复制链接
克隆策略

    {"description":"实验创建于2017/11/15","graph":{"edges":[{"to_node_id":"-281:options_data","from_node_id":"-214:data_1"},{"to_node_id":"-403:inputs","from_node_id":"-210:data"},{"to_node_id":"-293:inputs","from_node_id":"-210:data"},{"to_node_id":"-14834:inputs","from_node_id":"-218:data"},{"to_node_id":"-692:input_data","from_node_id":"-316:data"},{"to_node_id":"-332:trained_model","from_node_id":"-320:data"},{"to_node_id":"-214:input_1","from_node_id":"-332:data"},{"to_node_id":"-692:features","from_node_id":"-2295:data"},{"to_node_id":"-333:features","from_node_id":"-2295:data"},{"to_node_id":"-341:features","from_node_id":"-2295:data"},{"to_node_id":"-300:features","from_node_id":"-2295:data"},{"to_node_id":"-307:features","from_node_id":"-2295:data"},{"to_node_id":"-316:features","from_node_id":"-2295:data"},{"to_node_id":"-438:input_2","from_node_id":"-2295:data"},{"to_node_id":"-443:input_2","from_node_id":"-2295:data"},{"to_node_id":"-293:outputs","from_node_id":"-259:data"},{"to_node_id":"-14841:inputs","from_node_id":"-14806:data"},{"to_node_id":"-14806:inputs","from_node_id":"-14834:data"},{"to_node_id":"-259:inputs","from_node_id":"-14841:data"},{"to_node_id":"-408:inputs","from_node_id":"-403:data"},{"to_node_id":"-446:inputs","from_node_id":"-408:data"},{"to_node_id":"-218:inputs","from_node_id":"-446:data"},{"to_node_id":"-425:input_data","from_node_id":"-2290:data"},{"to_node_id":"-289:instruments","from_node_id":"-620:data"},{"to_node_id":"-300:instruments","from_node_id":"-620:data"},{"to_node_id":"-429:input_data","from_node_id":"-692:data"},{"to_node_id":"-436:input_2","from_node_id":"-333:data"},{"to_node_id":"-332:input_data","from_node_id":"-341:data"},{"to_node_id":"-214:input_2","from_node_id":"-341:data"},{"to_node_id":"-2290:data1","from_node_id":"-289:data"},{"to_node_id":"-307:input_data","from_node_id":"-300:data"},{"to_node_id":"-2290:data2","from_node_id":"-307:data"},{"to_node_id":"-316:instruments","from_node_id":"-322:data"},{"to_node_id":"-281:instruments","from_node_id":"-322:data"},{"to_node_id":"-320:input_model","from_node_id":"-293:data"},{"to_node_id":"-438:input_1","from_node_id":"-425:data"},{"to_node_id":"-443:input_1","from_node_id":"-429:data"},{"to_node_id":"-320:training_data","from_node_id":"-436:data_1"},{"to_node_id":"-320:validation_data","from_node_id":"-436:data_2"},{"to_node_id":"-333:input_data","from_node_id":"-438:data"},{"to_node_id":"-214:input_3","from_node_id":"-443:data"},{"to_node_id":"-341:input_data","from_node_id":"-443:data"}],"nodes":[{"node_id":"-214","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# 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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":"-214"},{"name":"input_2","node_id":"-214"},{"name":"input_3","node_id":"-214"}],"output_ports":[{"name":"data_1","node_id":"-214"},{"name":"data_2","node_id":"-214"},{"name":"data_3","node_id":"-214"}],"cacheable":true,"seq_num":2,"comment":"模型预测结果输出","comment_collapsed":false},{"node_id":"-210","module_id":"BigQuantSpace.dl_layer_input.dl_layer_input-v1","parameters":[{"name":"shape","value":"50,5","type":"Literal","bound_global_parameter":null},{"name":"batch_shape","value":"","type":"Literal","bound_global_parameter":null},{"name":"dtype","value":"float32","type":"Literal","bound_global_parameter":null},{"name":"sparse","value":"False","type":"Literal","bound_global_parameter":null},{"name":"name","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"inputs","node_id":"-210"}],"output_ports":[{"name":"data","node_id":"-210"}],"cacheable":false,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-218","module_id":"BigQuantSpace.dl_layer_lstm.dl_layer_lstm-v1","parameters":[{"name":"units","value":"32","type":"Literal","bound_global_parameter":null},{"name":"activation","value":"tanh","type":"Literal","bound_global_parameter":null},{"name":"user_activation","value":"","type":"Literal","bound_global_parameter":null},{"name":"recurrent_activation","value":"hard_sigmoid","type":"Literal","bound_global_parameter":null},{"name":"user_recurrent_activation","value":"","type":"Literal","bound_global_parameter":null},{"name":"use_bias","value":"True","type":"Literal","bound_global_parameter":null},{"name":"kernel_initializer","value":"glorot_uniform","type":"Literal","bound_global_parameter":null},{"name":"user_kernel_initializer","value":"","type":"Literal","bound_global_parameter":null},{"name":"recurrent_initializer","value":"Orthogonal","type":"Literal","bound_global_parameter":null},{"name":"user_recurrent_initializer","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_initializer","value":"Ones","type":"Literal","bound_global_parameter":null},{"name":"user_bias_initializer","value":"","type":"Literal","bound_global_parameter":null},{"name":"unit_forget_bias","value":"True","type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_kernel_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"recurrent_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"recurrent_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"recurrent_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_recurrent_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"bias_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"bias_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_bias_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"activity_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"activity_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"activity_