标题： 股票交易中呈正态分布的日收益率 显示英文标题

标题： The normaly distributed daily returns in stock trading

摘要： 在这份报告中，我们讨论了一种新的定量策略，用于选择最佳的股票进行交易。 这些基本概念通常已经被金融界广泛熟知。 这里的关键在于理解1）应用于样本的标准分数和2）应用于实际生活中不同时间序列的相关因子。 这些概念是我们研究的核心。 我们将从引言部分开始。 这部分中，我们谈到了方差、协方差、相关因子、股票交易中的日收益率以及Shapiro-Wilk检验来测试时间序列的正态性。 接下来，我谈到了我的方法的核心（如果你想挑选最佳的股票进行交易，你会怎么做）。 在这份报告的结尾，我谈到了如果你想同时分析多支股票时的一个新想法。 我的所有工作都伴随着一个主要思考：预测股票走势是一种随机漫步，没有人能100%确定股票的走向。 我们所能做的，只是假装拥有一个胜率大于51%的技术。 显示英文摘要

摘要： In this report, we talked about a new quantitative strategy for choosing the optimal(s) stock(s) to trade. The basic notions are generally very known by the financial community. The key here is to understand 1) the standard score applied to a sample and 2) the correlation factor applied to different time series in real life. These notions are the core of our research. We are going to begin with the introduction section. In this part, we talked about variance, covariance, correlation factor, daily returns in stock trading and the Shapiro-Wilk test to test the normality of a time serie. Next to that, I talked about the core of my method (what do you do if you want to pick the optimal(s) stock(s) to trade). At the end of this report, I talked about a new idea if you want to analyze more than one stock at the time. All my work goes with a primary reflexion : forecasting a stock direction is a random walk and nobody can be 100 % sure where a stock is going. All we can do, is to pretend to have a technic with a win/loss ratio greater than 51 %.