【专题研究】基于DNN模型的智能选股策略

深度学习
策略分享
dnn
专题报告
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(iQuant) #1

这是本系列专题研究的第六篇:基于DNN模型的深度学习智能选股策略。本文简单介绍了和DNN相关的原理,并举了一个实例,具体展示了如何应用以及应用的结果。


$$目录$$
1、DNN原理介绍
 1.1 神经元
 1.2 DNN
 1.3 反向传播

2、实例:DNN模型选股
 2.1 策略步骤和模型参数
 2.2 回测结果


1. DNN原理介绍

1.1 神经元

神经网络的每个单元结构如下:

%E5%9B%BE1
             图1.神经元结构

其对应公式如下:
$$h_{W,b}(x)=f(W^Tx)=f(\sum_{i=1}^3w_ix_i+b)$$
这相当于进行了两步:

  • 先计算各项输入的加权和:$$\sum=w_1x_1+w_2x_2+w_3x_3+b$$
  • 然后使用激活函数,将和作为输入计算得到输出结果:$$a=f(\sum)$$, f即为激活函数.

神经网络中常用到的激活函数如下表所示:

%E8%A1%A81
$$表1.激活函数特点对比$$

上述激活函数的函数图形如下图所示:

%E5%9B%BE2
                       图2. 激活函数图像

1.2 DNN

DNN是一个包含输入层,输出层和多个隐藏层的神经网络结构,每一层又包含多个1.1中所述的神经元。其基本结构如下图所示:

      %E5%9B%BE3
$$图3. DNN网络结构$$

如上图所示,黄色方块标记的为输入层,最后的y为输出层,中间各种颜色的圆形层为其隐藏层,DNN中的层与层之间是属于完全连接的结构,即任意层的神经元与它相邻层的所有神经元都是相互连接的。(这是DNN与CNN的主要区别,DNN是全连接的而CNN的局部连接的,如果对CNN感兴趣,可以前往平台的另一篇专题研究:【专题研究】基于一维CNN模型的智能选股策略)

1.3 反向传播

反向传播算法在 【专题研究】基于一维CNN模型的智能选股策略 中也有介绍,一句话简单概括就是: 前向传递输入信号直至输出产生误差,反向传播误差信息更新权重矩阵

DNN往往含有多个隐藏层,这里我们看一个带隐藏层的神经网络示意图:

%E5%9B%BE4
                    图4. 反向传播示意图

其中小女孩代表隐藏层节点,小黄帽代表输出层节点,小女孩左侧接受输入信号,经过隐层节点产生输出结果,小蓝猫代表了误差,指导参数往更优的方向调整。由于小蓝猫可以直接将误差反馈给小黄帽,所以与小黄帽直接相连的左侧参数矩阵可以直接通过误差进行参数优化(实纵线);而与小女孩直接相连的左侧参数矩阵由于不能得到小蓝猫的直接反馈而不能直接被优化(虚棕线)。但由于反向传播算法使得小蓝猫的反馈可以被传递到小女孩那进而产生间接误差,所以与小女孩直接相连的左侧权重矩阵可以通过间接误差得到权重更新,迭代几轮,误差会降低到最小。

反向传播主要由梯度下降+链式求导法则来实现,具体数学公式可以参考:机器学习:一步步教你理解反向传播方法

2. 实例:DNN模型选股

2.1 策略步骤和模型参数

%E5%9B%BE6
                   图5. DNN选股策略步骤

如图所示,DNN模型选股实例中包含下列步骤:

  • 数据获取:A股所有股票,2010-2015年数据用作训练,2016-2019年数据用作测试

  • 特征提取:选择了7个因子进行计算作为特征

  • 数据标注:计算未来5日的收益作为标注

  • 数据处理:进行缺失值处理;去掉特征异常的股票,比如某个特征值高于99.5%或低于0.5%的;标准化处理,去除特征量纲/数量级差异的影响。

  • 窗口滚动:窗口大小为1

  • 建立模型:建立一个简单的两个全连接层两个dropout层的DNN网络

  • 训练和测试:分别用训练集数据和测试集数据对模型进行训练和测试。

  • 模型评价:进行策略回测并根据回测结果对模型进行评价。

DNN模型参数如下:

