如何查看深度学习模型中间层结果

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(达达) #1

有些平台朋友在研究深度学习模型时,可能想要知道模型的结构以及中间层的结果。可以参考下面的例子:

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和m28模块中输入要回测的单只股票,以及数据的起止日期(分别为训练集和验证集)。

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

  • 通过基础特征数据抽取模块m22和m16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

  • 通过自动标注股票模块m21计算需要的标注指标,本例中首先计算未来10天收益,然后根据其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m23和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、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m25和m27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。

8、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。

9、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。

10、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。

11、将预测结果与时间拼接

  • 通过自定义模块m2将预测的每个滚动序列窗口的最后一个值最为当日的预测结果,并与预测集数据的时间列拼接,形成最终的每日预测结果。

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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    In [2]:
    # 本代码由可视化策略环境自动生成 2019年10月12日 09:40
    # 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 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='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_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='2015-01-01',
        end_date=T.live_run_param('trading_date', '2017-03-01'),
        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
    )
    
    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='binary_crossentropy',
        metrics='accuracy',
        batch_size=2048,
        epochs=10,
        n_gpus=1,
        verbose='1:输出进度条记录'
    )
    
    m28 = M.instruments.v2(
        start_date='2017-03-01',
        end_date='2019-01-01',
        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={}
    )
    
    m20 = M.dropnan.v1(
        input_data=m26.data
    )
    
    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=''
    )
    
    Epoch 1/10
    525/525 [==============================] - 1s 2ms/step - loss: 1.2100 - acc: 0.4781
    Epoch 2/10
    525/525 [==============================] - 0s 195us/step - loss: 1.3012 - acc: 0.4686
    Epoch 3/10
    525/525 [==============================] - 0s 216us/step - loss: 1.2714 - acc: 0.4724
    Epoch 4/10
    525/525 [==============================] - 0s 230us/step - loss: 1.0524 - acc: 0.5524
    Epoch 5/10
    525/525 [==============================] - 0s 255us/step - loss: 1.1205 - acc: 0.4971
    Epoch 6/10
    525/525 [==============================] - 0s 198us/step - loss: 1.0773 - acc: 0.5162
    Epoch 7/10
    525/525 [==============================] - 0s 210us/step - loss: 1.1179 - acc: 0.5029
    Epoch 8/10
    525/525 [==============================] - 0s 197us/step - loss: 1.1792 - acc: 0.4648
    Epoch 9/10
    525/525 [==============================] - 0s 239us/step - loss: 1.1691 - acc: 0.5048
    Epoch 10/10
    525/525 [==============================] - 0s 224us/step - loss: 1.1771 - acc: 0.4876
    
    DataSource(b0388ce384c94f4490a0adf7a10aa5a0T, v3)
    
    2017-03-01 15:00:00+00:00 买入!
    
    • 收益率87.83%
    • 年化收益率42.22%
    • 基准收益率-12.81%
    • 阿尔法0.46
    • 贝塔0.87
    • 夏普比率1.08
    • 胜率1.0
    • 盈亏比0.0
    • 收益波动率35.59%
    • 信息比率0.09
    • 最大回撤29.6%
    bigcharts-data-start/{"__id":"bigchart-62baa074616f4e63833dc6c88fc773fb","__type":"tabs"}/bigcharts-data-end

    读取模型训练结果

    In [ ]:
    model = m6.load_model()
    for i,layer in enumerate(model.layers):
        print(i,layer.name,layer.output_shape)
    

    构建中间层模型

    In [ ]:
    from keras.models import Model
    layer_model = Model(input=model.input, output=model.get_layer('L3').output)
    

    读取输入input数据

    In [ ]:
    x=m27.data.read()['x']
    

    获取中间层预测结果

    In [ ]:
    layer_result = layer_model.predict(x)
    

    查看中间层结果

    In [ ]:
    layer_result[0:1]
    

    (yangziriver) #2

    请问老师,为什么这个策略只有一次买入,没有其它交易。用中国平安作预测时也是这样,而用600010、600011作预测时就一次交易也没有发生。

    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和m28模块中输入要回测的单只股票,以及数据的起止日期(分别为训练集和验证集)。

    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

    • 通过基础特征数据抽取模块m22和m16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

    • 通过自动标注股票模块m21计算需要的标注指标,本例中首先计算未来10天收益,然后根据其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m23和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、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m25和m27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。

