英文标题：
《Black Magic Investigation Made Simple: Monte Carlo Simulations and
Historical Back Testing of Momentum Cross-Over Strategies Using FRACTI
Patterns》
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作者：
Jorge Faleiro, Edward Tsang
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最新提交年份：
2018
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英文摘要：
To promote economic stability, finance should be studied as a hard science, where scientific methods apply. When a trading strategy is proposed, the underlying model should be transparent and defined robustly to allow other researchers to understand and examine it thoroughly. Like any hard sciences, results must be repeatable to allow researchers to collaborate, and build upon each other\'s results. Large-scale collaboration, when applying the steps of scientific investigation, is an efficient way to leverage \"crowd science\" to accelerate research in finance. In this paper, we demonstrate how a real world problem in economics, an old problem still subject to a lot of debate, can be solved by the application of a crowd-powered, collaborative scientific computational framework, fully supporting the process of investigation dictated by the modern scientific method. This paper provides a real end-to-end example of investigation to illustrate the use of the framework. We intentionally selected an example that is self-contained, complete, simple, accessible, and of constant debate in both academia and the industry: the performance of a trading strategy used commonly in technical analysis. Claims of efficiency in technical analysis, referred derisively by some sources as \"Black Magic\", are of widespread use in mainstream media and usually met with a lot of controversy. In this paper we show that different researchers assess this strategy differently, and the subsequent debate is due more to the lack of method than purpose. Most results reported are not repeatable by other researchers. This is not satisfactory if we intend to approach finance as a hard science. To counterweight the status quo, we demonstrate what one could do by using collaborative and investigative features of contributions and leveraging the power of crowds.
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中文摘要：
为了促进经济稳定，金融应该作为一门硬科学来研究，并应用科学方法。当交易策略被提出时，基础模型应该是透明的，并且定义有力，以便其他研究人员能够彻底理解和检查它。像任何硬科学一样，研究结果必须是可重复的，以允许研究人员合作，并在彼此的结果基础上发展。在应用科学调查步骤时，大规模合作是利用“大众科学”加速金融研究的有效方式。在这篇文章中，我们展示了一个现实世界中的经济学问题，一个仍然备受争议的老问题，如何通过应用一个群体驱动、协作的科学计算框架来解决，充分支持现代科学方法所规定的调查过程。本文提供了一个真实的端到端调查示例来说明该框架的使用。我们特意选择了一个自成体系、完整、简单、易于获取且在学术界和业界都有争议的例子：技术分析中常用的交易策略的表现。技术分析中的效率主张被一些消息来源嘲笑为“黑魔法”，在主流媒体中广泛使用，通常会遇到很多争议。在本文中，我们表明，不同的研究人员对这一策略的评估不同，随后的争论更多是由于缺乏方法而非目的。其他研究人员无法重复报告的大多数结果。如果我们打算把金融作为一门硬科学来对待，这是不令人满意的。为了平衡现状，我们展示了通过使用贡献的协作和调查功能以及利用人群的力量可以做些什么。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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