请问这个模型不交易应该怎么改?谢谢

策略分享
标签: #<Tag:0x00007f4ccb6beda0>

(a1750271086) #1
克隆策略

使用深度学习技术预测股票价格

版本 v1.0

目录

  • ### 深度学习策略的交易规则

  • ### 策略构建步骤

  • ### 策略的实现

正文

一、深度学习策略的交易规则

  • 买入条件:预测的上涨概率>0.5,则买入或保持已有持仓。
  • 卖出条件 :预测的上涨概率<0.5,则卖出已有股票。

二、策略构建步骤

1、确定股票池和数据起止时间

  • 在证券代码列表m24模块中输入要回测的单只股票,以及数据的起止日期(含训练集和验证集)。

2、确定因子

  • 在输入特征列表m8模块中输入用于预测的N个因子表达式。

3、获取基础数据

  • 通过自定义模块m23获取指定股票池的基础数据,如收盘价等字段。

4、确定并计算模型标注

  • 通过自定义模块m16计算需要的标注指标,本例中首先计算未来10天收益df['return'],然后根据df['return']的正负来给每日数据标注1或0,来标识涨跌。

5、抽取因子数据

  • 通过衍生数据抽取模块m26计算因子数据。

6、合并标注与因子数据

  • 通过连接数据m17模块合并因子数据和标注数据。

7、划分训练集和预测集

  • 通过数据过滤模块m19和m20中设置的日期范围划分训练集数据和预测集数据。

8、生成序列窗口滚动数据集

  • 通过序列窗口滚动(深度学习)模块将训练集和预测集的数据生成固定窗口长度的数据序列,为后续模型训练和预测做准备。

9、构建LSTM + CNN模型构架

  • 在画布左侧模块列表中依次拖入输入层模块、Reshape层模块、Conv2D层模块、Reshape层模块、LSTM层模块、Dropout层模块和全连接层模块(两组),构成深度学习网络构架,

    最后通过“构建(深度学习)”模块组装各层。这里需要注意:

    输入层的shape参数是 窗口滚动数据集的大小 X 因子数量 , 本例为 50 行 X 5个因子

    ReShape层的参数是 窗口滚动数据集的大小 X 因子数量 X 1 ,本例为 50 行 X 5个因子 X1

    Conv2D层中的 kernel_size参数是滑动窗口的尺寸,本例中使用 3行 X 5列 的窗口, 每次滑动的步长为 1行 X 1列 , 卷积核数目为32,这里的窗口设置决定了后面ReShape层的参数

    ReShape层中的target_shape 参数,这是由 窗口滚动数据集 X 因子数量 和 Conv2D层中设置的窗口尺寸以及步长决定的。本例中 50行 X 5因子 的输入数据,使用 3行 X5列 的窗口滑动取数据,

    每次移动1行,共计可以得到48次数据(即可以通过滑动3行 X 5列的窗口48次来获取完整的数据),因此target_shape= 48 X 卷积核数32

    LSTM层的输出空间维度设置为卷积核数32,并设置激活函数

    Dropout层是防止过度拟合采用的主动裁剪数据技术,这里设置rate 为0.8

    全连接层共两层,第一层的输出空间维度与LSTM的输出维度保持一致为32,第二层将第一层的32维数据转变为1维数据输出,即获取预测的label值,此例为0到1之间的连续值,可以认为是上涨的概率。

10、训练深度学习模型

  • 在画布左侧模块列表中拖入“训练(深度学习)”模块m6,设置属性中的优化器、目标函数、评估指标、每次训练的数据量batch_size、迭代次数epochs和GPU的数量以及日志输出频率。

11、使用深度学习模型预测

  • 在画布左侧模块列表中拖入“预测(深度学习)”模块m7,并将“训练(深度学习)”模块m6的模型输出和验证集的序列窗口滚动数据集传给预测模块,通过预测模块即根据股票验证集的数据预测上涨的概率。

12、根据模型预测结果构建策略

  • 如果当日预测的上涨概率大于0.5,则保持持仓或买入

  • 如果当日预测的上涨概率小于0.5,则卖出股票或保持空仓。

13、模拟回测

  • 通过 trade 模块中的初始化函数定义交易手续费和滑点,通过 context.prediction 获取每日的上涨概率预测结果;

