An intelligent stock trading system using comprehensive features

The aim of this study is to predict automatic trading decisions in stock markets. Comprehensive features (CF) for predicting future trend are very difficult to generate in a complex environment, especially in stock markets. According to related work, the relevant stock information can help investors formulate objects that may result in better profits. With this in mind, we present a framework of an intelligent stock trading system using comprehensive features (ISTSCF) to predict future stock trading decisions. The ISTSCF consists of stock information extraction, prediction model learning and stock trading decision. We apply three different methods to generate comprehensive features, including sentiment analysis (SA) that provides sensitive market events from stock news articles for sentiment indices (SI), technical analysis (TA) that yields effective trading rules based on trading information on the stock exchange for technical indices (TI), as well as the trend-based segmentation method (TBSM) that raises trading decisions from stock price for trading signals (TS). Experiments on the Taiwan stock market show that the results of employing comprehensive features are significantly better than traditional methods using numeric features alone (without textual sentiment features).     

Intelligent stock trading system with price trend prediction and reversal recognition using dual-module neural networks

This article presents an intelligent stock trading system that can generate timely stock trading suggestions according to the prediction of short-term trends of price movement using dual-module neural networks(dual net). Retrospective technical indicators extracted from raw price and volume time series data gathered from the market are used as independent variables for neural modeling. Both neural network modules of thedual net learn the correlation between the trends of price movement and the retrospective technical indicators by use of a modified back-propagation learning algorithm. Reinforcing the temporary correlation between the neural weights and the training patterns, dual modules of neural networks are respectively trained on a short-term and a long-term moving-window of training patterns. An adaptive reversal recognition mechanism that can self-tune thresholds for identification of the timing for buying or selling stocks has also been developed in our system. It is shown that the proposeddual net architecture generalizes better than one single-module neural network. According to the features of acceptable rate of returns and consistent quality of trading suggestions shown in the performance evaluation, an intelligent stock trading system with price trend prediction and reversal recognition can be realized using the proposed dual-module neural networks.     

Satisfying quality requirements in the design of a partition‐based,distributed stock trading system

Although quality requirements (QRs) have become a major drive in today's software development, there have been very few real‐world examples in the literature that demonstrate how to meet these requirements. This paper presents such an example. Specifically, the paper describes the design of a partition‐based distributed stock trading service system that satisfies a set of QRs related to resource utilization, performance, scalability and availability. The paper evaluates this design through detailed experiments and discusses some design alternatives and the lessons learned. Central to this design are a static load distribution strategy and a dynamic load balancing strategy. The first strategy is to achieve an initial balanced workload on the system's server cluster during the system initialization time, whereas the second strategy is to maintain this balanced workload throughout the system execution time. Together, these two strategies work in unison to ensure that the server resources are efficiently utilized; the user requests are processed with the required speed; the application is partitioned with sufficient room to scale; and the system is highly available. Copyright © 2011 John Wiley & Sons, Ltd.     

Corruptible social agents

Agent‐based virtual simulations of social systems susceptible to corruption (e.g., police agencies) require agents capable of exhibiting corruptible behaviors to achieve realistic simulations and enable the analysis of corruption as a social problem. This paper proposes a formal belief‐desire‐intention framework supported by the functional event calculus and fuzzy logic for modeling corruption based on the integrity level of social agents and the influence of corrupters on them. Corruptible social agents are endowed with beliefs, desires, intentions, and corrupt‐prone plans to achieve their desires. This paper also proposes a fuzzy logic system to define the level of impact of corruption‐related events on the degree of belief in the truth of anti‐corruption factors (e.g., the integrity of the leader of an organization). Moreover, an agent‐based model of corruption supported by the proposed belief‐desire‐intention framework and the fuzzy logic system was devised and implemented. Results obtained from agent‐based simulations are consistent with actual macro‐level patterns of corruption reported in the literature. The simulation results show that (i) the bribery rate increases as more external entities attempt to bribe agents and (ii) the more anti‐corruption factors agents believe to be true, the less prone to perpetrate acts of corruption. Copyright © 2014 John Wiley & Sons, Ltd.     

