本文選題：量化交易 + 金融數(shù)據(jù)處理工具　； 參考：《電子科技大學》2016年碩士論文

【摘要】：隨著社會經(jīng)濟和互聯(lián)網(wǎng)的發(fā)展,中國金融市場迎來了空前的繁榮,投資者在面對機遇的同時,也伴隨著諸多的挑戰(zhàn)和風險。千萬級的股民數(shù)量,2500多支A股以及便捷的股票交易方式使得每個交易日產(chǎn)生的交易數(shù)據(jù)都很龐大,而且每日與金融相關(guān)的新聞資訊也在網(wǎng)絡(luò)上不斷更新,股民使用原始的投資手段已經(jīng)難以應(yīng)付如此大量的數(shù)據(jù)信息。因此,結(jié)合了數(shù)理統(tǒng)計和計算機技術(shù)的量化交易對解決上述問題具有重要的意義和價值,引起了研究人員廣泛的關(guān)注。本文構(gòu)建了一個提供給投資者進行算法研究的面向量化交易的金融數(shù)據(jù)處理平臺,旨在提供給用戶有效的金融數(shù)據(jù)處理工具,并在該平臺基礎(chǔ)之上實現(xiàn)了三個可行的策略思路供算法研究員參考。本文首先對量化交易的國內(nèi)外發(fā)展現(xiàn)狀進行了總結(jié),并對現(xiàn)有的量化交易手段進行了研究和分析,研究了時間序列分析法以及文本分析法在量化交易中的應(yīng)用。然后針對金融數(shù)據(jù)處理平臺的實際需求進行了詳細的需求分析,據(jù)此設(shè)計出了系統(tǒng)的整體架構(gòu),分解出了輔助功能模塊和算法實體模塊,分別對其關(guān)鍵功能進行詳細的設(shè)計。最后實現(xiàn)了具有多個工具的金融數(shù)據(jù)處理平臺,本文的工作量主要在以下四個方面:(1)在輔助功能模塊中實現(xiàn)了四種金融數(shù)據(jù)處理工具。一是使用網(wǎng)絡(luò)爬蟲獲取金融相關(guān)的新聞數(shù)據(jù);二是使用Node.js的Addons技術(shù)改進了股票交易數(shù)據(jù)獲取平臺;三是實現(xiàn)了能在任何歷史時刻進行交易模擬的程序;四是基于eCharts.js技術(shù)實現(xiàn)了平臺的可視化分析與呈現(xiàn)。(2)在基于文本處理的策略模塊里提出了基于TF-IDF的樸素貝葉斯模型的新聞情感傾向預(yù)測;并使用情感詞典來量化股評,將結(jié)果作為量化擇時特征數(shù)據(jù)的一部分。(3)在量化選股模塊里提出了基于多項式線性回歸模型的多因子策略來實現(xiàn)量化選股,該策略根據(jù)歷史股票交易數(shù)據(jù)、基本面數(shù)據(jù)以及衡量系統(tǒng)風險的?值來推薦股票組合。(4)在量化擇時模塊里,本文從情緒指標、市場前期走勢、經(jīng)濟指標、貨幣環(huán)境這四個方面提取出數(shù)據(jù)特征,并使用支持向量機作為訓練模型。
[Abstract]:With the development of social economy and Internet, China's financial market is facing unprecedented prosperity. Investors face opportunities, but also with a lot of challenges and risks. With more than 2,500 A-shares and convenient stock trading methods, the number of investors at the level of 10 million makes the transaction data generated on each trading day very large, and daily news information related to finance is constantly updated on the Internet. It is difficult for investors to deal with such a large amount of data by using primitive investment methods. Therefore, the combination of mathematical statistics and computer technology is of great significance and value in solving the above problems, and has attracted extensive attention of researchers. In this paper, we construct a financial data processing platform for quantitative transactions, which is designed to provide users with effective financial data processing tools. On the basis of the platform, three feasible strategic ideas are implemented for the reference of the algorithm researcher. Firstly, this paper summarizes the development of quantitative trading at home and abroad, studies and analyzes the existing quantitative trading methods, and studies the application of time series analysis and text analysis in quantitative transactions. Based on the detailed requirement analysis of the financial data processing platform, the overall architecture of the system is designed, the auxiliary function module and the algorithm entity module are decomposed, and the key functions are designed in detail. Finally, a financial data processing platform with multiple tools is implemented. The workload of this paper is mainly in the following four aspects: (1) four kinds of financial data processing tools are implemented in the auxiliary function module. First, using web crawler to obtain financial news data; second, using Node.js Addons technology to improve the stock trading data acquisition platform; third, realizing the procedure of trading simulation at any historical time; Fourthly, it realizes the visual analysis and presentation of the platform based on eCharts.js technology. (2) in the strategy module based on text processing, the prediction of news emotion tendency based on the naive Bayesian model of TF-IDF is put forward, and the emotion dictionary is used to quantify the stock review. The results are taken as part of the quantitative timing feature data. (3) in the quantitative stock selection module, a multi-factor strategy based on polynomial linear regression model is proposed to realize quantitative stock selection, which is based on the historical stock trading data. Fundamental data and measures of systemic risk? (4) in the quantitative timing module, this paper extracts the data features from four aspects: emotion index, early market trend, economic index and monetary environment, and uses support vector machine as the training model.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP311.52

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