  • 通过 trade 模块中的主函数(handle函数)查看每日的买卖交易信号,按照买卖原则执行相应的买入/卖出操作。

三、策略的实现

可视化策略实现如下:

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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_ds = input_1\n df = input_ds.read_df()\n df['return'] = (df.close.shift(-10)/df.close - 1)\n df['label'] = np.where(df['return'] > 0, 1, 0)\n ds = DataSource.write_df(df)\n return Outputs(data_1=ds)\n\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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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 fields = ['open','high','low','close','volume']\n input_1_df = input_1.read_pickle()\n ins = input_1_df['instruments']\n start_date = input_1_df['start_date']\n end_date = input_1_df['end_date']\n df = D.history_data(ins, start_date, end_date, fields) \n data_1 = DataSource.write_df(df)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\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 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":"-616"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-616"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-616"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-616","OutputType":null},{"Name":"data_2","NodeId":"-616","OutputType":null},{"Name":"data_3","NodeId":"-616","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":23,"IsPartOfPartialRun":null,"Comment":"获取基本数据","CommentCollapsed":false},{"Id":"-620","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2015-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2018-10-30","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"600009.SHA","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"-620"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-620","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":24,"IsPartOfPartialRun":null,"Comment":"证券标的及起始截止时间","CommentCollapsed":false},{"Id":"-692","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-692"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-692"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-692","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":26,"IsPartOfPartialRun":null,"Comment":"计算需要使用的特征","CommentCollapsed":false},{"Id":"-281","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"2017-04-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 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context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"# 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。\ndef bigquant_run(context, data):\n 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    In [1]:
    # 本代码由可视化策略环境自动生成 2019年5月22日 16:52
    # 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 m23_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 m23_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: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: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='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"""
    )
    
    m24 = M.instruments.v2(
        start_date='2015-01-01',
        end_date='2018-10-30',
        market='CN_STOCK_A',
        instrument_list='600009.SHA',
        max_count=0
    )
    
    m23 = M.cached.v3(
        input_1=m24.data,
        run=m23_run_bigquant_run,
        post_run=m23_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m16 = M.cached.v3(
        input_1=m23.data_1,
        run=m16_run_bigquant_run,
        post_run=m16_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m26 = M.derived_feature_extractor.v3(
        input_data=m23.data_1,
        features=m8.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m17 = M.join.v3(
        data1=m16.data_1,
        data2=m26.data,
        on='date',
        how='inner',
        sort=True
    )
    
    m18 = M.dropnan.v1(
        input_data=m17.data
    )
    
    m19 = M.filter.v3(
        input_data=m18.data,
        expr='date<\'2017-03-01\'',
        output_left_data=False
    )
    
    m25 = M.dl_convert_to_bin.v2(
        input_data=m19.data,
        features=m8.data,
        window_size=50,
        feature_clip=5,
        flatten=False,
        window_along_col=''
    )
    
    m6 = M.dl_model_train.v1(
        input_model=m5.data,
        training_data=m25.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'
    )
    
    m20 = M.filter.v3(
        input_data=m18.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=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=m24.data,
        options_data=m2.data_1,
        start_date='2017-04-01',
        end_date='',
        handle_data=m1_handle_data_bigquant_run,
        prepare=m1_prepare_bigquant_run,
        initialize=m1_initialize_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=''
    )
    
    Using TensorFlow backend.
    
    Epoch 1/10
    523/523 [==============================] - 2s 4ms/step - loss: 1.0089 - acc: 0.5201
    Epoch 2/10
    523/523 [==============================] - 0s 589us/step - loss: 1.0601 - acc: 0.4780
    Epoch 3/10
    523/523 [==============================] - 0s 407us/step - loss: 1.0700 - acc: 0.4837
    Epoch 4/10
    523/523 [==============================] - 0s 420us/step - loss: 1.0632 - acc: 0.5163
    Epoch 5/10
    523/523 [==============================] - 0s 380us/step - loss: 1.0479 - acc: 0.5182
    Epoch 6/10
    523/523 [==============================] - 0s 410us/step - loss: 1.0436 - acc: 0.5048
    Epoch 7/10
    523/523 [==============================] - 0s 450us/step - loss: 1.0337 - acc: 0.4952
    Epoch 8/10
    523/523 [==============================] - 0s 374us/step - loss: 1.0184 - acc: 0.4876
    Epoch 9/10
    523/523 [==============================] - 0s 466us/step - loss: 0.9484 - acc: 0.5468
    Epoch 10/10
    523/523 [==============================] - 0s 404us/step - loss: 1.0377 - acc: 0.5201
    
