证券投资心得与基于Java的MACD指标的实现

要想更好地进行证券交易必须从心理分析、资金管理、技术分析、基本面分析等4方面不断学习、实践、领悟、总结提高,制定适合自身个性的交易规则,并严格遵守.阐述了对心理分析、资金管理、技术分析的一些心得体会,并给出了Java语言编写的MACD指标.     

图形语言的延伸——传统图形语言对平面设计的启发

在经济全球化和文化多元化同时并进的时代,设计领域各种思想发生着强烈的碰撞和融合。国外各种新思潮的涌入和渗透,动摇着我们固有的价值观与审美观,使我们的作品越来越远离民族的个性和丧失自身的话语权。本文从分析传统图形语言的艺术魅力,肯定传统图形的民族独特性入手,研究传统图形的联想和拓展。探索平面设计中图形的形象化表现,意象化表现,"形"与"意"的综合表现手法,并探讨传统图形语言在平面设计中的再现。     

基于金融技术指标的用电数据分析

针对已有用电数据分析缺乏有效描述趋势性特征的不足,适应性地将金融领域中十字过滤线(VHF)、异同移动平均线(MACD)等技术指标迁移至用电数据分析中,提出了基于金融技术指标的异动检测算法和负荷预测算法.所提异动检测算法通过统计各指标的统计情况划定阈值,并采用阈值检测捕捉用户异常用电行为.所提负荷预测算法通过提取14项与...     

传统图形语言与现代设计

本文浅析了在经济全球化时代设计师对传统图形语言的把握,以及与现代设计发展遇到的一些问题。民间艺术对现代设计的影响和发展,现代设计对民间艺术的延伸与创新。重新认识传统图形语言的魅力,重视传统文化,把握传统图形语言的民族精神等。     

基于ATLG混合模型的股票价格预测

股市是金融市场的重要组成部分,对股票价格预测有着重要的意义.同时,深度学习具有强大的数据处理能力,可以解决金融时间序列的复杂性所带来的问题.对此,本文提出一种结合自注意力机制的混合神经网络模型(ATLG).该模型由长短期记忆网络(LSTM)、门控递归单元(GRU)、自注意力机制构建而成,用于对股票价格的预测.实验结果表明:(1)与LSTM、GRU、RNN-LSTM、RNN-GRU等模型相比, ATLG模型的准确率更高;(2)引入自注意力机制使模型更能聚焦于重要时间点的股票特征信息;(3)通过对比,双层神经网络起到的效果更为明显.(4)通过MACD (moving average convergence and divergence)指标进行回测检验,获得了53%的收益,高于同期沪深300的收益.结果证明了该模型在股票价格预测中的有效性和实用性.     

以折线为母线的自相似分形图形的程序实现

自相似分形图形是分形图形的重要分支,以折线为母线的自相似分形图形是自相似分形图形的主要组成部分。该文对传统的根据具体母线形状编程画图的方法加以改进,给出了一个程序实现所有以折线为母线的自相似分形图形的作图方法并编写了实现该方法的源程序、源程序经编译连接形成可执行文件后,运行时只需在命令中给出母线数据,计算机便自动获取屏幕尺寸,确定图形大小和位置,然后将图形画出。     

SVG网页数字图像标准的应用

可申缩放矢量图形（Scalable Vector Graphics,SVG）是基于可扩展标记语言（XML）,用于描述二维矢量图形的一种图形格式。与传统的位图相比,矢量图是由一些形状元素组成,放大后仍能够显示形状。而传统的位图由像素点组成,放大后将导致马赛克情况的出现。SVG是一种矢量图,能够无级缩放,不会出现马赛克现象,而日结合了位图的一些优点,动态性有很大提高,因此在众多领域都有着应用前景。     

苗族刺绣纹样之图形语言分析

图形语言分析的研究目的是解决图形符号以及符号组合的语义内涵。本文以凯棠型苗绣纹样为具体研究对象,从语言结构特点出发,分析了纹样的造型、色彩、风格特征以及语义内涵,并借助于现代形式语言对传统苗绣纹样的转换作可行性分析,尝试表达意义的重构。     

汉语语言的变化对传统图形设计的启示

图形语言无论在结构上还是在功能方面都与一般的言辞性语言有相当程度的共同性,在传达讯息方面近乎"异曲同工"。在演进的条件、方式和变化特点上我们都可以做以比较。以语言学的角度来看待传统图形设计的发展方向可以给我们新的启示。     