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_activity_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"kernel_constraint","value":"None","type":"Literal","bound_global_parameter":null},{"name":"user_kernel_constraint","value":"","type":"Literal","bound_global_parameter":null},{"name":"recurrent_constraint","value":"None","type":"Literal","bound_global_parameter":null},{"name":"user_recurrent_constraint","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_constraint","value":"None","type":"Literal","bound_global_parameter":null},{"name":"user_bias_constraint","value":"","type":"Literal","bound_global_parameter":null},{"name":"dropout","value":"0","type":"Literal","bound_global_parameter":null},{"name":"recurrent_dropout","value":0,"type":"Literal","bound_global_parameter":null},{"name":"return_sequences","value":"False","type":"Literal","bound_global_parameter":null},{"name":"implementation","value":"2","type":"Literal","bound_global_parameter":null},{"name":"name","value":"","type":"Literal","bound_global_parameter":null}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回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.prediction = context.options['data'].read_df()\n\n # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n try:\n prediction = context.prediction[data.current_dt.strftime('%Y-%m-%d')]\n except KeyError as e:\n return\n \n instrument = context.instruments[0]\n sid = context.symbol(instrument)\n cur_position = context.portfolio.positions[sid].amount\n \n # 交易逻辑\n if prediction > 0.5 and cur_position == 0:\n context.order_target_percent(context.symbol(instrument), 1)\n print(data.current_dt, '买入!')\n \n elif prediction < 0.5 and cur_position > 0:\n context.order_target_percent(context.symbol(instrument), 0)\n print(data.current_dt, '卖出!')\n 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#号开始的表示注释\n# 0. 每行一个,顺序执行,从第二个开始,可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html\n# 添加benchmark_前缀,可使用对应的benchmark数据\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_\n\n# 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)\nwhere(shift(close, -10) / close -1>0,1,0)\n\n# 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)\nwhere(shift(high, -1) == shift(low, -1), NaN, label)\n","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.SHA","type":"Literal","bound_global_parameter":null},{"name":"drop_na_label","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cast_label_int","value":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-289"}],"output_ports":[{"name":"data","node_id":"-289"}],"cacheable":true,"seq_num":21,"comment":"","comment_collapsed":true},{"node_id":"-300","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":90,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-300"},{"name":"features","node_id":"-300"}],"output_ports":[{"name":"data","node_id":"-300"}],"cacheable":true,"seq_num":22,"comment":"","comment_collapsed":true},{"node_id":"-307","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-307"},{"name":"features","node_id":"-307"}],"output_ports":[{"name":"data","node_id":"-307"}],"cacheable":true,"seq_num":23,"comment":"","comment_collapsed":true},{"node_id":"-322","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2015-02-11","type":"Literal","bound_global_parameter":"交易日期"},{"name":"end_date","value":"2019-09-01","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":"600009.SHA","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-322"}],"output_ports":[{"name":"data","node_id":"-322"}],"cacheable":true,"seq_num":28,"comment":"","comment_collapsed":true},{"node_id":"-293","module_id":"BigQuantSpace.dl_model_init.dl_model_init-v1","parameters":[],"input_ports":[{"name":"inputs","node_id":"-293"},{"name":"outputs","node_id":"-293"}],"output_ports":[{"name":"data","node_id":"-293"}],"cacheable":false,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-425","module_id":"BigQuantSpace.dropnan.dropnan-v2","parameters":[],"input_ports":[{"name":"input_data","node_id":"-425"},{"name":"features","node_id":"-425"}],"output_ports":[{"name":"data","node_id":"-425"}],"cacheable":true,"seq_num":19,"comment":"去掉为nan的数据","comment_collapsed":true},{"node_id":"-429","module_id":"BigQuantSpace.dropnan.dropnan-v2","parameters":[],"input_ports":[{"name":"input_data","node_id":"-429"},{"name":"features","node_id":"-429"}],"output_ports":[{"name":"data","node_id":"-429"}],"cacheable":true,"seq_num":29,"comment":"","comment_collapsed":true},{"node_id":"-436","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 from sklearn.model_selection import train_test_split\n data = input_2.read()\n x_train, x_val, y_train, y_val = train_test_split(data[\"x\"], data['y'])\n data_1 = DataSource.write_pickle({'x': x_train, 'y': y_train})\n data_2 = DataSource.write_pickle({'x': x_val, 'y': y_val})\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 return 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    In [1]:
    # 本代码由可视化策略环境自动生成 2022年12月15日 15:41
    # 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 m30_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        from sklearn.model_selection import train_test_split
        data = input_2.read()
        x_train, x_val, y_train, y_val = train_test_split(data["x"], data['y'])
        data_1 = DataSource.write_pickle({'x': x_train, 'y': y_train})
        data_2 = DataSource.write_pickle({'x': x_val, 'y': y_val})
        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 m30_post_run_bigquant_run(outputs):
        return outputs
    
