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摘要翻译：
机器学习在自动化和控制非线性、复杂系统方面的潜力已经很好地确立了。这些技术在投资领域，特别是在股票投资组合的管理方面，一直具有潜在的应用前景。在本文中，通过分析潜在的简单交易策略来研究这种利用的机会，然后可以将这些策略网格化，供机器学习系统在两者之间切换。本文研究的是这些策略的适用性，而不是应用。为了实现这一点，每个交易系统的基本假设被探索，并创建数据，以便评估这些系统的效率时，交易的数据与基本模式，他们期望。这些策略是根据买入并持有策略来测试的，以确定交易行为是否实际上产生了任何有价值的结果，或者只是基础价格的一面。然后，这些结果被用来根据期望的回报或期望的风险产生目标回报，因为这两者都是投资组合管理行业所需要的。结果表明，在上述行业中有一个非常可行的开发机会，策略在狭隘的假设下表现良好，智能系统将它们结合在一起，无需假设。
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英文标题：
《Suitability of using technical indicators as potential strategies within
intelligent trading systems》
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作者：
Evan Hurwitz and Tshilidzi Marwala
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最新提交年份：
2011
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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英文摘要：
The potential of machine learning to automate and control nonlinear, complex systems is well established. These same techniques have always presented potential for use in the investment arena, specifically for the managing of equity portfolios. In this paper, the opportunity for such exploitation is investigated through analysis of potential simple trading strategies that can then be meshed together for the machine learning system to switch between. It is the eligibility of these strategies that is being investigated in this paper, rather than application. In order to accomplish this, the underlying assumptions of each trading system are explored, and data is created in order to evaluate the efficacy of these systems when trading on data with the underlying patterns that they expect. The strategies are tested against a buy-and-hold strategy to determine if the act of trading has actually produced any worthwhile results, or are simply facets of the underlying prices. These results are then used to produce targeted returns based upon either a desired return or a desired risk, as both are required within the portfolio-management industry. Results show a very viable opportunity for exploitation within the aforementioned industry, with the Strategies performing well within their narrow assumptions, and the intelligent system combining them to perform without assumptions.
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PDF链接：
https://arxiv.org/pdf/1110.3383
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