【专题研究】基于一维CNN模型的智能选股策略

深度学习
策略分享
cnn
专题报告
标签: #<Tag:0x00007fb00813f6b8> #<Tag:0x00007fb00813eb78> #<Tag:0x00007fb00813e768> #<Tag:0x00007fb00813e290>

(iQuant) #1

这是本系列专题研究的第四篇:基于卷积神经网络CNN的深度学习因子选股模型。卷积神经网络(Convolutional Neural Network, CNN),是计算机视觉研究和应用领域中最具影响力的模型之一。同样,如果将时间看作一个空间维度,类似于二维图像的高度或宽度,CNN也可以对时间序列处理产生令人惊喜的效果。本文首先大致介绍了CNN的原理,然后详细解释了一维CNN模型如何进行应用于时间序列并进行特征选取,最后以一个实例展示一维CNN模型在因子选股方面的应用。


$$目录$$
1、CNN原理介绍
1.1 反向传播算法
1.2 CNN图像识别原理
2、一维CNN在时间序列中的应用
2.1 一维卷积
2.2 一维池化
3、实例:CNN模型选股


1、CNN原理介绍

1.1 反向传播算法

反向传播(Backpropagation,缩写为BP)是“误差反向传播”的简称,是一种与最优化方法(如梯度下降法)结合使用的,用来训练人工神经网络的常见方法。该方法对网络中所有权重计算损失函数的梯度,这个梯度会反馈给最优化方法,用来更新权值以最小化损失函数。

一句话解释: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

以没有隐层的神经网络为例,如逻辑回归,其中小黄帽代表输出层节点,左侧接受输入信号,右侧产生输出结果,小蓝猫则代表了误差,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,同时只有一个参数矩阵和小黄帽直接相连,所以可以直接通过误差进行参数优化(实线),迭代几轮,误差会降低到最小。

PNG
$$图1:无隐层反向传播算法示意$$

1.2 CNN图像识别原理

卷积神经网络的结构模仿了眼睛的视觉神经的工作原理。对于眼睛来说,大量的视觉神经分工协作,各自负责一小部分区域的视觉图像,再将图像的各种局部特征抽象组合到高层的视觉概念,传送到大脑使人类产生视觉。卷积神经网络也是类似,它包含了至少一层卷积层,由多个卷积核对图像的局部区域进行特征提取,最后进行合成。

以经典的LeNet-5模型为例:

PNG

$$图2:LeNet-5卷积神经网络模型$$

原始输入数据(图1中的input)为二维图像,横轴和纵轴分别是图像的高度和宽度的像素点,为了识别该图像,模型依次完成以下步骤:

  • 第一层卷积层(图1中的conv1)进行卷积运算。该层由若干卷积核组成,每个卷积核的参数都是通过反向传播算法优化得到的。卷积核的目的是通过扫描整张图片提取不同特征,第一层卷积层可能只能提取一些低级的特征如边缘、线条和角等层级。在卷积层之后都会使用非线性激活函数(如RELU,tanh 等)对特征进行非线性变换。
  • 第一层池化层(图1中的pool1)进行池化运算。通常在卷积层之后会得到维度很大的特征,池化层可以非常有效地缩小参数矩阵的尺寸,从而减少最后全连层中的参数数量。使用池化层既可以加快计算速度也有防止过拟合的作用。一般池化层将特征切成几个区域,取其最大值或平均值,得到新的、维度较小的特征。池化层其实是在对具有高维特征的图片进行特征降维(subsample)
  • 第二层卷积层和第二层池化层(图1中的conv2 和pool2) 进行进一步的特征提取和特征降维,得到更加高层和抽象的特征。
  • 全连接层(图1中的hidden4 和full connection) 把卷积核池化得到的特征展平为一维特征,用来进行最后的训练和预测。

总结而言,卷积层进行特征提取,池化层进行特征降维以防止过拟合。CNN通过上述过程实现了图像识别:


$$图3:CNN图像识别$$
通过第一层卷积识别边缘等低级特征,池化后通过第二层卷积识别眼睛、鼻子等小区域器官这样的中级特征,池化,最后通过第三层卷积识别整个面容这样的高级特征,最后通过全连接层整合,识别出最终的图像。

2、一维CNN在时间序列中的应用

CNN最主要应用于计算机视觉领域,通过卷积运算,从二维图像中提取特征,最终实现优秀的图像识别功能。对于这样一种优秀的算法,我们自然会思考如何将其运用到时间序列分析中。很容易想到,有两种思路:

