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# 本代码由可视化策略环境自动生成 2019年1月31日 17:56
# 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 m21_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    input_df = input_1.read_df().reset_index(drop='True')
    import talib

    def cal_kama(df):
        # 取出close_0列的数据转化为float
        close = [float(x) for x in df['close_0']]
         # 调用talib计算MACD指标
        df['KAMA'] = talib.KAMA(np.array(close), timeperiod=5)
        return df[['date','instrument','KAMA']]    
    
    result = input_df.groupby('instrument').apply(cal_kama)
    
    data_1 = DataSource.write_df(result)
    return Outputs(data_1=data_1, 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 m21_post_run_bigquant_run(outputs):
    return outputs


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2015-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m20 = 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
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m20.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m21 = M.cached.v3(
    input_1=m15.data,
    run=m21_run_bigquant_run,
    post_run=m21_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m20.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m22 = M.join.v3(
    data1=m21.data_1,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m13 = M.dropnan.v1(
    input_data=m22.data
)

[2019-01-31 17:56:51.193812] INFO: bigquant: instruments.v2 开始运行..
[2019-01-31 17:56:51.201006] INFO: bigquant: 命中缓存
[2019-01-31 17:56:51.202113] INFO: bigquant: instruments.v2 运行完成[0.008329s].
[2019-01-31 17:56:51.204912] INFO: bigquant: input_features.v1 开始运行..
[2019-01-31 17:56:51.209483] INFO: bigquant: 命中缓存
[2019-01-31 17:56:51.210381] INFO: bigquant: input_features.v1 运行完成[0.005477s].
[2019-01-31 17:56:51.215595] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-01-31 17:56:51.219919] INFO: bigquant: 命中缓存
[2019-01-31 17:56:51.220930] INFO: bigquant: general_feature_extractor.v7 运行完成[0.005337s].
[2019-01-31 17:56:51.224645] INFO: bigquant: cached.v3 开始运行..
[2019-01-31 17:56:59.480688] INFO: bigquant: cached.v3 运行完成[8.255999s].
[2019-01-31 17:56:59.488493] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-01-31 17:56:59.494433] INFO: bigquant: 命中缓存
[2019-01-31 17:56:59.495480] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.006994s].
[2019-01-31 17:56:59.500676] INFO: bigquant: join.v3 开始运行..
[2019-01-31 17:57:02.010445] INFO: join: /y_2010, 行数=431567/431567, 耗时=1.276989s
[2019-01-31 17:57:03.146173] INFO: join: /y_2011, 行数=511455/511455, 耗时=1.120019s
[2019-01-31 17:57:04.439341] INFO: join: /y_2012, 行数=565675/565675, 耗时=1.276905s
[2019-01-31 17:57:06.005996] INFO: join: /y_2013, 行数=564168/564168, 耗时=1.532349s
[2019-01-31 17:57:07.347811] INFO: join: /y_2014, 行数=569948/569948, 耗时=1.320967s
[2019-01-31 17:57:07.466793] INFO: join: 最终行数: 2642813
[2019-01-31 17:57:07.470146] INFO: bigquant: join.v3 运行完成[7.969442s].
[2019-01-31 17:57:07.476212] INFO: bigquant: dropnan.v1 开始运行..
[2019-01-31 17:57:08.091627] INFO: dropnan: /y_2010, 421499/431567
[2019-01-31 17:57:08.589460] INFO: dropnan: /y_2011, 509994/511455
[2019-01-31 17:57:09.075821] INFO: dropnan: /y_2012, 564864/565675
[2019-01-31 17:57:09.550413] INFO: dropnan: /y_2013, 564108/564168
[2019-01-31 17:57:10.068404] INFO: dropnan: /y_2014, 569351/569948
[2019-01-31 17:57:10.097166] INFO: dropnan: 行数: 2629816/2642813
[2019-01-31 17:57:10.119084] INFO: bigquant: dropnan.v1 运行完成[2.642811s].