_post_run_bigquant_run(outputs):
    return outputs


m1 = M.instruments.v2(
    start_date='2016-01-01',
    end_date='2019-03-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = 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
cond = (close_0 / close_1 > 1.01) & (close_1 / close_2 > 1.01) & (close_2 / close_3 > 1.01)
label = where(shift(return_0, -1) > 1.01, 1, 0)
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m2 = M.cached.v3(
    input_1=m16.data,
    run=m2_run_bigquant_run,
    post_run=m2_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m4 = M.plot_dataframe.v1(
    input_data=m2.data_1,
    title='',
    chart_type='column',
    x='',
    y='',
    options={
    'chart': {
        'height': 400
    }
},
    candlestick=False,
    pane_1='',
    pane_2='',
    pane_3='',
    pane_4=''
)