直接克隆过来的,为啥出这个错误呢

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克隆策略

使用深度学习技术预测股票价格

    {"Description":"实验创建于2017/11/15","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-306:options_data","SourceOutputPortId":"-214:data_1"},{"DestinationInputPortId":"-316:inputs","SourceOutputPortId":"-210:data"},{"DestinationInputPortId":"-403:inputs","SourceOutputPortId":"-210:data"},{"DestinationInputPortId":"-14834:inputs","SourceOutputPortId":"-218:data"},{"DestinationInputPortId":"-320:input_model","SourceOutputPortId":"-316:data"},{"DestinationInputPortId":"-332:trained_model","SourceOutputPortId":"-320:data"},{"DestinationInputPortId":"-214:input_1","SourceOutputPortId":"-332:data"},{"DestinationInputPortId":"-278:features","SourceOutputPortId":"-2295:data"},{"DestinationInputPortId":"-287:features","SourceOutputPortId":"-2295:data"},{"DestinationInputPortId":"-295:features","SourceOutputPortId":"-2295:data"},{"DestinationInputPortId":"-316:outputs","SourceOutputPortId":"-259:data"},{"DestinationInputPortId":"-14841:inputs","SourceOutputPortId":"-14806:data"},{"DestinationInputPortId":"-14806:inputs","SourceOutputPortId":"-14834:data"},{"DestinationInputPortId":"-259:inputs","SourceOutputPortId":"-14841:data"},{"DestinationInputPortId":"-408:inputs","SourceOutputPortId":"-403:data"},{"DestinationInputPortId":"-446:inputs","SourceOutputPortId":"-408:data"},{"DestinationInputPortId":"-218:inputs","SourceOutputPortId":"-446:data"},{"DestinationInputPortId":"-2290:data1","SourceOutputPortId":"-1966:data_1"},{"DestinationInputPortId":"-2296:input_data","SourceOutputPortId":"-2290:data"},{"DestinationInputPortId":"-2300:input_data","SourceOutputPortId":"-2296:data"},{"DestinationInputPortId":"-2306:input_data","SourceOutputPortId":"-2296:data"},{"DestinationInputPortId":"-287:input_data","SourceOutputPortId":"-2300:data"},{"DestinationInputPortId":"-214:input_3","SourceOutputPortId":"-2306:data"},{"DestinationInputPortId":"-295:input_data","SourceOutputPortId":"-2306:data"},{"DestinationInputPortId":"-1966:input_1","SourceOutputPortId":"-616:data_1"},{"DestinationInputPortId":"-278:input_data","SourceOutputPortId":"-616:data_1"},{"DestinationInputPortId":"-616:input_1","SourceOutputPortId":"-620:data"},{"DestinationInputPortId":"-306:instruments","SourceOutputPortId":"-620:data"},{"DestinationInputPortId":"-2290:data2","SourceOutputPortId":"-278:data"},{"DestinationInputPortId":"-320:training_data","SourceOutputPortId":"-287:data"},{"DestinationInputPortId":"-332:input_data","SourceOutputPortId":"-295:data"},{"DestinationInputPortId":"-214:input_2","SourceOutputPortId":"-295:data"}],"ModuleNodes":[{"Id":"-214","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"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 input_series = input_1\n input_df = input_2\n test_data = input_df.read_pickle()\n pred_label = input_series.read_pickle()\n \n pred_result = pred_label.reshape(pred_label.shape[0]) \n dt = input_3.read_df()['date'][-1*len(pred_result):]\n pred_df = pd.Series(pred_result, index=dt)\n ds = DataSource.write_df(pred_df)\n \n pred_label = np.where(pred_label>0.5,1,0)\n labels = test_data['y']\n print('准确率%s'%(np.mean(pred_label==labels)))\n \n return Outputs(data_1=ds)\n","ValueType":"Literal","LinkedGlobalParameter":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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outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-1966"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-1966"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-1966"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-1966","OutputType":null},{"Name":"data_2","NodeId":"-1966","OutputType":null},{"Name":"data_3","NodeId":"-1966","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":16,"IsPartOfPartialRun":null,"Comment":"数据标注","CommentCollapsed":false},{"Id":"-2290","ModuleId":"BigQuantSpace.join.join-v3","ModuleParameters":[{"Name":"on","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"how","Value":"inner","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sort","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data1","NodeId":"-2290"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"-2290"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-2290","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":18,"IsPartOfPartialRun":null,"Comment":"标注特征连接","CommentCollapsed":false},{"Id":"-2296","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-2296"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-2296","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":19,"IsPartOfPartialRun":null,"Comment":"去掉为nan的数据","CommentCollapsed":true},{"Id":"-2300","ModuleId":"BigQuantSpace.filter.filter-v3","ModuleParameters":[{"Name":"expr","Value":"date<'2017-03-01'","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_left_data","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-2300"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-2300","OutputType":null},{"Name":"left_data","NodeId":"-2300","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"训练数据","CommentCollapsed":false},{"Id":"-2306","ModuleId":"BigQuantSpace.filter.filter-v3","ModuleParameters":[{"Name":"expr","Value":"date>'2017-03-01'","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_left_data","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-2306"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-2306","OutputType":null},{"Name":"left_data","NodeId":"-2306","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":21,"IsPartOfPartialRun":null,"Comment":"测试数据","CommentCollapsed":false},{"Id":"-616","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# 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    In [1]:
    # 本代码由可视化策略环境自动生成 ‎2019‎年‎11月‎21‎日‎ ‎18‎:‎25
    # 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 m24_run_bigquant_run(input_1, input_2, input_3):
        fields = ['open','high','low','close','volume']
        input_1_df = input_1.read_pickle()
        ins = input_1_df['instruments']
        start_date = input_1_df['start_date']
        end_date = input_1_df['end_date']
        df = D.history_data(ins, start_date, end_date, fields)     
        data_1 = DataSource.write_df(df)
        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 m24_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 m16_run_bigquant_run(input_1, input_2, input_3):
        input_ds = input_1
        df = input_ds.read_df()
        df['return'] = (df.close.shift(-10)/df.close - 1)
        df['label'] = np.where(df['return'] > 0, 1, 0)
        ds = DataSource.write_df(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 m16_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):
        input_series = input_1
        input_df = input_2
        test_data = input_df.read_pickle()
        pred_label = input_series.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)
        