Agents in object‐oriented software engineering

Software engineers of multi‐agent systems (MASs) are faced with different concerns such as autonomy, adaptation, interaction, collaboration, learning, and mobility, which are essentially different from classical concerns addressed in object‐oriented software engineering. MAS developers, however, have relied mostly on object‐oriented design techniques and programming languages, such as Java. This often leads to a poor separation of MAS concerns and in turn to the production of MASs that are difficult to maintain and reuse. This paper discusses software engineering approaches for MASs, and presents a new method for integrating agents into object‐oriented software engineering from an early stage of design. The proposed approach encourages the separate handling of MAS concerns, and provides a disciplined scheme for their composition. Our proposal explores the benefits of aspect‐oriented software development for the incorporation of agents into object‐oriented systems. We also illustrate our aspect‐oriented approach through the Portalware multi‐agent system, a Web‐based environment for the development of e‐commerce portals. Copyright © 2004 John Wiley & Sons, Ltd.     

证券集中交易系统设计

介绍了证券交易系统的技术架构设计方法,提出了分布式证券集中交易系统的设计方案,阐述了证券交易系统设计中涉及的技术架构、业务架构、功能、性能、可靠性、可扩展性、运维和监控等方面的设计方法和原则,并列举、分析了两个根据文中方法建设的证券交易系统的应用案例。     

Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system

The increasing reliance on Computational Intelligence techniques like Artificial Neural Networks and Genetic Algorithms to formulate trading decisions have sparked off a chain of research into financial forecasting and trading trend identifications. Many research efforts focused on enhancing predictive capability and identifying turning points. Few actually presented empirical results using live data and actual technical trading rules. This paper proposed a novel RSPOP Intelligent Stock Trading System, that combines the superior predictive capability of RSPOP FNN and the use of widely accepted Moving Average and Relative Strength Indicator Trading Rules. The system is demonstrated empirically using real live stock data to achieve significantly higher Multiplicative Returns than a conventional technical rule trading system. It is able to outperform the buy-and-hold strategy and generate several folds of dollar returns over an investment horizon of four years. The Percentage of Winning Trades was increased significantly from an average of 70% to more than 92% using the system as compared to the conventional trading system; demonstrating the system’s ability to filter out erroneous trading signals generated by technical rules and to preempt any losing trades. The system is designed based on the premise that it is possible to capitalize on the swings in a stock counter’s price, without a need for predicting target prices.     

面向Agent的软件设计模式

设计模式针对一类在特定上下文中反复出现的问题给出了通用解决方案,模式的提取和重用有助于提高软件开发的效率和质量,其思想和方法已在面向对象软件工程中得到广泛应用和验证。我们认为同一问题采用不同的实现技术往往具有不同的设计形态,不同软件开发范型也有其各自不同的设计模式。作为一种新颖的软件开发范型,近年来面向Agent软件工程的研究取得了长足进展,如何提高软件Agent技术实用性,发挥其技术潜力,扩大其应用范围成为当前人们关注的焦点。本文将模式思想引入到面向Agent软件工程,针对软件Agent技术的特点,从组织结构、交互协作和个体Agent体系结构三个视点提出了一组面向Agent的软件设计模式,以支持多Agent系统的设计;并进一步给出了面向Agent设计模式的描述框架,进行了典型设计模式和应用案例的分析。     

面向对象软件度量工具的设计实现

利用软件度量工具对程序源代码的各类质量属性度量,对于提高程序质量具有重要意义。提出了一个适用于多种面向对象程序设计语言的软件度量工具的灵活软件结构,该结构的核心是以数据库为中间件。给出了该工具的结构,总结了该结构的特点和实用性,给出了以图形形式显示的分析结果和度量指标与质量特征之间关系的实验结果。     