    8、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。

    9、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。

    10、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。

    11、将预测结果与时间拼接

    • 通过自定义模块m2将预测的每个滚动序列窗口的最后一个值最为当日的预测结果,并与预测集数据的时间列拼接,形成最终的每日预测结果。

    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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      In [43]:
      # 本代码由可视化策略环境自动生成 2019年10月14日 16:05
      # 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 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='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_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='2015-01-01',
          end_date=T.live_run_param('trading_date', '2017-03-01'),
          market='CN_STOCK_A',
          instrument_list='601318.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
      )
      
      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='binary_crossentropy',
          metrics='accuracy',
          batch_size=2048,
          epochs=10,
          n_gpus=1,
          verbose='1:输出进度条记录'
      )
      
      m28 = M.instruments.v2(
          start_date='2017-03-01',
          end_date='2019-10-12',
          market='CN_STOCK_A',
          instrument_list='601318.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={}
      )
      
      m20 = M.dropnan.v1(
          input_data=m26.data
      )
      
      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=''
      )
      
      Epoch 1/10
      525/525 [==============================] - 2s 4ms/step - loss: 1.1340 - acc: 0.4590
      Epoch 2/10
      525/525 [==============================] - 0s 384us/step - loss: 1.0469 - acc: 0.5200
      Epoch 3/10
      525/525 [==============================] - 0s 370us/step - loss: 1.1304 - acc: 0.4762
      Epoch 4/10
      525/525 [==============================] - 0s 316us/step - loss: 1.0206 - acc: 0.5010
      Epoch 5/10
      525/525 [==============================] - 0s 337us/step - loss: 1.0157 - acc: 0.5410
      Epoch 6/10
      525/525 [==============================] - 0s 374us/step - loss: 1.0511 - acc: 0.5124
      Epoch 7/10
      525/525 [==============================] - 0s 271us/step - loss: 1.0324 - acc: 0.5124
      Epoch 8/10
      525/525 [==============================] - 0s 323us/step - loss: 0.9875 - acc: 0.5276
      Epoch 9/10
      525/525 [==============================] - 0s 301us/step - loss: 1.0989 - acc: 0.5048
      Epoch 10/10
      525/525 [==============================] - 0s 287us/step - loss: 1.0055 - acc: 0.4838
      
      DataSource(471b9ed6b5a94b25a255e0bdaebb97dcT, v3)
      
      2017-03-01 15:00:00+00:00 买入!
      
      • 收益率165.57%
      • 年化收益率47.08%
      • 基准收益率13.29%
      • 阿尔法0.35
      • 贝塔1.24
      • 夏普比率1.34
      • 胜率1.0
      • 盈亏比0.0
      • 收益波动率30.01%
      • 信息比率0.12
      • 最大回撤28.54%
      bigcharts-data-start/{"__id":"bigchart-3c3182e984a7410d8147562ced7a6c37","__type":"tabs"}/bigcharts-data-end

      读取模型训练结果

      In [44]:
      model = m6.load_model()
      for i,layer in enumerate(model.layers):
          print(i,layer.name,layer.output_shape)
      
      0 L0 (None, 50, 5)
      1 L1 (None, 50, 5, 1)
      2 L2 (None, 48, 1, 32)
      3 L3 (None, 48, 32)
      4 L4 (None, 32)
      5 L5 (None, 32)
      6 L6 (None, 32)
      7 L7 (None, 32)
      8 L8 (None, 1)
      

      构建中间层模型

      In [45]:
      from keras.models import Model
      layer_model = Model(input=model.input, output=model.get_layer('L3').output)
      

      读取输入input数据

      In [46]:
      x=m27.data.read()['x']
      

      获取中间层预测结果

      In [47]:
      layer_result = layer_model.predict(x)
      

      查看中间层结果

      In [48]:
      layer_result[0:1]
      
      Out[48]:
      array([[[0.00642237, 0.00526455, 0.00849133, ..., 0.00811406,
               0.        , 0.00208105],
              [0.00642237, 0.00526455, 0.00849133, ..., 0.00811406,
               0.        , 0.00208105],
              [0.00642237, 0.00526455, 0.00849133, ..., 0.00811406,
               0.        , 0.00208105],
              ...,
              [0.00642237, 0.00526455, 0.00849133, ..., 0.00811406,
               0.        , 0.00208105],
              [0.00642237, 0.00526455, 0.00849133, ..., 0.00811406,
               0.        , 0.00208105],
              [0.        , 0.00841189, 0.02042075, ..., 0.        ,
               0.08327392, 0.        ]]], dtype=float32)

      (达达) #3

      这里只是给出一个实现案例,只出现一次信号一般是因为预测结果可能只有一次触发了买入条件,可以检查一下数据处理流程,模型进行适当调整,添加例如因子或标注的标准化等处理,可以参考学院中DNN模型案例的做法。