克隆策略

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    In [49]:
    # 本代码由可视化策略环境自动生成 2020年7月30日 10:56
# 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 m5_run_bigquant_run(input_1, input_2, input_3):
    
    # 先获取股票的日线数据
    daily_data = input_1.read()[["close", "open", "high", "low", "date", "instrument"]]
#     daily_data = DataSource("bar1d_CN_STOCK_A").read("000001.SZA", start_date="2018-01-01", end_date="2019-01-01", fields=["close", "open", "high", "low", "date", "instrument"])

    # 获取交易日周索引
    index_ = daily_data.resample("W", how="first", on="date").set_index("date").index

    # 进行resample，注意高开低收的各个resample规则不同，需要分开处理
    daily_data.set_index("date", inplace=True)
    daily_data_close = daily_data.resample("W", label="left", how="last")["close"]
    daily_data_open = daily_data.resample("W", label="left", how="first")["open"]
    daily_data_high = daily_data.resample("W", label="left", how="max")["high"]
    daily_data_low = daily_data.resample("W", label="left", how="min")['low']
    daily_data_instrument = daily_data.resample("W", label="left", how="first")['instrument']

    week_data = pd.DataFrame({"instrument": daily_data_instrument.values, "close": daily_data_close.values, "open": daily_data_open.values, "high": daily_data_high.values, "low":daily_data_low.values, "date": index_}, index=index_)

    # 删除nan
    week_data.dropna(how="any", inplace=True, axis=0)

    data_1 = DataSource.write_df(week_data)
    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 m5_post_run_bigquant_run(outputs):
    return outputs


m2 = 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
ta_bbands_l(close, 2)"""
)

m3 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2019-01-01',
    market='CN_STOCK_A',
    instrument_list='000001.SZA',
    max_count=0
)

m1 = M.use_datasource.v1(
    instruments=m3.data,
    datasource_id='bar1d_CN_STOCK_A',
    start_date='2018-01-01',
    end_date='2019-01-01'
)

m5 = M.cached.v3(
    input_1=m1.data,
    run=m5_run_bigquant_run,
    post_run=m5_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m4 = M.derived_feature_extractor.v3(
    input_data=m5.data_1,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=True,
    user_functions={}
)