    # 用户的自定义层需要写到字典中,比如
    # {
    #   "MyLayer": MyLayer
    # }
    m6_custom_objects_bigquant_run = {
        
    }
    
    # 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):
    
        test_data = input_2.read_pickle()
        pred_label = input_1.read_pickle()
        pred_result = pred_label.reshape(pred_label.shape[0]) 
        dt = input_3.read_df()['date'][-1*len(pred_result):]
        pred_df = pd.Series(pred_result, index=dt)
        ds = DataSource.write_df(pred_df)
        
        return Outputs(data_1=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
    
    # 回测引擎:初始化函数,只执行一次
    def m1_initialize_bigquant_run(context):
        # 加载预测数据
        context.prediction = context.options['data'].read_df()
    
        # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
        context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
    # 回测引擎:每日数据处理函数,每天执行一次
    def m1_handle_data_bigquant_run(context, data):
        # 按日期过滤得到今日的预测数据
        try:
            prediction = context.prediction[data.current_dt.strftime('%Y-%m-%d')]
        except KeyError as e:
            return
        
        instrument = context.instruments[0]
        sid = context.symbol(instrument)
        cur_position = context.portfolio.positions[sid].amount
        
        # 交易逻辑
        if prediction > 0.5 and cur_position == 0:
            context.order_target_percent(context.symbol(instrument), 1)
            print(data.current_dt, '买入!')
            
        elif prediction < 0.5 and cur_position > 0:
            context.order_target_percent(context.symbol(instrument), 0)
            print(data.current_dt, '卖出!')
        
    # 回测引擎:准备数据,只执行一次
    def m1_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
    def m1_before_trading_start_bigquant_run(context, data):
        pass
    
    
    m3 = M.dl_layer_input.v1(
        shape='50,5',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m13 = M.dl_layer_reshape.v1(
        inputs=m3.data,
        target_shape='50,5,1',
        name=''
    )
    
    m14 = M.dl_layer_conv2d.v1(
        inputs=m13.data,
        filters=32,
        kernel_size='3,5',
        strides='1,1',
        padding='valid',
        data_format='channels_last',
        dilation_rate='1,1',
        activation='relu',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m15 = M.dl_layer_reshape.v1(
        inputs=m14.data,
        target_shape='48,32',
        name=''
    )
    
    m4 = M.dl_layer_lstm.v1(
        inputs=m15.data,
        units=32,
        activation='tanh',
        recurrent_activation='hard_sigmoid',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        recurrent_initializer='Orthogonal',
        bias_initializer='Ones',
        unit_forget_bias=True,
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        recurrent_regularizer='None',
        recurrent_regularizer_l1=0,
        recurrent_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        recurrent_constraint='None',
        bias_constraint='None',
        dropout=0,
        recurrent_dropout=0,
        return_sequences=False,
        implementation='2',
        name=''
    )
    
    m11 = M.dl_layer_dropout.v1(
        inputs=m4.data,
        rate=0.4,
        noise_shape='',
        name=''
    )
    
    m10 = M.dl_layer_dense.v1(
        inputs=m11.data,
        units=32,
        activation='tanh',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m12 = M.dl_layer_dropout.v1(
        inputs=m10.data,
        rate=0.8,
        noise_shape='',
        name=''
    )
    
    m9 = M.dl_layer_dense.v1(
        inputs=m12.data,
        units=1,
        activation='sigmoid',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m5 = M.dl_model_init.v1(
        inputs=m3.data,
        outputs=m9.data
    )
    