    DataSource(96f108ac29d143409540401141b61033T, v3)
    
    准确率0.6020151133501259
    
    2017-04-05 15:00:00+00:00 买入!
    
    • 收益率61.27%
    • 年化收益率36.73%
    • 基准收益率-10.01%
    • 阿尔法0.42
    • 贝塔0.81
    • 夏普比率0.96
    • 胜率1.0
    • 盈亏比0.0
    • 收益波动率36.51%
    • 信息比率0.08
    • 最大回撤29.6%
    bigcharts-data-start/{"__id":"bigchart-36784d1083844e4285cba45f08d5b2aa","__type":"tabs"}/bigcharts-data-end

    (达达) #2

    最后的Trade模块有个回测模块的起止日期填写有误。


    (达达) #3
    克隆策略

    使用深度学习技术预测股票价格

    版本 v1.0

    目录

    • ### 深度学习策略的交易规则

    • ### 策略构建步骤

    • ### 策略的实现

    正文

    一、深度学习策略的交易规则

    • 买入条件:预测的上涨概率>0.5,则买入或保持已有持仓。
    • 卖出条件 :预测的上涨概率<0.5,则卖出已有股票。

    二、策略构建步骤

    1、确定股票池和数据起止时间

    • 在证券代码列表m24模块中输入要回测的单只股票,以及数据的起止日期(含训练集和验证集)。

    2、确定因子

    • 在输入特征列表m8模块中输入用于预测的N个因子表达式。

    3、获取基础数据

    • 通过自定义模块m23获取指定股票池的基础数据,如收盘价等字段。

    4、确定并计算模型标注

    • 通过自定义模块m16计算需要的标注指标,本例中首先计算未来10天收益df['return'],然后根据df['return']的正负来给每日数据标注1或0,来标识涨跌。

    5、抽取因子数据

    • 通过衍生数据抽取模块m26计算因子数据。

    6、合并标注与因子数据

    • 通过连接数据m17模块合并因子数据和标注数据。

    7、划分训练集和预测集

    • 通过数据过滤模块m19和m20中设置的日期范围划分训练集数据和预测集数据。

    8、生成序列窗口滚动数据集

    • 通过序列窗口滚动(深度学习)模块将训练集和预测集的数据生成固定窗口长度的数据序列,为后续模型训练和预测做准备。

    9、构建LSTM + CNN模型构架

    • 在画布左侧模块列表中依次拖入输入层模块、Reshape层模块、Conv2D层模块、Reshape层模块、LSTM层模块、Dropout层模块和全连接层模块(两组),构成深度学习网络构架,

      最后通过“构建(深度学习)”模块组装各层。这里需要注意:

      输入层的shape参数是 窗口滚动数据集的大小 X 因子数量 , 本例为 50 行 X 5个因子

      ReShape层的参数是 窗口滚动数据集的大小 X 因子数量 X 1 ,本例为 50 行 X 5个因子 X1

      Conv2D层中的 kernel_size参数是滑动窗口的尺寸,本例中使用 3行 X 5列 的窗口, 每次滑动的步长为 1行 X 1列 , 卷积核数目为32,这里的窗口设置决定了后面ReShape层的参数

      ReShape层中的target_shape 参数,这是由 窗口滚动数据集 X 因子数量 和 Conv2D层中设置的窗口尺寸以及步长决定的。本例中 50行 X 5因子 的输入数据,使用 3行 X5列 的窗口滑动取数据,