        pred_label = np.where(pred_label>0.5,1,0)
        labels = test_data['y']
        print('准确率%s'%(np.mean(pred_label==labels)))
        
        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 m29_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 m29_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
        #print('date: ',data.current_dt, '持仓: ', cur_position)
        
        # 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 m29_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 m29_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='0',
        name=''
    )
    
    m11 = M.dl_layer_dropout.v1(
        inputs=m4.data,
        rate=0.8,
        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/shift(close,1)-1)*10
    (high/shift(high,1)-1)*10
    (low/shift(low,1)-1)*10
    (open/shift(open,1)-1)*10
    (volume/shift(volume,1)-1)*10"""
    )
    
    m25 = M.instruments.v2(
        start_date='2015-01-01',
        end_date='2018-02-07',
        market='CN_STOCK_A',
        instrument_list='600009.SHA',
        max_count=0
    )
    
    m24 = M.cached.v3(
        input_1=m25.data,
        run=m24_run_bigquant_run,
        post_run=m24_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m16 = M.cached.v3(
        input_1=m24.data_1,
        run=m16_run_bigquant_run,
        post_run=m16_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m17 = M.derived_feature_extractor.v3(
        input_data=m24.data_1,
        features=m8.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m18 = M.join.v3(
        data1=m16.data_1,
        data2=m17.data,
        on='date',
        how='inner',
        sort=True
    )
    
    m19 = M.dropnan.v1(
        input_data=m18.data
    )
    
    m20 = M.filter.v3(
        input_data=m19.data,
        expr='date<\'2017-03-01\'',
        output_left_data=False
    )
    
    m27 = M.dl_convert_to_bin.v2(
        input_data=m20.data,
        features=m8.data,
        window_size=50,
        feature_clip=5,
        flatten=True,
        window_along_col='instrument'
    )
    
    m6 = M.dl_model_train.v1(
        input_model=m5.data,
        training_data=m27.data,
        optimizer='Adam',
        loss='binary_crossentropy',
        metrics='accuracy',
        batch_size=2048,
        epochs=10,
        n_gpus=1,
        verbose='1:输出进度条记录'
    )
    
    m21 = M.filter.v3(
        input_data=m19.data,
        expr='date>\'2017-03-01\'',
        output_left_data=False
    )
    
    m28 = M.dl_convert_to_bin.v2(
        input_data=m21.data,
        features=m8.data,
        window_size=50,
        feature_clip=5,
        flatten=True,
        window_along_col='instrument'
    )
    
    m7 = M.dl_model_predict.v1(
        trained_model=m6.data,
        input_data=m28.data,
        batch_size=10240,
        n_gpus=2,
        verbose='2:每个epoch输出一行记录'
    )
    
    m2 = M.cached.v3(
        input_1=m7.data,
        input_2=m28.data,
        input_3=m21.data,
        run=m2_run_bigquant_run,
        post_run=m2_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m29 = M.trade.v4(
        instruments=m25.data,
        options_data=m2.data_1,
        start_date='2017-04-01',
        end_date='',
        initialize=m29_initialize_bigquant_run,
        handle_data=m29_handle_data_bigquant_run,
        prepare=m29_prepare_bigquant_run,
        before_trading_start=m29_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='000300.SHA'
    )
    
    Using TensorFlow backend.
    

    序列窗口滚动(深度学习)(dl_convert_to_bin)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    ---------------------------------------------------------------------------
    KeyError                                  Traceback (most recent call last)
    <ipython-input-1-10f5a6134104> in <module>()
        301     feature_clip=5,
        302     flatten=True,
    --> 303     window_along_col='instrument'
        304 )
        305 
    
    KeyError: 'instrument'

    (达达) #2

    参考一下学院的 机器学习板块中的案例