    m8 = M.input_features.v1(
        features="""(close_0/close_1-1)*10
    (high_0/high_1-1)*10
    (low_0/low_1-1)*10
    (open_0/open_1-1)*10
    (volume_0/volume_1-1)*10"""
    )
    
    m24 = M.instruments.v2(
        start_date='2015-07-02',
        end_date='2017-10-30',
        market='CN_STOCK_A',
        instrument_list='600009.SHA',
        max_count=0
    )
    
    m21 = M.advanced_auto_labeler.v2(
        instruments=m24.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    where(shift(close, -10) / close -1>0,1,0)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True,
        user_functions={}
    )
    
    m22 = M.general_feature_extractor.v7(
        instruments=m24.data,
        features=m8.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m23 = M.derived_feature_extractor.v3(
        input_data=m22.data,
        features=m8.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m17 = M.join.v3(
        data1=m21.data,
        data2=m23.data,
        on='date',
        how='inner',
        sort=True
    )
    
    m19 = M.dropnan.v2(
        input_data=m17.data
    )
    
    m18 = M.standardlize.v8(
        input_1=m19.data,
        input_2=m8.data,
        columns_input=''
    )
    
    m25 = M.dl_convert_to_bin.v2(
        input_data=m18.data,
        features=m8.data,
        window_size=50,
        feature_clip=5,
        flatten=False,
        window_along_col=''
    )
    
    m30 = M.cached.v3(
        input_2=m25.data,
        run=m30_run_bigquant_run,
        post_run=m30_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m6 = M.dl_model_train.v1(
        input_model=m5.data,
        training_data=m30.data_1,
        validation_data=m30.data_2,
        optimizer='Adam',
        loss='binary_crossentropy',
        metrics='accuracy',
        batch_size=2048,
        epochs=10,
        custom_objects=m6_custom_objects_bigquant_run,
        n_gpus=0,
        verbose='1:输出进度条记录'
    )
    
    m28 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-02-11'),
        end_date=T.live_run_param('trading_date', '2019-09-01'),
        market='CN_STOCK_A',
        instrument_list='600009.SHA',
        max_count=0
    )
    
    m16 = M.general_feature_extractor.v7(
        instruments=m28.data,
        features=m8.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m26 = M.derived_feature_extractor.v3(
        input_data=m16.data,
        features=m8.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m29 = M.dropnan.v2(
        input_data=m26.data
    )
    
    m20 = M.standardlize.v8(
        input_1=m29.data,
        input_2=m8.data,
        columns_input=''
    )
    
    m27 = M.dl_convert_to_bin.v2(
        input_data=m20.data,
        features=m8.data,
        window_size=50,
        feature_clip=5,
        flatten=False,
        window_along_col=''
    )
    
    m7 = M.dl_model_predict.v1(
        trained_model=m6.data,
        input_data=m27.data,
        batch_size=10240,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m2 = M.cached.v3(
        input_1=m7.data,
        input_2=m27.data,
        input_3=m20.data,
        run=m2_run_bigquant_run,
        post_run=m2_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m1 = M.trade.v4(
        instruments=m28.data,
        options_data=m2.data_1,
        start_date='',
        end_date='',
        initialize=m1_initialize_bigquant_run,
        handle_data=m1_handle_data_bigquant_run,
        prepare=m1_prepare_bigquant_run,
        before_trading_start=m1_before_trading_start_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='真实价格',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark=''
    )
    
    DataSource(40af4ed7616c4867b280e42d961c472cT)
    
    • 收益率0.0%
    • 年化收益率0.0%
    • 基准收益率11.52%
    • 阿尔法-0.03
    • 贝塔0.0
    • 夏普比率n/a
    • 胜率0.0
    • 盈亏比0.0
    • 收益波动率0.0%
    • 信息比率-0.01
    • 最大回撤0.0%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-09fb2986928145cb91b45330c8fcb8c5"}/bigcharts-data-end
    In [2]:
    # 方法一:手动绘制曲线
    from matplotlib import pyplot as plt
    
    train_loss = m6.data.read()["history"]["loss"]
    val_loss = m6.data.read()["history"]["val_loss"]
    
    plt.plot(train_loss, label="train")
    plt.plot(val_loss, label="validation")
    plt.legend()
    plt.show()
    
    In [3]:
    train_acc = m6.data.read()["history"]["accuracy"]
    val_acc = m6.data.read()["history"]["val_accuracy"]
    
    plt.plot(train_acc, label="train")
    plt.plot(val_acc, label="validation")
    plt.legend()
    plt.show()