      每次移动1行,共计可以得到48次数据(即可以通过滑动3行 X 5列的窗口48次来获取完整的数据),因此target_shape= 48 X 卷积核数32

      LSTM层的输出空间维度设置为卷积核数32,并设置激活函数

      Dropout层是防止过度拟合采用的主动裁剪数据技术,这里设置rate 为0.8

      全连接层共两层,第一层的输出空间维度与LSTM的输出维度保持一致为32,第二层将第一层的32维数据转变为1维数据输出,即获取预测的label值,此例为0到1之间的连续值,可以认为是上涨的概率。

    10、训练深度学习模型

    • 在画布左侧模块列表中拖入“训练(深度学习)”模块m6,设置属性中的优化器、目标函数、评估指标、每次训练的数据量batch_size、迭代次数epochs和GPU的数量以及日志输出频率。

    11、使用深度学习模型预测

    • 在画布左侧模块列表中拖入“预测(深度学习)”模块m7,并将“训练(深度学习)”模块m6的模型输出和验证集的序列窗口滚动数据集传给预测模块,通过预测模块即根据股票验证集的数据预测上涨的概率。

    12、根据模型预测结果构建策略

    • 如果当日预测的上涨概率大于0.5,则保持持仓或买入

    • 如果当日预测的上涨概率小于0.5,则卖出股票或保持空仓。

    13、模拟回测

    • 通过 trade 模块中的初始化函数定义交易手续费和滑点,通过 context.prediction 获取每日的上涨概率预测结果;

    • 通过 trade 模块中的主函数(handle函数)查看每日的买卖交易信号,按照买卖原则执行相应的买入/卖出操作。

    三、策略的实现

    可视化策略实现如下:

      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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_ds = input_1\n df = input_ds.read_df()\n df['return'] = (df.close.shift(-10)/df.close - 1)\n df['label'] = np.where(df['return'] > 0, 1, 0)\n ds = DataSource.write_df(df)\n return Outputs(data_1=ds)\n\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 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,"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":17,"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":18,"Comment":"去掉为nan的数据","CommentCollapsed":true},{"Id":"-616","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 fields = ['open','high','low','close','volume']\n input_1_df = input_1.read_pickle()\n ins = input_1_df['instruments']\n start_date = input_1_df['start_date']\n end_date = input_1_df['end_date']\n df = D.history_data(ins, start_date, end_date, fields) \n data_1 = DataSource.write_df(df)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\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 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":"-616"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-616"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-616"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-616","OutputType":null},{"Name":"data_2","NodeId":"-616","OutputType":null},{"Name":"data_3","NodeId":"-616","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":23,"Comment":"获取基本数据","CommentCollapsed":false},{"Id":"-620","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2010-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2017-03-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"600009.SHA","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"-620"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-620","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":24,"Comment":"训练集起止日期","CommentCollapsed":true},{"Id":"-692","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-692"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-692"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-692","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":26,"Comment":"计算需要使用的特征","CommentCollapsed":false},{"Id":"-281","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 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))","ValueType":"Literal","LinkedGlobalParameter":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 ","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n pass\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"# 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。\ndef bigquant_run(context, data):\n pass\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"close","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"capital_base","Value":"50000","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"auto_cancel_non_tradable_orders","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"data_frequency","Value":"daily","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"price_type","Value":"真实价格","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"product_type","Value":"股票","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"plot_charts","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"backtest_only","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-281"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-281"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-281"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-281"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-281"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-281","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":1,"Comment":"","CommentCollapsed":true},{"Id":"-333","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"50","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":5,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-333"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-333"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-333","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":25,"Comment":"","CommentCollapsed":true},{"Id":"-341","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"50","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":5,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-341"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-341"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-341","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":27,"Comment":"","CommentCollapsed":true},{"Id":"-289","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2017-03-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2019-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"600009.SHA","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"-289"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-289","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":21,"Comment":"证券标的及起始截止时间","CommentCollapsed":true},{"Id":"-301","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 fields = ['open','high','low','close','volume']\n input_1_df = input_1.read_pickle()\n ins = input_1_df['instruments']\n start_date = input_1_df['start_date']\n end_date = input_1_df['end_date']\n df = D.history_data(ins, start_date, end_date, fields) \n data_1 = DataSource.write_df(df)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\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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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_ds = input_1\n df = input_ds.read_df()\n df['return'] = (df.close.shift(-10)/df.close - 1)\n df['label'] = np.where(df['return'] > 0, 1, 0)\n ds = DataSource.write_df(df)\n return Outputs(data_1=ds)\n\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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      In [18]:
      # 本代码由可视化策略环境自动生成 2019年7月10日 18:37
      # 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 m23_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 m23_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 m22_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 m22_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 m19_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 m19_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 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='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"""
      )
      
