复制链接
克隆策略

    {"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-35986:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-63533:input_1","from_node_id":"-4500:data"},{"to_node_id":"-54253:input_1","from_node_id":"-556:predictions"},{"to_node_id":"-35993:input_data","from_node_id":"-35986:data"},{"to_node_id":"-36003:input_data","from_node_id":"-35993:data"},{"to_node_id":"-556:data","from_node_id":"-36003:data"},{"to_node_id":"-556:model","from_node_id":"-1290:data_1"},{"to_node_id":"-1290:input_1","from_node_id":"-63533:data_1"},{"to_node_id":"-35986:features","from_node_id":"-63533:data_1"},{"to_node_id":"-36003:features","from_node_id":"-63533:data_1"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2023-11-01","type":"Literal","bound_global_parameter":"交易日期"},{"name":"end_date","value":"2023-12-01","type":"Literal","bound_global_parameter":"交易日期"},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"cacheable":true,"seq_num":9,"comment":"","comment_collapsed":true},{"node_id":"-4500","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"Alpha_1=ts_min(amount_0, 7)\n\nAlpha_2=abs(ts_argmin(rank_market_cap_float_0, 3))\n\n\nAlpha_3=ts_min(amount_0, 7)\n\nAlpha_4=sum(ta_sma((2*close_0-high_0-low_0)/((high_0+low_0+close_0+open_0)/4), 4), 6)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-4500"}],"output_ports":[{"name":"data","node_id":"-4500"}],"cacheable":true,"seq_num":34,"comment":"","comment_collapsed":true},{"node_id":"-556","module_id":"BigQuantSpace.random_forest_predict.random_forest_predict-v2","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"model","node_id":"-556"},{"name":"data","node_id":"-556"}],"output_ports":[{"name":"predictions","node_id":"-556"}],"cacheable":true,"seq_num":40,"comment":"","comment_collapsed":true},{"node_id":"-35986","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"100","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-35986"},{"name":"features","node_id":"-35986"}],"output_ports":[{"name":"data","node_id":"-35986"}],"cacheable":true,"seq_num":22,"comment":"","comment_collapsed":true},{"node_id":"-35993","module_id":"BigQuantSpace.chinaa_stock_filter.chinaa_stock_filter-v1","parameters":[{"name":"index_constituent_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"board_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22displayValue%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"industry_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22displayValue%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22displayValue%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22displayValue%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22displayValue%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22displayValue%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22displayValue%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22s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numpy\n\ndef add(df,x1,x2):\n return np.add(x1,x2)\ndef sub(df,x1,x2):\n return np.subtract(x1,x2)\ndef mul(df,x1,x2):\n return np.multiply(x1,x2) \ndef div(df,x1, x2):\n with np.errstate(divide='ignore', invalid='ignore'):\n return np.where(np.abs(x2) > 0.001, np.divide(x1, x2), 1.)\nbigquant_run = {\n 'add':add,\n 'sub':sub,\n 'mul':mul,\n 'div':div\n \n}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-36003"},{"name":"features","node_id":"-36003"}],"output_ports":[{"name":"data","node_id":"-36003"}],"cacheable":true,"seq_num":30,"comment":"","comment_collapsed":true},{"node_id":"-1290","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):\n # 示例代码如下。在这里编写您的代码\n df = input_1.read_pickle()\n for i in df:\n if '=' in i and '= ' not in i:\n fac = i.split('=')[0] \n data = pd.read_pickle('/home/bigquant/work/userlib/'+fac+'.csv')\n model_ds = DataSource.write_pickle(data.iloc[0].to_dict())\n return Outputs(data_1=model_ds)\n\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 return outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-1290"},{"name":"input_2","node_id":"-1290"},{"name":"input_3","node_id":"-1290"}],"output_ports":[{"name":"data_1","node_id":"-1290"},{"name":"data_2","node_id":"-1290"},{"name":"data_3","node_id":"-1290"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-54253","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):\n # 示例代码如下。在这里编写您的代码\n df = input_1.read_df()\n #df = pd.concat([df1,df2,df3]).reset_index(drop = True)\n \n def choose(x):\n if len(x)>1:\n if x.pred_label.mean()>=0.5:\n x.at[list(x.index),'pred_label']=[1]*len(x)\n else:\n x.at[list(x.index),'pred_label']=[0]*len(x)\n return x\n data = df.groupby(['date','instrument']).apply(choose)\n result = data.drop('pred_prob',axis=1).drop_duplicates()\n data_1 = DataSource.write_df(result)\n\n return Outputs(data_1=data_1)\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 return outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-54253"},{"name":"input_2","node_id":"-54253"},{"name":"input_3","node_id":"-54253"}],"output_ports":[{"name":"data_1","node_id":"-54253"},{"name":"data_2","node_id":"-54253"},{"name":"data_3","node_id":"-54253"}],"cacheable":false,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-63533","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):\n # 示例代码如下。在这里编写您的代码\n df = input_1.read_pickle()\n for i in df:\n data1 = DataSource.write_pickle([i])\n return 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    In [2]:
    # 本代码由可视化策略环境自动生成 2023年12月13日 10:00
# 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 m11_run_bigquant_run(input_1):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read_pickle()
    for i in df:
        data1 = DataSource.write_pickle([i])
        return Outputs(data_1=data1)



