英文标题：
《Evaluating the Effectiveness of Common Technical Trading Models》
---
作者：
Joseph Attia
---
最新提交年份：
2019
---
英文摘要：
  How effective are the most common trading models? The answer may help investors realize upsides to using each model, act as a segue for investors into more complex financial analysis and machine learning, and to increase financial literacy amongst students. Creating original versions of popular models, like linear regression, K-Nearest Neighbor, and moving average crossovers, we can test how each model performs on the most popular stocks and largest indexes. With the results for each, we can compare the models, and understand which model reliably increases performance. The trials showed that while all three models reduced losses on stocks with strong overall downward trends, the two machine learning models did not work as well to increase profits. Moving averages crossovers outperformed a continuous investment every time, although did result in a more volatile investment as well. Furthermore, once finished creating the program that implements moving average crossover, what are the optimal periods to use? A massive test consisting of 169,880 trials, showed the best periods to use to increase investment performance (5,10) and to decrease volatility (33,44). In addition, the data showed numerous trends such as a smaller short SMA period is accompanied by higher performance. Plotting volatility against performance shows that the high risk, high reward saying holds true and shows that for investments, as the volatility increases so does its performance.
---
中文摘要：
最常见的交易模式有多有效？答案可能有助于投资者认识到使用每种模型的好处，为投资者提供进入更复杂金融分析和机器学习的机会，并提高学生的金融素养。创建流行模型的原始版本，如线性回归、K-最近邻和移动平均交叉，我们可以测试每个模型在最受欢迎的股票和最大指数上的表现。根据每个模型的结果，我们可以比较这些模型，并了解哪个模型可靠地提高了性能。试验表明，虽然这三种模型都减少了整体下跌趋势强劲的股票的损失，但这两种机器学习模型在增加利润方面效果不佳。移动平均线交叉点的表现每次都优于连续投资，尽管也确实导致了更不稳定的投资。此外，一旦完成创建实现移动平均交叉的程序，最佳使用时段是什么？一项由169880项试验组成的大规模测试显示了提高投资绩效（5,10）和降低波动性（33,44）的最佳时期。此外，数据显示了许多趋势，例如SMA周期越短，性能越高。将波动率与业绩进行对比表明，高风险、高回报的说法是正确的，并表明对于投资而言，随着波动率的增加，其业绩也会增加。
---
分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
--
一级分类：Computer Science        计算机科学
二级分类：Computers and Society        计算机与社会
分类描述：Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
涵盖计算机对社会的影响、计算机伦理、信息技术和公共政策、计算机的法律方面、计算机和教育。大致包括ACM学科类K.0、K.2、K.3、K.4、K.5和K.7中的材料。
--

---
PDF下载：
-->