  • 将一维的时间序列二维化。考虑历史截面期,将每个样本的特征数据组织成二维形式,尝试构建“特征图片”。如总共有3000个样本,每个样本有10个特征,那就考虑5个截面期,每个样本整合5个截面得到一张“特征图片”,用时间数据作为图片标签。如此,可以得到3000个带时间特征的图片,然后运用二维CNN处理。这种方法的具体实现可参见华泰证券的研报《人工智能选股之卷积神经网络
  • 探索一维CNN在时间序列上的运用。本篇研究就是基于这个思路。如果将时间理解为图像的长或者宽,其他特征理解为一个维度,那么就可以用一维CNN进行处理。

2.1 一维卷积

一维卷积,也就是从序列中按照一定大小的窗口提取局部一维序列段(即子序列),然后与一个权重做点积,然后输出为新序列上的一个部分。以大小为5的时间窗口为例:

image
$$图4:一维CNN的一层卷积示意$$

2.2 一维池化

同二维池化一样,一维池化的目的也是为了对卷积的结果“模糊化”,归纳局部区域内的统计特征,并且通过降维避免过拟合。一维池化是从输入中提取一维序列段(即子序列), 然后输出其最大值(最大池化)或平均值(平均池化),降低一维输入的长度(子采样)。

3、实例:CNN模型选股


$$图5:CNN智能选股策略$$

如图5所示,一维CNN选股策略构建包含下列步骤:

  • 获取数据 :A股所有股票。
  • 特征和标签提取 :计算7个因子作为样本特征;计算第2日的个股收益,极值处理后分成20类作为标签。
  • 特征预处理 :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。
  • 序列窗口滚动 :窗口大小设置为5,滚动切割。
  • 搭建CNN模型 :构建一个简单的两层一维卷积神经网络预测股票价格。
  • 模型训练与预测 :使用CNN模型进行训练和预测;可以尝试多种激活函数,策略默认为relu。
  • 策略回测 :利用2010到2014年数据进行训练,预测2015到2017年的股票表现。每日买入预测排名最靠前的20只股票,至少持有2日,同时淘汰排名靠后的股票。具体而言,预测排名越靠前,分配到的资金越多且最大资金占用比例不超过20%;初始5日平均分配资金,之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)。
  • 模型评价 :查看模型回测结果。

一维CNN模型的参数如下:

  • 输入数据:输入是形状为 (samples, time, features)的三维张量,并返回类似形状的三维张量。卷积窗口是时间轴上的一维窗口(时间轴是输入张量的第二个轴)。7个因子,时间窗口为5,因此输入7*5的一个矩阵。
  • 卷积层:2层一维卷积层(Conv1D层),每层包含20 个卷积核。激活函数采用relu。同时选择“valid”参数,只进行有效卷积,对边界数据不处理。卷积核权重使用glorot_uniform初始化方法,偏置向量使用Zeros初始化方法。
  • 池化层:2层池化层。
  • 全连接层:激活函数linear。权重使用glorot_uniform初始化方法,偏置向量使用Zeros初始化方法。
  • 训练次数率:epochs值为5,共训练5轮,以mae作为评估指标。
  • 优化器和:RMSProp。
  • 损失函数:均方误差MSE。

预测结果如下:
prediction
$$图6:CNN模型预测结果$$

回测结果如下:


$$图7:CNN模型回测结果$$

可以看到,CNN的回测结果还是非常惊喜的,相比于基准收益有着非常突出的表现。所以,我们认为将CNN卷积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,我们运用了两层的一维CNN模型,具体的卷积层数、模型参数有非常大的调整空间,欢迎大家尝试。

