为什么保存下来的模型没法读取出来继续训练呢

策略分享
标签: #<Tag:0x00007fc824dcb088>

(wygwsg) #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 [202]:
    # 本代码由可视化策略环境自动生成 2020年1月10日 18:43
    # 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 > 0.9 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='50,19',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m13 = M.dl_layer_reshape.v1(
        inputs=m3.data,
        target_shape='50,19,1',
        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='720,32',
        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='L1L2',
        activity_regularizer_l1=0.003,
        activity_regularizer_l2=0.003,
        kernel_constraint='None',
        recurrent_constraint='None',
        bias_constraint='None',
        dropout=0.1,
        recurrent_dropout=0.1,
        return_sequences=False,
        implementation='2',
        name=''
    )
    
    m11 = M.dl_layer_dropout.v1(
        inputs=m4.data,
        rate=0.1,
        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='L1L2',
        activity_regularizer_l1=0.003,
        activity_regularizer_l2=0.003,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m12 = M.dl_layer_dropout.v1(
        inputs=m10.data,
        rate=0.1,
        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='L1L2',
        bias_regularizer_l1=0.01,
        bias_regularizer_l2=0.01,
        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/open_0-1)*10
    (volume_0/volume_1-1)
    avg_turn_0/avg_turn_4-1
    avg_amount_0/avg_amount_4-1
    ((high_0/open_0)-(close_0/low_0))*50
    (ta_ma(close_0, timeperiod=5)/ta_ma(close_0, timeperiod=30)-1)*10
    ta_rsi(close_0, timeperiod=14)/50-1
    ta_mom(close_0, timeperiod=14)/5
    ta_adx(high_0, low_0, close_0, timeperiod=14)/ta_adx(high_0, low_0, close_0, timeperiod=28)-1
    ta_roc(close_0, timeperiod=14)/10
    (ta_kdj_k(high_0, low_0, close_0, 12, 3)/ta_kdj_d(high_0, low_0, close_0, 12, 3,3)-1)*2
    ta_bias(close_0, timeperiod=28)*10
    volatility_5_0/volatility_60_0-1
    (close_0/open_4-1)*5
    ta_macd_macd_12_26_9_0
    ta_macd_macdhist_12_26_9_0
    ta_macd_macdsignal_12_26_9_0
    avg_mf_net_amount_4/avg_amount_4*5
    mf_net_pct_main_0*5
    """
    )
    
    m24 = M.instruments.v2(
        start_date='2012-07-24',
        end_date='2017-07-24',
        market='CN_STOCK_A',
        instrument_list="""002425.SZA
    """,
        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,-5)/open>1)&(mean(close,-5)/open>1)&(close/open>1),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)
    
    """,
        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=''
    )
    
    m28 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2017-08-18'),
        end_date=T.live_run_param('trading_date', '2020-01-06'),
        market='CN_STOCK_A',
        instrument_list='002425.SZA',
        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=''
    )
    
    m29 = M.model_read.v1(
        filedir='/home/bigquant/work/userlib/',
        filename='test0110-1000'
    )
    
    m6 = M.dl_model_train.v1(
        input_model=m29.data,
        training_data=m25.data,
        optimizer='Adam',
        loss='binary_crossentropy',
        metrics='accuracy',
        batch_size=1600,
        epochs=1000,
        verbose='2:每个epoch输出一行记录'
    )
    
    m19 = M.model_save.v1(
        input_1=m6.data,
        filedir='/home/bigquant/work/userlib/',
        filename='test0110-1000'
    )
    
    m7 = M.dl_model_predict.v1(
        trained_model=m6.data,
        input_data=m27.data,
        batch_size=10240,
        n_gpus=0,
        verbose='0:不显示'
    )
    
    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='000001.SHA'
    )
    

    训练(深度学习)(dl_model_train)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    ---------------------------------------------------------------------------
    AttributeError                            Traceback (most recent call last)
    <ipython-input-202-c905405e5219> in <module>()
        200     batch_size=1600,
        201     epochs=1000,
    --> 202     verbose='2:每个epoch输出一行记录'
        203 )
        204 
    
    AttributeError: 'dict' object has no attribute 'read'

    (达达) #2

    训练好之后把训练模块和预测模块之间也断开,模型读取模块连预测模块。


    (wygwsg) #3

    我的意思是训练好的模型还能读取出来继续训练么?而不是预测,预测我知道。听你们客服说是可以中间接着训练的