      m24 = M.instruments.v2(
          start_date='2010-01-01',
          end_date='2017-03-01',
          market='CN_STOCK_A',
          instrument_list='600009.SHA',
          max_count=0
      )
      
      m23 = M.cached.v3(
          input_1=m24.data,
          run=m23_run_bigquant_run,
          post_run=m23_post_run_bigquant_run,
          input_ports='',
          params='{}',
          output_ports=''
      )
      
      m16 = M.cached.v3(
          input_1=m23.data_1,
          run=m16_run_bigquant_run,
          post_run=m16_post_run_bigquant_run,
          input_ports='',
          params='{}',
          output_ports=''
      )
      
      m26 = M.derived_feature_extractor.v3(
          input_data=m23.data_1,
          features=m8.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False,
          user_functions={}
      )
      
      m17 = M.join.v3(
          data1=m16.data_1,
          data2=m26.data,
          on='date',
          how='inner',
          sort=True
      )
      
      m18 = M.dropnan.v1(
          input_data=m17.data
      )
      
      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=''
      )
      
      m6 = M.dl_model_train.v1(
          input_model=m5.data,
          training_data=m25.data,
          optimizer='Adam',
          loss='categorical_hinge',
          metrics='accuracy',
          batch_size=6144,
          epochs=60,
          n_gpus=1,
          verbose='1:输出进度条记录'
      )
      
      m21 = M.instruments.v2(
          start_date=T.live_run_param('trading_date', '2017-03-01'),
          end_date=T.live_run_param('trading_date', '2019-01-01'),
          market='CN_STOCK_A',
          instrument_list='600009.SHA',
          max_count=0
      )
      
      m22 = M.cached.v3(
          input_1=m21.data,
          run=m22_run_bigquant_run,
          post_run=m22_post_run_bigquant_run,
          input_ports='',
          params='{}',
          output_ports=''
      )
      
      m28 = M.derived_feature_extractor.v3(
          input_data=m22.data_1,
          features=m8.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False,
          user_functions={}
      )
      
      m19 = M.cached.v3(
          input_1=m22.data_1,
          run=m19_run_bigquant_run,
          post_run=m19_post_run_bigquant_run,
          input_ports='',
          params='{}',
          output_ports=''
      )
      
      m20 = M.join.v3(
          data1=m28.data,
          data2=m19.data_1,
          on='date',
          how='inner',
          sort=True
      )
      
      m29 = M.dropnan.v1(
          input_data=m20.data
      )
      
      m27 = M.dl_convert_to_bin.v2(
          input_data=m29.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=1,
          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=m29.data,
          run=m2_run_bigquant_run,
          post_run=m2_post_run_bigquant_run,
          input_ports='',
          params='{}',
          output_ports=''
      )
      
      m1 = M.trade.v4(
          instruments=m21.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,
          order_price_field_buy='open',
          order_price_field_sell='close',
          capital_base=50000,
          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(dbd961078d5f4b94a0bab85263defcecT, v3)
      
      准确率0.5886363636363636
      
      2017-03-02 15:00:00+00:00 买入!
      
      • 收益率88.13%
      • 年化收益率42.35%
      • 基准收益率-12.81%
      • 阿尔法0.46
      • 贝塔0.86
      • 夏普比率1.09
      • 胜率1.0
      • 盈亏比0.0
      • 收益波动率35.29%
      • 信息比率0.09
      • 最大回撤29.42%
      bigcharts-data-start/{"__id":"bigchart-0f627d63e32142a4834cd0c001a1f009","__type":"tabs"}/bigcharts-data-end