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    {"description":"实验创建于2021/12/21","graph":{"edges":[{"to_node_id":"-419:features","from_node_id":"-406:data"},{"to_node_id":"-419:instruments","from_node_id":"-410:data"},{"to_node_id":"-173:input_1","from_node_id":"-419:data"}],"nodes":[{"node_id":"-173","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# 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\ndef bigquant_run(input_1, input_2, input_3, SW_type, SW_name):\n basic_data = input_1.read()\n start_date = str(basic_data.date.min())\n end_date = str(basic_data.date.max())\n SW_data = DataSource('basic_info_IndustrySw').read()\n SW_data['code'] = SW_data.code.astype('int')\n SW_data_2014_1 = SW_data[(SW_data.version==2014)&(SW_data.industry_sw_level==1)][['code','name']].rename(columns={'code':'industry_sw_level1_0','name':'name_SW1'})\n 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'})\n SW_data_2014_3 = SW_data[(SW_data.version==2014)&(SW_data.industry_sw_level==3)][['code','name']].rename(columns={'code':'industry_sw_level3_0','name':'name_SW3'})\n SW_data_2021_1 = SW_data[(SW_data.version==2021)&(SW_data.industry_sw_level==1)][['code','name']].rename(columns={'code':'industry_sw_level1_0','name':'name_SW1'})\n 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'})\n SW_data_2021_3 = SW_data[(SW_data.version==2021)&(SW_data.industry_sw_level==3)][['code','name']].rename(columns={'code':'industry_sw_level3_0','name':'name_SW3'})\n # 提取所需数据\n# basic_data = DataSource('features_CN_STOCK_A_G300').read(start_date=start_date,end_date=end_date,fields=['industry_sw_level1_0','industry_sw_level2_0','industry_sw_level3_0','market_cap_float_0','daily_return_0'])\n# basic_data = basic_data[basic_data.industry_sw_level1_0 !=0]\n basic_data['daily_return_0'] = basic_data['daily_return_0']-1\n\n if end_date < '2021-12-13':\n basic_data = basic_data.merge(SW_data_2014_1,how='left',on='industry_sw_level1_0')\n basic_data = basic_data.merge(SW_data_2014_2,how='left',on='industry_sw_level2_0')\n basic_data = basic_data.merge(SW_data_2014_3,how='left',on='industry_sw_level3_0')\n else:\n basic_data_2014 = basic_data[basic_data.date < '2021-12-13']\n basic_data_2014 = basic_data_2014.merge(SW_data_2014_1,how='left',on='industry_sw_level1_0')\n basic_data_2014 = basic_data_2014.merge(SW_data_2014_2,how='left',on='industry_sw_level2_0')\n basic_data_2014 = basic_data_2014.merge(SW_data_2014_3,how='left',on='industry_sw_level3_0')\n basic_data_2021 = basic_data[basic_data.date >= '2021-12-13']\n basic_data_2021 = basic_data_2021.merge(SW_data_2021_1,how='left',on='industry_sw_level1_0')\n basic_data_2021 = basic_data_2021.merge(SW_data_2021_2,how='left',on='industry_sw_level2_0')\n basic_data_2021 = basic_data_2021.merge(SW_data_2021_3,how='left',on='industry_sw_level3_0')\n basic_data = pd.concat([basic_data_2014,basic_data_2021])\n \n def calculate_daily_ret(df):\n df['ret'] = df['daily_return_0'] * (df['market_cap_float_0']/df['market_cap_float_0'].sum())\n return df\n \n SW_type = SW_type\n SW_name = SW_name\n \n SW_daily_ret = basic_data.groupby([SW_type,'date']).apply(calculate_daily_ret)\n \n SW_daily_ret = SW_daily_ret[SW_daily_ret[SW_type]==SW_name]\n\n SW_daily_ret = SW_daily_ret[['date','ret']].groupby('date').sum()\n \n #第一天收益为0\n SW_daily_ret['ret'][0:1]=0\n \n SW_daily_ret['cum_ret'] = (SW_daily_ret['ret']+1).cumprod()-1\n \n SW_index_dict = {'SW_type':SW_type,'SW_name':SW_name}\n \n data_1 = DataSource.write_df(SW_daily_ret)\n data_2 = DataSource.write_pickle(SW_index_dict)\n return Outputs(data_1=data_1, data_2=data_2, data_3=None)\n","type":"Literal","bound_global_parameter":null},{"name":"post_run","value":"# 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。\ndef bigquant_run(outputs):\n daily_ret = outputs.data_1.read()\n index_info = outputs.data_2.read()\n# daily_ret.set_index(\"date\", inplace=True)\n # 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    In [8]:
    # 本代码由可视化策略环境自动生成 2021年12月22日 11:16
# 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 m1_run_bigquant_run(input_1, input_2, input_3, SW_type, SW_name):
    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_1 = SW_data[(SW_data.version==2014)&(SW_data.industry_sw_level==1)][['code','name']].rename(columns={'code':'industry_sw_level1_0','name':'name_SW1'})
    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_2014_3 = SW_data[(SW_data.version==2014)&(SW_data.industry_sw_level==3)][['code','name']].rename(columns={'code':'industry_sw_level3_0','name':'name_SW3'})
    SW_data_2021_1 = SW_data[(SW_data.version==2021)&(SW_data.industry_sw_level==1)][['code','name']].rename(columns={'code':'industry_sw_level1_0','name':'name_SW1'})
    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'})
    SW_data_2021_3 = SW_data[(SW_data.version==2021)&(SW_data.industry_sw_level==3)][['code','name']].rename(columns={'code':'industry_sw_level3_0','name':'name_SW3'})
    # 提取所需数据
#     basic_data = DataSource('features_CN_STOCK_A_G300').read(start_date=start_date,end_date=end_date,fields=['industry_sw_level1_0','industry_sw_level2_0','industry_sw_level3_0','market_cap_float_0','daily_return_0'])
#     basic_data = basic_data[basic_data.industry_sw_level1_0 !=0]
    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_1,how='left',on='industry_sw_level1_0')
        basic_data = basic_data.merge(SW_data_2014_2,how='left',on='industry_sw_level2_0')
        basic_data = basic_data.merge(SW_data_2014_3,how='left',on='industry_sw_level3_0')
    else:
        basic_data_2014 = basic_data[basic_data.date < '2021-12-13']
        basic_data_2014 = basic_data_2014.merge(SW_data_2014_1,how='left',on='industry_sw_level1_0')
        basic_data_2014 = basic_data_2014.merge(SW_data_2014_2,how='left',on='industry_sw_level2_0')
        basic_data_2014 = basic_data_2014.merge(SW_data_2014_3,how='left',on='industry_sw_level3_0')
        basic_data_2021 = basic_data[basic_data.date >= '2021-12-13']
        basic_data_2021 = basic_data_2021.merge(SW_data_2021_1,how='left',on='industry_sw_level1_0')
        basic_data_2021 = basic_data_2021.merge(SW_data_2021_2,how='left',on='industry_sw_level2_0')
        basic_data_2021 = basic_data_2021.merge(SW_data_2021_3,how='left',on='industry_sw_level3_0')
        basic_data = pd.concat([basic_data_2014,basic_data_2021])
    
