量化交易策略的研究

重庆大学硕士学位论文
中文摘要
摘
要
在不断发展的证券市场上，如何去投资已经成为人们讨论的关键问题，而如 何从众多品种中选出合适的品种，如何选择恰当的时机进行买卖，如何在适当的 位置退出市场以实现收益最大化或者亏损最小化，如何在不同的市场行情下选择 不同的交易策略，如何从大堆历史数据中挖掘出规律，如何去有效地组合交易品 种或者交易策略。这些问题已经成为传统的主观交易无法回答的问题，这个时候 量化交易的思想开始闪现在众多投资者的脑海，投资者可以借助计算机实现自己 的想法，确实如此，就是计算机使得投资者变得更加理性，收益变得更加稳定。 本文构建了两个交易策略，一个是简单地构建了一个趋势交易策略，然后用 改进后的遗传算法去寻求最优的交易策略，对最优的交易策略进行跟踪得到稳定 可观的收益和较小的回撤。另外一个交易策略是基于统计的方法进行跨期套利， 利用统计学中的 GARCH 模型来发现套利的时机。本文选取的期货品种是股指期 货主力合约和其他合约，原因在于对股指期货相对较熟悉，而且股指期货相当活 跃，可以为市场带来很大的流动性。 本文的创新之处在于： （1）对遗传算法进行了适当的改进，对交叉算子中的 交叉概率，以及变异算子中的变异概率采取了动态变化的思想，这样做合情合理， 因为优秀的个体确实应该能有较大的概率进入下一代，相对较差的个体应该具有 很大的变异概率，使得个体（染色体）能够朝最优方向进化； （2）构建的交易策 略为完整的一个交易过程，而不是简单地选择几个技术指标，从品种的选择来看， 突破了不可以做空和 T+1 的限制条件，采用股指期货主力合约，而不是股票。利 用技术指标 CCI 和 MACD 进行入场， 利用 K 线进行过滤， 最后采用了动态止盈和 固定止损的出场方式，保证了收益的最大化和回撤的可控性；本文遗传算法的适 应度函数由收益率和最大历史回撤率组成的二维向量，当对回撤率设置不同的值 将会体现出投资者对风险的偏好程度，这样就克服了采用单一衡量因子的弊端， 但是衡量一个交易策略的优劣的因素有很多，这也是本文下一步需要改进的地方 之一； （3）构建套利交易策略的时候，不是利用历史成交数据计算出套利区间， 然后发现超出套利区间的时候进行套利，而是利用统计模型来捕捉套利机会，这 样打破了传统套利思想要求价差满足正态分布的局限性。而且最后的实证结果显 示本文构建的套利交易策略可以取得可观的收益。 关键词：趋势投资策略，自适应遗传算法，套利，协整，GARCH 模型
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重庆大学硕士学位论文
英文摘要
ABSTRACT
With the growing in the stock market, how to invest has become a key issue of discussion, how to choose the suitable varieties from a number of futures varieties , how to choose the right time to buy or sell, how to exit the market at the right position in order to maximize profits or minimize losses, how to choose a different trading strategies in different market conditions, how to dig out from piles of historical data so that we could find the regular pattern, how to combine different varieties or investment strategy. These problems have become impossible for the traditional investors. It is this time quantitative trading began flashed in the minds of many investors, investors can realize their ideas by means of a computer, Indeed, the computer let investors become more rational, could get more stable earnings. In this paper ,author construct two trading strategies, one is a trend trading strategy, our perpose is seeking the optimal trading strategies with improved genetic algorithm, the optimal trading strategy could get stable gains and smaller retracement. Another is the arbitrage strategy which is based on the use of GARCH models to find arbitrage opportunities. This paper selected stock index futures and other contracts, due to the relatively familiar with the stock index futures and stock index futures is active. The innovation of this paper is:(1)the paper adopt improved genetic algorithm, crossover probability and mutation probability took the idea of dynamic change,which is proved irrational.because of outstanding individuals should have a greater probability into the next generation, the weak individual should have relatively small variation probability.(2)the paper constructed a complete transaction process, rather than simply selecting several technical indicators,and consider the short lots and T + 0 conditions.The stock index futures contracts, rather than stocks,is considered. Firstly,the paper adopt CCI and MACD to conduct admission strategy.Secondly,the paper use K-line to filter.Finally the strategy has fixed loss percent and dynamic profit percent to ensure that the profit realize maximization and retracement is small as soon as possible; genetic algorithm’s fitness function use a two-dimensional vector-profit and history retracement,the value of retracement will reflect investors' risk preferences.However,a strategy should consider more aspects,rather than only two aspects,which is task of next study.(3)Construction of arbitrage strategy use statistical models to capture arbitrage
By Liu Manzai Supervised by Ass. Prof. Huang Guanghui Specialty: Probability and Statistics
College of Mathematic and Statistics of Chongqing University, Chongqing, China April, 2015
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重庆大学硕士学位论文
英文摘要
opportunities, instead of using historical transaction data to calculate arbitrage interval. The results show that strategy can capture a very effective profit opportunities. Key words: Trend Investment Strategy, Adaptive Genetic Algorithm, Arbitrage, Cointegration, GARCH Model
1.1 研究背景和意义 ........................................................................................................................ 1 1.2 国内外研究现状 ........................................................................................................................ 2 1.3 论文的基本结构和内容 ............................................................................................................ 2 1.4 可能的创新点 ............................................................................................................................ 3
量化交易策略的研究
重庆大学硕士学位论文
（学术学位）
学生姓名：刘满在 指导教师：黄光辉 专 副教授
业：概率论与数理统计 学
学科门类：理
重庆大学数学与统计学院
二 O 一五年四月
Research on the Quantitative Trading Strategies
A Thesis Submitted to Chongqing University in Partial Fulfillment of the Requirement for the Master’s Degree of Science
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重庆大学硕士学位论文
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中文摘要..........................................................................................................................................I 英文摘要........................................................................................................................................ II 1 绪 论 ...................................................................................................................................... 1