电影英语自主学习系统的设计与实现

电影英语自主学习系统使用电影作为素材进行英语教学;根据语言教学的要求,在软件需求分析中,以电影字幕文本及其对应的时间戳数据为核心,构建其播放控制、学习流程、词汇管理、段落标记、复读、复习和测试等功能模块.在架构分析和设计中,以Java本地调用结合媒体播放器COM组件构建桌面应用;用包含DirectShow功能的守护进程通过网络套接-字为影文词典Web应用提供服务;而素材检索、知识库下载等服务则通过PHP-Web动态网站的形式提供.基于该分析设计的系统实现已经完成并进入商业运作.     

Modeling Big data‐based systems through ontological trading

One of the great challenges the information society faces is dealing with the huge amount of information generated and handled daily on the Internet. Today, progress in Big data proposals attempt to solve this problem, but there are certain limitations to information search and retrieval due basically to the large volumes handled, the heterogeneity of the information, and its dispersion among a multitude of sources. In this article, a formal framework is defined to facilitate the design and development of an environmental management information system, which works with a heterogeneous and large amount of data. Nevertheless, this framework can be applied to other information systems that work with Big data, because it does not depend on the type of data and can be utilized in other domains. The framework is based on an ontological web‐trading model (OntoTrader), which follows model‐driven engineering and ontology‐driven engineering guidelines to separate the system architecture from its implementation. The proposal is accompanied by a case study, SOLERES‐KRS, an environmental knowledge representation system designed and developed using software agents and multi‐agent systems. Copyright © 2017 John Wiley & Sons, Ltd.     

一种星载软件重构系统的设计与实现

针对航天器寿命延长与功能复杂度提升带来的对软件在轨重构和升级的迫切需求,基于SpaceWire设计了一种对星载软件升级与重构进行统一管理的网络,并设计实现了一款通用的软件维护控制器;星载软件重构网络通过SpaceWire路由器连接大容量存储模块以及星载计算机、载荷处理器等需要进行软件重构的终端设备,大容量存储器作为共享的存储资源池,用于接收各类星载设备的重构和升级软件程序,直接将软件程序通过SpaceWire分发到相应的设备,软件维护控制器位于终端设备内部,对软件重构数据进行统一格式封装,支持软件升级数据的片段化更新、软件ECC编码存储、软件APP切换控制等功能,解决星载软件升级缺乏统一操作流程、缺乏通用通信协议、缺乏高可靠设计支持的困境,并且该控制器采用抗辐射加固ASIC设计,满足宇航使用环境要求,为实现星载软件的灵活、便捷在轨重构奠定基础。     

支持多工具协同的流程管理系统的设计与实现

软件开发流程中越来越依赖各种计算机辅助软件（CAS）。在使用多种工具的情况下,会遇到开发流程不可定制、流程数据不一致和流程管理效率低等问题。针对这些问题提出了一个支持多工具协同工作的软件开发过程管理系统。通过分析软件开发过程,研究支持快速迭代、偏向流程管理的软件工程开发模式,结合工作流思想设计分层架构的软件开发过程管理系统。该系统经过严格的测试用例检测,在设计的576个测试用例下通过率达到85%,能够完成工具协同的大部分需求,包括流程可定义、保持数据一致和开发流程管理。该系统在7个软件开发组,共约200人的规模下推广试用。根据管理组、开发组和测试组的反馈结果,该系统比传统方式节省了每周例会的时间,方便了开发任务的管理,从而提高了开发效率。     

BDI Agent解释器的研究和改进

现有的“信念-愿望-意向(BDI)”Agent系统将它的3个主要心智状态(信念、愿望、意向) 作为数据结构而不是模态算子来使用,导致信念-愿望-意向Agent的研究在理论和实践之间 存在较大的差距.该文深入研究了过程推理系统(precedure reasoning system,简称PRS)和 扩展的多Agent推理系统(developed multi-agent reasoning system,简称dMARs),针对dM ARs系统的解释器,提出了一种改进的编程语言AgentBDI,结合一个典型的分布     