克隆策略

    {"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-106:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-773:input_1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"-106:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-113:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-122:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-129:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-768:input_2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-778:input_2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-243:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-251:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-3895:input_2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-3907:input_2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-243:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"-122:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-141:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-113:input_data","SourceOutputPortId":"-106:data"},{"DestinationInputPortId":"-768:input_1","SourceOutputPortId":"-113:data"},{"DestinationInputPortId":"-129:input_data","SourceOutputPortId":"-122:data"},{"DestinationInputPortId":"-2431:input_2","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-778:input_1","SourceOutputPortId":"-129:data"},{"DestinationInputPortId":"-2680:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-3880:inputs","SourceOutputPortId":"-160:data"},{"DestinationInputPortId":"-1540:trained_model","SourceOutputPortId":"-1098:data"},{"DestinationInputPortId":"-2431:input_1","SourceOutputPortId":"-1540:data"},{"DestinationInputPortId":"-141:options_data","SourceOutputPortId":"-2431:data_1"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-768:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"-773:data"},{"DestinationInputPortId":"-251:input_data","SourceOutputPortId":"-778:data"},{"DestinationInputPortId":"-3895:input_1","SourceOutputPortId":"-243:data"},{"DestinationInputPortId":"-3907:input_1","SourceOutputPortId":"-251:data"},{"DestinationInputPortId":"-2712:inputs","SourceOutputPortId":"-2680:data"},{"DestinationInputPortId":"-3840:inputs","SourceOutputPortId":"-2712:data"},{"DestinationInputPortId":"-3784:inputs","SourceOutputPortId":"-3773:data"},{"DestinationInputPortId":"-3880:outputs","SourceOutputPortId":"-3784:data"},{"DestinationInputPortId":"-3872:inputs","SourceOutputPortId":"-3840:data"},{"DestinationInputPortId":"-3773:inputs","SourceOutputPortId":"-3872:data"},{"DestinationInputPortId":"-1098:input_model","SourceOutputPortId":"-3880:data"},{"DestinationInputPortId":"-1098:training_data","SourceOutputPortId":"-3895:data_1"},{"DestinationInputPortId":"-1540:input_data","SourceOutputPortId":"-3907:data_1"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2010-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2015-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":" ","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# #号开始的表示注释\n# 0. 每行一个,顺序执行,从第二个开始,可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html\n# 添加benchmark_前缀,可使用对应的benchmark数据\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_\n\n# 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)\nshift(close, -2) / shift(open, -1)-1\n\n# 极值处理:用1%和99%分位的值做clip\nclip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))\n\n# 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)\nwhere(shift(high, -1) == shift(low, -1), NaN, label)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na_label","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"cast_label_int","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"close_0/mean(close_0,5)\nclose_0/mean(close_0,10)\nclose_0/mean(close_0,20)\nclose_0/open_0\nopen_0/mean(close_0,5)\nopen_0/mean(close_0,10)\nopen_0/mean(close_0,20)","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":3,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","ModuleId":"BigQuantSpace.join.join-v3","ModuleParameters":[{"Name":"on","Value":"date,instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"how","Value":"inner","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sort","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data1","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":7,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2015-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2017-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":9,"IsPartOfPartialRun":null,"Comment":"预测数据,用于回测和模拟","CommentCollapsed":false},{"Id":"-106","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-106"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-106"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-106","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":15,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-113","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":"True","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":"-113"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-113"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-113","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":16,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-122","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-122"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-122"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-122","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":17,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-129","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":"True","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":"-129"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-129"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-129","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":18,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-141","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","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 ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 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\n # 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 context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 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\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\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":"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.ranker_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))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 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\n stock_count = 20\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.options['hold_days'] = 2\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":0.025,"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":1000000,"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":"后复权","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":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-141"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-141","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":19,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-160","ModuleId":"BigQuantSpace.dl_layer_input.dl_layer_input-v1","ModuleParameters":[{"Name":"shape","Value":"7,5","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_shape","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dtype","Value":"float32","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sparse","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-160"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-160","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":6,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1098","ModuleId":"BigQuantSpace.dl_model_train.dl_model_train-v1","ModuleParameters":[{"Name":"optimizer","Value":"RMSprop","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_optimizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"loss","Value":"mean_squared_error","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_loss","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"metrics","Value":"mae","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_size","Value":"256","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"epochs","Value":"5","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"n_gpus","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"verbose","Value":"2:每个epoch输出一行记录","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_model","NodeId":"-1098"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"training_data","NodeId":"-1098"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"validation_data","NodeId":"-1098"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1098","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1540","ModuleId":"BigQuantSpace.dl_model_predict.dl_model_predict-v1","ModuleParameters":[{"Name":"batch_size","Value":"1024","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"n_gpus","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"verbose","Value":"2:每个epoch输出一行记录","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trained_model","NodeId":"-1540"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1540"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1540","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":11,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-2431","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 # 示例代码如下。