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 2020年11月9日 09:17
# 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。


import pandas as pd
import numpy as np
# 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):
    # 示例代码如下。在这里编写您的代码
    data = input_1.read_df()
    data = data[['date', 'close']]
    close_fft = np.fft.fft(np.asarray(data['close'].tolist()))
    fft_df = pd.DataFrame({'fft':close_fft})
    fft_df['absolute'] = fft_df['fft'].apply(lambda x: np.abs(x))
    fft_df['angle'] = fft_df['fft'].apply(lambda x: np.angle(x))


    fft_list = np.asarray(fft_df['fft'].tolist())
    fft_list[300:-300] = 0
    f1= np.fft.ifft(fft_list)
    return Outputs(data_1=f1, data_2=None, data_3=None)

# 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='2020-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m5 = 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
"""
)

m2 = M.use_datasource.v1(
    instruments=m1.data,
    features=m5.data,
    datasource_id='bar1d_CN_STOCK_A',
    start_date='',
    end_date=''
)

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=''
)

m3 = M.derived_feature_extractor.v3(
    input_data=m2.data,
    features=m4.data_1,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

    ---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-5c7a70cc4c82> in <module>()
     56     input_ports='',
     57     params='{}',
---> 58     output_ports=''
     59 )

TypeError: _cache_value_encoder: not supported type: <class 'numpy.ndarray'>