發(fā)布時(shí)間：2018-07-29 18:37

【摘要】：多年來(lái),在金融市場(chǎng)的股票投資方面,人們一直都希望能掌握其背后運(yùn)行的規(guī)律,并進(jìn)行分析與預(yù)測(cè)。各國(guó)投資專家也通過(guò)使用不同的投資分析方法,利用海量的股票數(shù)據(jù)進(jìn)行數(shù)據(jù)挖掘,以求尋找出股市背后潛在的運(yùn)行規(guī)則和股票交易的規(guī)律,實(shí)現(xiàn)對(duì)未來(lái)的股市發(fā)展變化進(jìn)行預(yù)測(cè),以達(dá)到收益最大化的目的。本文的主要研究?jī)?nèi)容是上市公司的股票數(shù)據(jù)以及公司股價(jià)的變化情況。根據(jù)公司在研究周期內(nèi)股價(jià)變化水平,計(jì)算得到特征值,設(shè)計(jì)了一個(gè)優(yōu)化版本的k-最近鄰算法,然后建立上升趨勢(shì)體系模型,預(yù)測(cè)上市公司的股價(jià)走勢(shì)類型,選擇適合自己風(fēng)險(xiǎn)類型的上市公司進(jìn)行投資。隨著大數(shù)據(jù)相關(guān)技術(shù)的不斷成熟,在處理大規(guī)模股票數(shù)據(jù)集的平臺(tái)選擇上,本文主要考慮的是內(nèi)存消耗和數(shù)據(jù)運(yùn)算效率方面,采用了大數(shù)據(jù)Hadoop平臺(tái)的HDFS分布式文件系統(tǒng)以及更加高效的MapReduce分布式計(jì)算框架,使得整個(gè)數(shù)據(jù)集的ETL流程能高效率便捷的運(yùn)行。機(jī)器學(xué)習(xí)也是本文的一個(gè)核心問(wèn)題,在對(duì)KNN算法進(jìn)行深入研究后,在此基礎(chǔ)上提出了面向大數(shù)據(jù)模式識(shí)別算法。此外,本文提出了三類不同特征集,即分鐘價(jià)格特征、K線特征、股本特征。通過(guò)在大量的真實(shí)股票數(shù)據(jù)上的實(shí)驗(yàn)表明,各類特征集對(duì)預(yù)測(cè)股票價(jià)格走勢(shì)均是有效的,在同一類特征集上面向大數(shù)據(jù)模式識(shí)別算法得到的預(yù)測(cè)結(jié)果優(yōu)于k-最近鄰算法,而在不同類的特征集上,股本特征集預(yù)測(cè)結(jié)果的準(zhǔn)確率比分鐘價(jià)格特征集和K線特征集有了較大幅度的提高。本文的研究為在大量的股票標(biāo)的中選擇合適的交易對(duì)象提供了一種有效的方法。
[Abstract]:Over the years, in the stock investment of the financial market, people have always wanted to be able to grasp the rules behind the stock market and carry out analysis and prediction. By using different investment analysis methods, various investment experts use a large amount of stock data to excavate the data in order to find out the underlying operating rules and stock trading behind the stock market. The main research content of this paper is the stock data of the listed company and the change of the stock price of the company. According to the company's stock price change level in the study cycle, we have calculated the eigenvalue of the stock price, and designed an optimized version of the k- nearest neighbor. And then we establish an uptrend system model, predict the type of stock price trend of listed companies, select the listed companies suitable for their own risk types, and invest in the listed companies which are suitable for their own risk types. Using the HDFS distributed file system of large data Hadoop platform and the more efficient MapReduce distributed computing framework, the ETL process of the whole data set can run efficiently and conveniently. Machine learning is also a core issue of this paper. After the in-depth study of the KNN algorithm, a large data model is proposed on this basis. In addition, three kinds of different feature sets, namely, minute price features, K-line features and equity characteristics, are proposed in this paper. Through experiments on a large number of real stock data, it is shown that all types of feature sets are effective in predicting stock price trends, and the prediction results obtained from the same type of feature set are superior to the large data pattern recognition algorithm. On the k- nearest neighbor algorithm, on the feature set of different classes, the accuracy of the equity feature set prediction result is greatly improved than the minute price feature set and the K-line feature set. This study provides an effective method for selecting appropriate trading objects in a large number of stock markers.
【學(xué)位授予單位】：天津工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP181

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