在这里编写您的代码\n pred_label = input_1.read_pickle()\n df = input_2.read_df()\n df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})\n df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])\n return Outputs(data_1=DataSource.write_df(df), 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":"-2431"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-2431"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-2431"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-2431","OutputType":null},{"Name":"data_2","NodeId":"-2431","OutputType":null},{"Name":"data_3","NodeId":"-2431","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":24,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-768","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"[]","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-768"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-768"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-768","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":14,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-773","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"['label']","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-773"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-773"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-773","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":13,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-778","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"[]","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-778"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-778"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-778","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":25,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-243","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"5","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":5,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-243"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-243"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-243","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":26,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-251","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"5","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":5,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-251"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-251"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-251","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":27,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-2680","ModuleId":"BigQuantSpace.dl_layer_conv1d.dl_layer_conv1d-v1","ModuleParameters":[{"Name":"filters","Value":"20","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_size","Value":"3","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"strides","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"padding","Value":"valid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dilation_rate","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"relu","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-2680"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-2680","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":10,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-2712","ModuleId":"BigQuantSpace.dl_layer_maxpooling1d.dl_layer_maxpooling1d-v1","ModuleParameters":[{"Name":"pool_size","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"strides","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"padding","Value":"valid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-2712"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-2712","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":12,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3773","ModuleId":"BigQuantSpace.dl_layer_globalmaxpooling1d.dl_layer_globalmaxpooling1d-v1","ModuleParameters":[{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-3773"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3773","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":28,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3784","ModuleId":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","ModuleParameters":[{"Name":"units","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"linear","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-3784"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3784","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":30,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3840","ModuleId":"BigQuantSpace.dl_layer_conv1d.dl_layer_conv1d-v1","ModuleParameters":[{"Name":"filters","Value":"20","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_size","Value":"3","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"strides","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"padding","Value":"valid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dilation_rate","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"relu","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"use_bias","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_initializer","Value":"glorot_uniform","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_initializer","Value":"Zeros","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_initializer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l1","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activity_regularizer_l2","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activity_regularizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_kernel_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"bias_constraint","Value":"None","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_bias_constraint","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-3840"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3840","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":32,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3872","ModuleId":"BigQuantSpace.dl_layer_maxpooling1d.dl_layer_maxpooling1d-v1","ModuleParameters":[{"Name":"pool_size","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"strides","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"padding","Value":"valid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-3872"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3872","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":33,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3880","ModuleId":"BigQuantSpace.dl_model_init.dl_model_init-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-3880"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"outputs","NodeId":"-3880"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3880","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":34,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3895","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 # 示例代码如下。在这里编写您的代码\n df = input_1.read_pickle()\n feature_len = len(input_2.read_pickle())\n \n \n df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))\n \n data_1 = DataSource.write_pickle(df)\n return Outputs(data_1=data_1)\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":"-3895"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-3895"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-3895"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-3895","OutputType":null},{"Name":"data_2","NodeId":"-3895","OutputType":null},{"Name":"data_3","NodeId":"-3895","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-3907","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 # 示例代码如下。在这里编写您的代码\n df = input_1.read_pickle()\n feature_len = len(input_2.read_pickle())\n \n \n df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))\n \n data_1 = DataSource.write_pickle(df)\n return Outputs(data_1=data_1)\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":"-3907"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-3907"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-3907"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-3907","OutputType":null},{"Name":"data_2","NodeId":"-3907","OutputType":null},{"Name":"data_3","NodeId":"-3907","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":8,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true}],"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'><Meta /><NodePositions><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='394,20,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='208,220,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='800,-49,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='379,435,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1074,127,200,200'/><NodePosition Node='-106' Position='547,173,200,200'/><NodePosition Node='-113' Position='548,275,200,200'/><NodePosition Node='-122' Position='1078,236,200,200'/><NodePosition Node='-129' Position='1081,327,200,200'/><NodePosition Node='-141' Position='477,955,200,200'/><NodePosition Node='-160' Position='-210,-162,200,200'/><NodePosition Node='-1098' Position='137,642,200,200'/><NodePosition Node='-1540' Position='203,733,200,200'/><NodePosition Node='-2431' Position='370.84564208984375,854.746337890625,200,200'/><NodePosition Node='-768' Position='569,357,200,200'/><NodePosition Node='-773' Position='230,329,200,200'/><NodePosition Node='-778' Position='1067,432,200,200'/><NodePosition Node='-243' Position='384,492,200,200'/><NodePosition Node='-251' Position='1061,508,200,200'/><NodePosition Node='-2680' Position='-84,-53,200,200'/><NodePosition Node='-2712' Position='-88,35,200,200'/><NodePosition Node='-3773' Position='-85,330,200,200'/><NodePosition Node='-3784' Position='-87,437,200,200'/><NodePosition Node='-3840' Position='-86,121,200,200'/><NodePosition Node='-3872' Position='-88,214,200,200'/><NodePosition Node='-3880' Position='-85,535,200,200'/><NodePosition Node='-3895' Position='385,569,200,200'/><NodePosition Node='-3907' Position='1056,590,200,200'/></NodePositions><NodeGroups /></DataV1>"},"IsDraft":true,"ParentExperimentId":null,"WebService":{"IsWebServiceExperiment":false,"Inputs":[],"Outputs":[],"Parameters":[{"Name":"交易日期","Value":"","ParameterDefinition":{"Name":"交易日期","FriendlyName":"交易日期","DefaultValue":"","ParameterType":"String","HasDefaultValue":true,"IsOptional":true,"ParameterRules":[],"HasRules":false,"MarkupType":0,"CredentialDescriptor":null}}],"WebServiceGroupId":null,"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'><Meta /><NodePositions></NodePositions><NodeGroups /></DataV1>"},"DisableNodesUpdate":false,"Category":"user","Tags":[],"IsPartialRun":true}
    In [7]:
    # 本代码由可视化策略环境自动生成 2019年5月29日 17:51
    # 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 m4_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df =  input_1.read_pickle()
        feature_len = len(input_2.read_pickle())
        
