复制链接

克隆策略

In [29]:

# 本代码由可视化策略环境自动生成 2022年9月26日 10:32
# 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 m16_run_bigquant_run(input_1, input_2, input_3, SW_type):
    basic_data = input_1.read()
    start_date = str(basic_data.date.min())
    end_date = str(basic_data.date.max())
    SW_data = DataSource('basic_info_IndustrySw').read()
    SW_data['code'] = SW_data.code.astype('int')

    SW_data_2014_2 = SW_data[(SW_data.version==2014)&(SW_data.industry_sw_level==2)][['code','name']].rename(columns={'code':'industry_sw_level2_0','name':'name_SW2'})

    SW_data_2021_2 = SW_data[(SW_data.version==2021)&(SW_data.industry_sw_level==2)][['code','name']].rename(columns={'code':'industry_sw_level2_0','name':'name_SW2'})

    basic_data['daily_return_0'] = basic_data['daily_return_0']-1

    if end_date < '2021-12-13':

        basic_data = basic_data.merge(SW_data_2014_2,how='left',on='industry_sw_level2_0')

    else:
        basic_data_2014 = basic_data[basic_data.date < '2021-12-13']

        basic_data_2014 = basic_data_2014.merge(SW_data_2014_2,how='left',on='industry_sw_level2_0')

        basic_data_2021 = basic_data[basic_data.date >= '2021-12-13']

        basic_data_2021 = basic_data_2021.merge(SW_data_2021_2,how='left',on='industry_sw_level2_0')

        basic_data = pd.concat([basic_data_2014,basic_data_2021])
        
    block_data = basic_data.groupby(['name_SW2','date']).agg({'daily_return_0':'sum','market_cap_float_0':'sum'}).reset_index()
    block_data.columns = ['name_SW2','date','daily_return_0_block_sum','market_cap_float_0_block_sum']
    rst = pd.merge(basic_data[['date','name_SW2','instrument','daily_return_0','market_cap_float_0']],block_data,on=['date','name_SW2'],how='left').dropna()
    
    rst['板块流通收益率'] = rst['daily_return_0']*rst['market_cap_float_0']/rst['market_cap_float_0_block_sum']
    data_1 = DataSource.write_df(rst)
    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 m16_post_run_bigquant_run(outputs):
    return outputs


m2 = M.instruments.v2(
    start_date='2022-04-15',
    end_date='2022-09-15',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m11 = M.use_datasource.v1(
    instruments=m2.data,
    datasource_id='bar1d_CN_STOCK_A',
    start_date='',
    end_date=''
)

m6 = M.input_features.v1(
    features="""industry_sw_level2_0
market_cap_float_0
daily_return_0
"""
)

m13 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m6.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m16 = M.cached.v3(
    input_1=m13.data,
    run=m16_run_bigquant_run,
    post_run=m16_post_run_bigquant_run,
    input_ports='',
    params="""{
    'SW_type':'name_SW2'
}""",
    output_ports='data_1,data_2'
)

m17 = M.join.v3(
    data1=m16.data_1,
    data2=m11.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m12 = 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

correlation(log(volume_0),return_0,10)

"""
)

m3 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m12.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m5 = M.derived_feature_extractor.v3(
    input_data=m3.data,
    features=m12.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m4 = M.chinaa_stock_filter.v1(
    input_data=m5.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板', '创业板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m18 = M.join.v3(
    data1=m4.left_data,
    data2=m17.data,
    on='date,instrument',
    how='inner',
    sort=False,
    m_cached=False
)

m7 = M.filter.v3(
    input_data=m18.data,
    expr='total>0 & rank(板块流通收益率)>0.95',
    output_left_data=False
)

[2022-09-26 10:31:32.578698] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-09-26 10:31:32.592269] INFO: moduleinvoker: 命中缓存

[2022-09-26 10:31:32.594495] INFO: moduleinvoker: instruments.v2 运行完成[0.015838s].

[2022-09-26 10:31:32.600095] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-09-26 10:31:32.612814] INFO: moduleinvoker: 命中缓存

[2022-09-26 10:31:32.614892] INFO: moduleinvoker: use_datasource.v1 运行完成[0.014811s].

[2022-09-26 10:31:32.620379] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-09-26 10:31:32.635040] INFO: moduleinvoker: 命中缓存

[2022-09-26 10:31:32.636846] INFO: moduleinvoker: input_features.v1 运行完成[0.01648s].

[2022-09-26 10:31:32.661461] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-09-26 10:31:32.682041] INFO: moduleinvoker: 命中缓存

[2022-09-26 10:31:32.684947] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.023502s].

[2022-09-26 10:31:32.701389] INFO: moduleinvoker: cached.v3 开始运行..

[2022-09-26 10:31:32.716553] INFO: moduleinvoker: 命中缓存

[2022-09-26 10:31:32.718477] INFO: moduleinvoker: cached.v3 运行完成[0.01711s].

[2022-09-26 10:31:32.728900] INFO: moduleinvoker: join.v3 开始运行..

[2022-09-26 10:31:32.748392] INFO: moduleinvoker: 命中缓存

[2022-09-26 10:31:32.750336] INFO: moduleinvoker: join.v3 运行完成[0.02143s].

[2022-09-26 10:31:32.754365] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-09-26 10:31:32.768825] INFO: moduleinvoker: 命中缓存

[2022-09-26 10:31:32.772250] INFO: moduleinvoker: input_features.v1 运行完成[0.017864s].

[2022-09-26 10:31:32.791023] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-09-26 10:31:32.799345] INFO: moduleinvoker: 命中缓存

[2022-09-26 10:31:32.801205] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.010206s].

[2022-09-26 10:31:32.808758] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-09-26 10:31:32.819324] INFO: moduleinvoker: 命中缓存

[2022-09-26 10:31:32.821579] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.012817s].

[2022-09-26 10:31:32.833737] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2022-09-26 10:31:38.305072] INFO: A股股票过滤: 过滤 /y_2022, 665995/0/775251

[2022-09-26 10:31:38.307648] INFO: A股股票过滤: 过滤完成, 665995 + 0

[2022-09-26 10:31:38.357340] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[5.523569s].

[2022-09-26 10:31:38.409012] INFO: moduleinvoker: join.v3 开始运行..

[2022-09-26 10:31:38.411177] ERROR: moduleinvoker: module name: join, module version: v3, trackeback: AttributeError: 'NoneType' object has no attribute 'open_df_store'

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-29-bfb022869f35> in <module>
    131 )
    132 
--> 133 m18 = M.join.v3(
    134     data1=m4.left_data,
    135     data2=m17.data,

AttributeError: 'NoneType' object has no attribute 'open_df_store'