基于ZYNQ的行人检测系统的设计与实现

随着行人检测技术的发展和应用,迫切需要能够进行实时处理的嵌入式行人检测系统。采用ZYNQ-7000作为算法平台,设计一种基于HOG与AdaBoost级联分类器的行人检测系统。利用FPGA的并行特性,采用流水线结构替代传统的串行结构,实现HOG算法加速;将AdaBoost级联分类器保存在FPGA的BRAM中,通过查找表的方式,在单个时钟周期内即可完成匹配判断。利用ZYNQ的软硬件协同设计,根据功能和资源进行软硬件分工,提高系统性能。实验结果表明,该设计方法在保持同等检测性能的条件下,检测速度相比ARM片上系统提高了44倍。     

基于HLA的卫星分布仿真系统设计与实现

基于HLA(高层体系结构)规范,利用自主版权的AST-RTI(运行时间框架)软件,设计并实现了具有开放性、可扩展性、稳定性的卫星分布仿真系统。该系统完成了卫星侦察、通讯的过程仿真以及数据处理评估。解决了一系列关键技术,如时钟同步、大数据量信息传输、第3方软件集成、仿真Agent技术等。     

一种路由交换阵列节点芯片及其系统的设计与实现

介绍一种面向大数据处理数据中心应用的计算/控制/网络存储的路由交换阵列节点芯片及其所组成的安全交换阵列原型机的设计与实现;该路由交换阵列系统通过因特网远程使用软件定义网络(SDN)方式对高速安全交换网络的内部路由控制和安全等模块进行集中编程控制,满足数据中心对数据传输带宽容量的需求;同时并行计算过程中消除网络传输瓶颈,避免了数据中心网络等资源的长期占用浪费,为下一代数据中心解决方案的形成打下基础。另外还简述了其在金融交易系统领域大数据应用尝试的研究近况。     

Evaluating the recovery‐oriented approach through the systematic development of real complex applications

Recovery‐oriented software is built with the perspective that hardware or software failures and operation mistakes are facts to be coped with, as they are problems that cannot be fully solved while developing real complex applications. Consequently, any software will always have a non‐zero chance of failure. Some of these failures may be caused by defects that may be removed or encapsulated. From the point of view of removing or encapsulating defects, a failure is considered to be trivial, when (i) the required effort to identify and eliminate or encapsulate the causing defect is small, (ii) the risk of making mistakes in these steps is also small and (iii) the consequences of the failure are tolerable. It is highly important to design systems in such a way that most (ideally all) of the failures are trivial. Such systems are called ‘debuggable systems’. In this study, we present the results of systematic applying techniques that focus on creating debuggable software for real embedded applications. Copyright © 2008 John Wiley & Sons, Ltd.     

A Computational Framework for Practical Social Reasoning

This article describes a framework for practical social reasoning designed to be used for analysis, specification, and implementation of the social layer of agent reasoning in multiagent systems. Our framework, called the expectation strategy behavior (ESB) framework, is based on (i) using sets of update rules for social beliefs tied to observations (so‐called expectations), (ii) bounding the amount of reasoning to be performed over these rules by defining a reasoning strategy, and (iii) influencing the agent's decision‐making logic by means of behaviors conditioned on the truth status of current and future social beliefs. We introduce the foundations of ESB conceptually and present a formal framework and an actual implementation of a reasoning engine, which is specifically combined with a general (belief–desire–intention‐based) practical reasoning programming system. We illustrate the generality of ESB through select case studies, which show that it is able to represent and implement different typical styles of social reasoning. The broad coverage of existing social reasoning methods, the modularity that derives from its declarative nature, and its focus on practical implementation make ESB a useful tool for building advanced socially reasoning agents.