  • 输入层:选用了7个因子,窗口大小为1,因此输入层形状为一维,大小为7

  • 全连接层:共有3个全连接层。前两个为隐藏层,输出空间维度分别为256和128,这个数字可以根据需要进行改动,数字越大,模型越复杂。根据表1,这里选择relu激活函数。权重使用glorot_uniform初始化方法,偏置向量使用Zeros初始化方法。最后一个全连接层为输出层,因此选择linear激活函数,输出维度为1,其他设置不变。

  • dropout层:dropout将在训练过程中每次更新参数时,按一定概率(即rate参数)随机断开输入神经元,用于防止过拟合。这里rate参数设为0.1。

  • 训练次数率 :epochs值为5,共训练5轮,以mse作为评估指标

2.2 回测结果

模型回测结果如下所示:


$$图7.回测结果$$

从图中可以看到,相比于基准收益,DNN模型有着非常突出的表现。所以,我们认为将DNN深度神经网络应用于资本市场因子选股是很有前景的。在本次的策略中,我们提取了7个因子,构建了两层的DNN模型,因子的选择提取,模型的深度和具体的模型参数都还有很大的调整空间,欢迎大家继续尝试探索。

克隆策略

策略简介

因子:样例因子(7个)

因子是否标准化:是

标注:未来5日收益(不做离散化)

算法:DNN

类型:回归问题

训练集:10-15年

测试集:16-19年

选股依据:根据预测值降序排序买入

持股数:30

持仓天数:5

模型结构

输入层 7 - 因子数量

全连接层 256 激活函数为relu

dropout 0.1

全连接层 128 激活函数为relu

全连接层 1 激活函数为linear - 预测输出

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实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n 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    In [2]:
    # 本代码由可视化策略环境自动生成 2019年7月17日 17:22
    # 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):
        # 示例代码如下。在这里编写您的代码
        pred_label = input_1.read_pickle()
        df = input_2.read_df()
        df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
        df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])
        return Outputs(data_1=DataSource.write_df(df), 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
    
    # 回测引擎:初始化函数,只执行一次
    def m19_initialize_bigquant_run(context):
        # 加载预测数据
        context.ranker_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))
        # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
        # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
        stock_count = 20
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.2
        context.options['hold_days'] = 5
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.perf_tracker.position_tracker.positions.items()}
    
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            # print('rank order for sell %s' % instruments)
            for instrument in instruments:
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                if cash_for_sell <= 0:
                    break
    
        # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        buy_cash_weights = context.stock_weights
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        for i, instrument in enumerate(buy_instruments):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            if cash > 0:
                context.order_value(context.symbol(instrument), cash)
    
    # 回测引擎:准备数据,只执行一次
    def m19_prepare_bigquant_run(context):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2015-12-31',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.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/data_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/big_expr.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)
    shift(close, -5) / shift(open, -1)-1
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 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=False
    )
    
    m13 = M.standardlize.v8(
        input_1=m2.data,
        columns_input='label',
        m_cached=False
    )
    
    m3 = M.input_features.v1(
        features="""close_0/mean(close_0,5)
    close_0/mean(close_0,10)
    close_0/mean(close_0,20)
    close_0/open_0
    open_0/mean(close_0,5)
    open_0/mean(close_0,10)
    open_0/mean(close_0,20)""",
        m_cached=False
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m14 = M.standardlize.v8(
        input_1=m16.data,
        input_2=m3.data,
        columns_input='[]'
    )
    
    m7 = M.join.v3(
        data1=m13.data,
        data2=m14.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m26 = M.dl_convert_to_bin.v2(
        input_data=m7.data,
        features=m3.data,
        window_size=1,
        feature_clip=5,
        flatten=True,
        window_along_col='instrument'
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2016-01-01'),
        end_date=T.live_run_param('trading_date', '2019-04-20'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m25 = M.standardlize.v8(
        input_1=m18.data,
        input_2=m3.data,
        columns_input='[]'
    )
    