        
        df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
        
        data_1 = DataSource.write_pickle(df)
        return Outputs(data_1=data_1)
    
    # 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 m4_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 m8_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df =  input_1.read_pickle()
        feature_len = len(input_2.read_pickle())
        
        
        df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
        
        data_1 = DataSource.write_pickle(df)
        return Outputs(data_1=data_1)
    
    # 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 m8_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 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: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
    
    # 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'] = 2
    
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2015-01-01',
        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, -2) / 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']
    )
    
    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)"""
    )
    
    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=5,
        feature_clip=5,
        flatten=True,
        window_along_col='instrument'
    )
    
    m4 = M.cached.v3(
        input_1=m26.data,
        input_2=m3.data,
        run=m4_run_bigquant_run,
        post_run=m4_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        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=5,
        feature_clip=5,
        flatten=True,
        window_along_col='instrument'
    )
    
    m8 = M.cached.v3(
        input_1=m27.data,
        input_2=m3.data,
        run=m8_run_bigquant_run,
        post_run=m8_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m6 = M.dl_layer_input.v1(
        shape='7,5',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m10 = M.dl_layer_conv1d.v1(
        inputs=m6.data,
        filters=20,
        kernel_size='3',
        strides='1',
        padding='valid',
        dilation_rate=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=''
    )
    
    m12 = M.dl_layer_maxpooling1d.v1(
        inputs=m10.data,
        pool_size=1,
        padding='valid',
        name=''
    )
    
    m32 = M.dl_layer_conv1d.v1(
        inputs=m12.data,
        filters=20,
        kernel_size='3',
        strides='1',
        padding='valid',
        dilation_rate=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=''
    )
    
    m33 = M.dl_layer_maxpooling1d.v1(
        inputs=m32.data,
        pool_size=1,
        padding='valid',
        name=''
    )
    
    m28 = M.dl_layer_globalmaxpooling1d.v1(
        inputs=m33.data,
        name=''
    )
    
    m30 = M.dl_layer_dense.v1(
        inputs=m28.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=''
    )
    
    m34 = M.dl_model_init.v1(
        inputs=m6.data,
        outputs=m30.data
    )
    
    m5 = M.dl_model_train.v1(
        input_model=m34.data,
        training_data=m4.data_1,
        optimizer='RMSprop',
        loss='mean_squared_error',
        metrics='mae',
        batch_size=256,
        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=m8.data_1,
        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='',
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        initialize=m19_initialize_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
     - 78s - loss: 0.9915 - mean_absolute_error: 0.7285
    Epoch 2/5
     - 77s - loss: 0.9880 - mean_absolute_error: 0.7272
    Epoch 3/5
     - 80s - loss: 0.9873 - mean_absolute_error: 0.7270
    Epoch 4/5
     - 86s - loss: 0.9869 - mean_absolute_error: 0.7268
    Epoch 5/5
     - 80s - loss: 0.9865 - mean_absolute_error: 0.7267
    
    DataSource(38e120313f9c4728844c0ab89fc1ddbaT, v3)
    
    • 收益率265.37%
    • 年化收益率95.25%
    • 基准收益率-6.33%
    • 阿尔法0.76
    • 贝塔0.98
    • 夏普比率1.64
    • 胜率0.57
    • 盈亏比0.99
    • 收益波动率45.35%
    • 信息比率0.15
    • 最大回撤51.81%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-64bca152826a4cad9563a7720a4d65e7"}/bigcharts-data-end

    参考文献


    (asonyang) #2

    端午节有事做了。


    (sunxking) #3

    nb啊,请问下因子的选择是根据什么原理或标准来的


    (antuar) #4

    真的很牛逼啊


    (pengxm) #5

    请问为什么要把收益分成20个标签啊


    (iQuant) #6

    就是对股票未来表现分档 可以分的更多也可以更少,默认的 20类是经过尝试调优后效果比较好的结果,您也可以自己调优尝试。


    (copen) #7

    离散化以后,只有20个数值,模型不会学得太细,一定程度上可以规避过拟合。