摘 要

证券交易，是指证券持有者在交易平台上以一定的规则与其他证券持有者相互交易的行为。量化交易是指投资者利用计算机技术、数学与统计学相关知识，结合股票交易相关技术指标将股票交易的思想、方法建立模型并编码实现，使用计算机执行策略自动交易的现代股票交易方法。在量化交易出现前，股票交易使用人工进行交易。同人工交易相比，量化交易基于大规模数据分析，可获得较为稳定、持续的收益。相关数据显示，国外证券交易市场中70%-80%的交易都是量化交易，而国内的量化交易还出于起步阶段，未来量化交易必定是国内金融市场的一个大的发展趋势。

论文通过对证券交易的相关知识进行梳理，对量化交易的策略、流程进行分析，通过分析相关量化交易平台，并结合国内个人投资者的实际需求，设计并实现了基于量化交易的证券交易辅助系统，系统专注于交易策略模块与自动交易模块，对一些繁杂不必要的功能进行了删减，其它量化交易平台相比具有功能简洁易操作、支持实盘自动交易与微信消息通知的特点，非常适合个人投资者。本文首先介绍证券交易及量化交易的相关背景知识，对量化交易平台进行功能分析；随后对证券交易相关的技术指标和常用量化交易策略进行了详细的分析和介绍；最后使用python语言，结合其丰富的第三方金融工具库，以Django框架为基础，搭建了可提供Web交互服务的证券交易辅助系统。系统实现了策略管理、策略回测、交易管理、自动交易、交易消息提醒等实用性功能。最后，通过实际操作，以实例展示了系统的使用，课题成功实现了基于量化交易的证券交易辅助系统。

关键词：证券；技术指标；量化交易；策略回测；交易辅助系统

Abstract

Securities trading refers to the behavior of securities holders to trade with other securities holders on certain rules on the trading platform. Quantitative trading refers to the modern stock trading method in which investors use computer technology, mathematics and statistics related knowledge, combined with stock trading related technical indicators to model and encode stock trading ideas and methods, and use computer to execute strategy automatic trading. Stock trading uses manual trading before the occurrence of a quantitative transaction. Compared with manual trading, quantitative transactions are based on large-scale data analysis, which can obtain relatively stable and sustained benefits. Relevant data show that 70%-80% of transactions in foreign stock exchanges are quantitative transactions, while domestic quantitative transactions are still in their infancy, and future quantitative transactions must be a big development trend in domestic financial markets.

This thesis analyzes the relevant knowledge of securities trading, analyzes the strategies and processes of quantitative trading, and analyzes the relevant quantitative trading platform, and combines the actual needs of domestic individual investors to design and implement a securities trading auxiliary system based on quantitative trading. The system focuses on the trading strategy module and the automatic trading module, and cuts down some complicated and unnecessary functions. Other quantitative trading platforms have functional introduction and easy operation, support real-time automatic trading and WeChat message notification, which is very suitable for personal investment. Characteristics of the person. This thesis first introduces the relevant background knowledge of securities trading and quantitative trading, and analyzes the function of quantitative trading platform. Then it analyzes and introduces the technical indicators related to securities trading and the commonly used quantitative trading strategies. Finally, it uses Python language and combines its richness. The third-party financial tool library, based on the Django framework, builds a securities trading assistance system that provides Web interactive services. The system implements practical functions such as policy management, policy backtesting, transaction management, automatic trading, and transaction message reminding. Finally, through practical operation, the use of the system is demonstrated by practical examples. The system operation is simple, and the quantitative backtesting and automatic trading functions can be realized. The project successfully implements the securities trading assistant system based on quantitative trading.

Key Words：Securities; Technical indicators ;quantitative trading; strategy backtesting; transaction support system

目录

摘要………………………………………………………………………………………I

Abstract………………………………………………..………….……….……………. II

第1章 绪论………………………………………………….………….……………1

1.1 研究目的及意义………………………………………………………………….1

1.1.1 研究目的 1

1.1.2 研究意义 1

1.2 国内外研究现状 2

1.2.1 国外研究现状 2

1.2.2 国内研究现状 3

1.3 主要工作 3

1.4 论文结构 3

第2章 需求分析和总体设计…………………………………………………………..5

2.1 需求分析与可行性分析 5

2.1.1 用户需求分析 5

2.1.2 交易流程分析 5

2.1.3 可行性分析 6

2.1.4 开发环境与工具 6

2.2 总体设计 7

2.2.1 系统功能模块 7

2.2.2 平台架构 7

2.2.3 基于事件的处理机制 8

2.3 小结 8

第3章 常用技术指标和量化策略……………………………………………………..9

3.1 常用技术指标 9

3.1.1 均线 9

3.1.2 趋势指标MACD 9

3.1.3 震荡指标KDJ 10

3.1.4 跳空缺口 12

3.1.5 金叉和死叉 13

3.2 量化策略 14

3.2.1 趋势突破择时策略 14

3.2.2 多趋势突破策略融合 15

3.2.3 ATR 止盈止损风险策略 16

3.3 小结 17

第4章 证券交易辅助系统实现………………………………………………………18

4.1 数据获取、清洗及可视化 18

4.1.1 数据获取 18

4.1.2 数据清洗 18

4.1.3 数据存储 19

4.1.4 数据可视化 19

4.2 技术指标计算 21

4.3量化策略实现 21

4.4 量化策略运行机制实现 22

4.5 自动交易和消息通知功能实现 22

4.5.1 自动交易实现 22

4.5.2 微信消息通知实现 23

4.6 web交互平台搭建 23

4.6.1 web前端实现 23

4.6.2 后台搭建 24

4.7 小结 24

第5章 系统运行与测试………………………………………………………………25

5.1 策略管理及回测 25

5.2 交易管理 25

5.3 系统实用性 27

5.4 小结 27

第6章 总结与展望……………………………………………………………………28

6.1 总结 28

6.2 展望 .........................................................................................28

参考文献………………………………………………………………………………..29

致谢……………………………………………………………………………………..30