Aberdeen Asset Management Implements Machine Learning–Based Portfolio Allocation Models in the Cloud

为了优化其投资组合分配策略，Aberdeen需要创建模型投资组合，其中各个资产类别如股票，商品，债券和财产，与基准相比超重或体重不足。这些决定部分基于与影响市场影响市场的因素的行为及其对未来资产绩效的影响的复杂关系模式部分。Aberdeen希望应用机器学习算法来表征这些关系，了解他们的模式，并根据它们产生交易决策。

Aberdeen分析师需要使用可用的市场数据训练和反馈机器学习算法。认识到他们所拥有的数据越多，他们必须支持其结果的证据越多，集团希望使用市场数据延伸超过15年。金宝app对本地PC的多维问题的较大的数据响起，对于本地PC来说太慢，它们需要使用计算群集加速进程。

Abderdeen used MATLAB, Parallel Computing Toolbox™, and MATLAB Parallel Server to implement machine learning algorithms for asset allocation and run them in the Microsoft Azure cloud.

在Matlab，Llorente-Cano和他的团队工作了一套分类模型。每个都基于来自统计和机器学习工具箱™和深度学习工具箱™的不同机器学习算法，包括神经网络，决策树和支持向量机（SVM）。金宝app

他们使用货币政策，企业利润，利率和隐含波动等因素培训了模型。他使用DataFeed Toolbox™访问市场数据。

The team backtested the trained models on more than 15 years of historical data. The tests, which are performed repeatedly as new methods are explored and new data becomes available, took up to a full day to complete.

要加快此过程，Aberdeen的james Mann，Solution Architect解决方案架构师，在桌面上使用并行计算工具箱进行了平行实现，然后使用MATLAB并行服务器在具有80名工人的现场计算机集群上运行并行执行。

稍后，Mann将模型重新部署到Microsoft Azure虚拟机（VM）上运行的相同工作人员。他写了一个脚本，允许Matlab用户启动云中的VM，其中MatlabParket Server提供对工人的机器学习算法。完成后，用户运行另一个脚本以关闭VM。

Llorente-Cano继续改进资产分配的机器学习模型。他目前正在使用MATLAB基于生态物理学启发的变更点分析方法以及全局优化工具箱中的全局优化方法制定交易​​策略。

• Portfolio performance goals supported.“我们在利用Matlab机器学习算法开发的资产分配过程中提供了许多投资组合，”Llorente-Cano说。“这些算法有助于我们确定组合是否将超重或超重与我们的基准相比。”
• 处理时间从24小时切割到3。“当我们开始使用Matlab分配计算服务器时，我们开始在Azure Cloud上运行时，我们的处理时间从24小时到3个。”Notes Mann。“因为作业调度程序集成到MATLAB中，因此只需通过打开池并使用”并行计算即可利用并行计算议案循环。“
• 结果用多种机器学习技术确认。“我们认为，学习的不同方法带来不同类型的知识，”Llorente-Cano说。“与MATLAB，我们向神经网络，SVM和分类树提出了相同的数据，当这些不同的型号来到同样的交易决定时，它给我们带来了很大的信心。”

重要信息

仅限专业投资者 - 不适合零售投资者或顾问使用

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