    m27 = M.dl_convert_to_bin.v2(
        input_data=m25.data,
        features=m3.data,
        window_size=1,
        feature_clip=5,
        flatten=True,
        window_along_col='instrument'
    )
    
    m6 = M.dl_layer_input.v1(
        shape='7',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m8 = M.dl_layer_dense.v1(
        inputs=m6.data,
        units=256,
        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=''
    )
    
    m21 = M.dl_layer_dropout.v1(
        inputs=m8.data,
        rate=0.1,
        noise_shape='',
        name=''
    )
    
    m20 = M.dl_layer_dense.v1(
        inputs=m21.data,
        units=128,
        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=''
    )
    
    m22 = M.dl_layer_dropout.v1(
        inputs=m20.data,
        rate=0.1,
        noise_shape='',
        name=''
    )
    
    m23 = M.dl_layer_dense.v1(
        inputs=m22.data,
        units=1,
        activation='linear',
        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=''
    )
    
    m4 = M.dl_model_init.v1(
        inputs=m6.data,
        outputs=m23.data
    )
    
    m5 = M.dl_model_train.v1(
        input_model=m4.data,
        training_data=m26.data,
        optimizer='Adam',
        loss='mean_squared_error',
        metrics='mse',
        batch_size=1024,
        epochs=5,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m11 = M.dl_model_predict.v1(
        trained_model=m5.data,
        input_data=m27.data,
        batch_size=1024,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m24 = M.cached.v3(
        input_1=m11.data,
        input_2=m18.data,
        run=m24_run_bigquant_run,
        post_run=m24_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m24.data_1,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_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='后复权',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    
    Epoch 1/5
     - 84s - loss: 0.9901 - mean_squared_error: 0.9901
    Epoch 2/5
     - 85s - loss: 0.9891 - mean_squared_error: 0.9891
    Epoch 3/5
     - 95s - loss: 0.9889 - mean_squared_error: 0.9889
    Epoch 4/5
     - 83s - loss: 0.9886 - mean_squared_error: 0.9886
    Epoch 5/5
     - 86s - loss: 0.9885 - mean_squared_error: 0.9885
    
    DataSource(80bec2c1c82e484da700d5e27e95fa50T, v3)
    
    • 收益率122.94%
    • 年化收益率28.61%
    • 基准收益率10.44%
    • 阿尔法0.25
    • 贝塔0.74
    • 夏普比率0.94
    • 胜率0.58
    • 盈亏比0.94
    • 收益波动率27.58%
    • 信息比率0.06
    • 最大回撤20.37%
    bigcharts-data-start/{"__id":"bigchart-cf7a8d91609c48c6b04a86a717f35e80","__type":"tabs"}/bigcharts-data-end

    (galaxyery) #4

    Using TensorFlow backend.

    TypeError Traceback (most recent call last)
    in ()
    253
    254 m14 = M.standardlize.v8(
    –> 255 columns_input=’[]’
    256 )
    257

    TypeError: bigquant_run() takes at least 1 positional argument (0 given)

    这是什么错误?


    (iQuant) #5

    您好,应该是由于我们昨天对平台的模块进行了更新,所以导致了这个问题。您现在可以重新复制一下这个策略再运行,应该就可以了


    (oversky2003) #6

    刚刚克隆了,也报这个错:

    [2019-07-19 15:32:25.797583] INFO: bigquant: standardlize.v8 开始运行…
    [2019-07-19 15:32:26.588028] ERROR: bigquant: module name: standardlize, module version: v8, trackeback: Traceback (most recent call last):
    TypeError: bigquant_run() takes at least 1 positional argument (0 given)

    TypeError Traceback (most recent call last)
    in ()
    112 input_1=m2.data,
    113 columns_input=‘label’,
    –> 114 m_cached=False
    115 )
    116

    TypeError: bigquant_run() takes at least 1 positional argument (0 given)


    (iQuant) #7

    您好,我这边直接克隆策略运行没有报错,如果您还能复现这个问题的话可以把整个策略克隆上来我们再看一下