# 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 m11_post_run_bigquant_run(outputs):
    return outputs

# 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 m2_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read_pickle()
    for i in df:
        if '=' in i and '= ' not in i:
            fac = i.split('=')[0]  
    data = pd.read_pickle('/home/bigquant/work/userlib/'+fac+'.csv')
    model_ds = DataSource.write_pickle(data.iloc[0].to_dict())
    return Outputs(data_1=model_ds)


# 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 m2_post_run_bigquant_run(outputs):
    return outputs

import numpy

def add(df,x1,x2):
    return np.add(x1,x2)
def sub(df,x1,x2):
    return np.subtract(x1,x2)
def mul(df,x1,x2):
    return np.multiply(x1,x2)   
def div(df,x1, x2):
    with np.errstate(divide='ignore', invalid='ignore'):
        return np.where(np.abs(x2) > 0.001, np.divide(x1, x2), 1.)
m30_user_functions_bigquant_run = {
    'add':add,
    'sub':sub,
    'mul':mul,
    'div':div
    
}
# 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
    df = input_1.read_df()
    #df = pd.concat([df1,df2,df3]).reset_index(drop = True)
    
    def choose(x):
        if len(x)>1:
            if x.pred_label.mean()>=0.5:
                x.at[list(x.index),'pred_label']=[1]*len(x)
            else:
                x.at[list(x.index),'pred_label']=[0]*len(x)
        return x
    data = df.groupby(['date','instrument']).apply(choose)
    result = data.drop('pred_prob',axis=1).drop_duplicates()
    data_1 = DataSource.write_df(result)

    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 m4_post_run_bigquant_run(outputs):
    return outputs


m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2023-11-01'),
    end_date=T.live_run_param('trading_date', '2023-12-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m34 = M.input_features.v1(
    features="""Alpha_1=ts_min(amount_0, 7)

Alpha_2=abs(ts_argmin(rank_market_cap_float_0, 3))


Alpha_3=ts_min(amount_0, 7)

Alpha_4=sum(ta_sma((2*close_0-high_0-low_0)/((high_0+low_0+close_0+open_0)/4), 4), 6)"""
)

m11 = M.cached.v3(
    input_1=m34.data,
    run=m11_run_bigquant_run,
    post_run=m11_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m2 = M.cached.v3(
    input_1=m11.data_1,
    run=m2_run_bigquant_run,
    post_run=m2_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m22 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m11.data_1,
    start_date='',
    end_date='',
    before_start_days=100
)

m28 = M.chinaa_stock_filter.v1(
    input_data=m22.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
)

m30 = M.derived_feature_extractor.v3(
    input_data=m28.data,
    features=m11.data_1,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False,
    user_functions=m30_user_functions_bigquant_run
)

m40 = M.random_forest_predict.v2(
    model=m2.data_1,
    data=m30.data,
    date_col='date',
    instrument_col='instrument',
    sort=True
)

m4 = M.cached.v3(
    input_1=m40.predictions,
    run=m4_run_bigquant_run,
    post_run=m4_post_run_bigquant_run,
    input_ports='',
    params='',
    output_ports='',
    m_cached=False
)