基于均线及统计规律的股票投资策略

Stock investment strategy based on moving average and statistical law

股票交易中买点的选择是核心问题。投资者大多通过基本面分析判断股市行情来选择股票买卖点,但这样的交易时机选择往往有较强的主观性。基于此,从概率统计的角度对股票买点的选择进行了定量分析,并给出了一种交易策略算法。首先,通过历史交易大数据对买点的特征进行提取;其次,利用经验分布函数在统计意义上对随机买点的获利概率分布进行分析;最后,利用买点特征进行交易,以检验买点选择的质量。这里以深证中元股份价格作为实验数据,实验结果表明了提出算法的有效性。

The eternal issue in stock trading is how to choose buying points.Most investors judge the stock market through policy analysis and decide how to choose stock trading.But such choices of trading points tend to be more subjective.In this paper we quantitatively analyze the choice of stock buying points from the perspective of probability and statistics,and propose a trading strategy algorithm.Firstly,the characteristics of the buying point are extracted through historical trading big data.Secondly,the empirical distribution function is used to analyze the profit probability distribution of random buying points in a statistical sense.Finally,the buying point feature is used to trade to verify the trading strategy algorithm.Zhongyuan shares listed on the Shenzhen Stock Exchange is used as experimental data.The experimental results show that the proposed trading strategy algorithm is feasible and effective.