    def calculate_daily_ret(df):
        df['ret'] = df['daily_return_0'] * (df['market_cap_float_0']/df['market_cap_float_0'].sum())
        return df
    
    SW_type = SW_type
    SW_name = SW_name
    
    SW_daily_ret = basic_data.groupby([SW_type,'date']).apply(calculate_daily_ret)
    
    SW_daily_ret = SW_daily_ret[SW_daily_ret[SW_type]==SW_name]

    SW_daily_ret = SW_daily_ret[['date','ret']].groupby('date').sum()
    
    #第一天收益为0
    SW_daily_ret['ret'][0:1]=0
    
    SW_daily_ret['cum_ret'] = (SW_daily_ret['ret']+1).cumprod()-1
    
    SW_index_dict = {'SW_type':SW_type,'SW_name':SW_name}
    
    data_1 = DataSource.write_df(SW_daily_ret)
    data_2 = DataSource.write_pickle(SW_index_dict)
    return Outputs(data_1=data_1, data_2=data_2, 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 m1_post_run_bigquant_run(outputs):
    daily_ret = outputs.data_1.read()
    index_info = outputs.data_2.read()
#     daily_ret.set_index("date", inplace=True)
    # 画图
    options={"series": [{"color": "#FF0000"}]}
    T.plot(df=daily_ret[["cum_ret"]], title=index_info['SW_name']+"行业指数", options=options, double_precision=6)
    
    return outputs


m2 = M.input_features.v1(
    features="""industry_sw_level1_0
industry_sw_level2_0
industry_sw_level3_0
market_cap_float_0
daily_return_0
"""
)

m3 = M.instruments.v2(
    start_date='2021-01-01',
    end_date='2021-12-20',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m4 = M.general_feature_extractor.v7(
    instruments=m3.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m1 = M.cached.v3(
    input_1=m4.data,
    run=m1_run_bigquant_run,
    post_run=m1_post_run_bigquant_run,
    input_ports='',
    params="""{
    'SW_type':'name_SW1','SW_name':'基础化工'
}""",
    output_ports='data_1,data_2'
)