软件生产线可变性建模与分析方法的研究

为了对软件生产线进行可变性建模,提出了使用特征模型捕获领域需求的思想。针对传统特征模型缺乏对可变性建模的支持,仅有图形上的表示,缺乏严谨的语义分析的不足,通过对软件生产线可变性依赖约束的形式化描述,提出了一种扩展特征模型的软件生产线可变性需求建模方法,以手机领域为例子进行了可变性需求建模。提出了扩展特征模型图形元素的语义特征及文档类型定义,并与传统特征模型建模进行了对比。结合逻辑学提出了可变性绑定配置过程中的分析方法。     

Forecasting IBEX-35 moves using support vector machines

This research aims at examining the application of support vector machines (SVMs) to the task of forecasting the weekly change in the Madrid IBEX-35 stock index. The data cover the period between 10/18/1990 and 10/29/2010. A trading simulation is implemented so that statistical efficiency is complemented by measures of economic performance. The inputs retained are traditional technical trading rules commonly used in the analysis of equity markets such as the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD) decision rules. The SVMs with given values of the RSI and MACD indicators are used in order to determine the best situations to buy or sell the market. The two outputs of the SVM are both the direction of the market and the probability attached to each forecast market move. The best result that it has been achieved is a hit ratio of 100% using the SVM classifier under some chosen risk-aversion parameters. However, these results are obtained analyzing recent periods rather than using all the dataset information.     

一种基于用户关联分析的热点话题识别算法

为了提高从社交网络文本信息中发现热点话题的准确率,提出一种基于用户关联分析的热点话题识别算法。该算法综合考虑词频变化率和用户权威度,词频变化率通过EMA和MACD等指标来计算,用户权威度通过建立用户关联图的方式来计算。使用基于HITS算法的话题热度度量计算方法,将词频变化率数据和用户权威度数据结合在一起,得到话题的热度值。实验结果表明,使用基于用户关联分析的热点话题识别算法能够提高热点话题发现准确率。     

Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition

The management of financial portfolios or funds constitutes a widely known problematic in financial markets which normally requires a rigorous analysis in order to select the most profitable assets. The presented paper proposes a new approach, based on Intelligent Computation, in particular genetic algorithms, which aims to manage a financial portfolio by using technical analysis indicators (EMA, HMA, ROC, RSI, MACD, TSI, OBV). In order to validate the developed solution an extensive evaluation was performed, comparing the designed strategy against the market itself and several other investment methodologies, such as Buy and Hold and a purely random strategy. The time span (2003–2009) employed to test the approach allowed the performance evaluation under distinct market conditions, culminating with the most recent financial crash. The results are promising since the approach clearly beats the remaining approaches during the recent market crash.     

A system for relevance analysis of performance indicators in higher education using Bayesian networks

In this paper, we propose a methodology for relevance analysis of performance indicators in higher education based on the use of Bayesian networks. These graphical models provide, at first glance, a snapshot of the relevant relationships among the variables under consideration. We analyse the behaviour of the proposed methodology in a practical case, showing that it is a useful tool to help decision making when elaborating policies based on performance indicators. The methodology has been implemented in a software that interacts with the Elvira package for graphical models, and that is available to the administration board at the University of Almería (Spain) through a web interface. The software also implements a new method for constructing composite indicators by using a Bayesian network regression model.     

Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules

Recent work has been devoted to study the use of multiobjective evolutionary algorithms (MOEAs) in stock portfolio optimization, within a common mean-variance framework. This article proposes the use of a more appropriate framework, mean-semivariance framework, which takes into account only adverse return variations instead of overall variations. It also proposes the use and comparison of established technical analysis (TA) indicators in pursuing better outcomes within the risk-return relation. Results show there is some difference in the performance of the two selected MOEAs – non-dominated sorting genetic algorithm II (NSGA II) and strength pareto evolutionary algorithm 2 (SPEA 2) – within portfolio optimization. In addition, when used with four TA based strategies – relative strength index (RSI), moving average convergence/divergence (MACD), contrarian bollinger bands (CBB) and bollinger bands (BB), the two selected MOEAs achieve solutions with interesting in-sample and out-of-sample outcomes for the BB strategy.     

Bresenham's Line Generation Algorithm with Built-in Clipping

One of the most important operations in many graphical systems is the generation of a line segment. This process consists of two stages: clipping and drawing. These two stages are separated in current graphical applications. In this paper a new approach to line generation is proposed, which unifies these stages. The proposed algorithm is based on Bresenham's line generation algorithm to include necessary line clipping. The line clipping stage is an operation-reduced, integer arithmetic only algorithm. The notion of correctness of line clipping is introduced and correctness of the proposed algorithm is shown. Complete C-notation of the algorithm is included.     

TM53202O实现的数字滤波器

本文叙述了一个由ＴＭＳ３２０２０实现的数字滤波器，对该滤波器的硬件结构与软件编程作了详细讨论。该滤波器由Ａ／Ｄ变换、数字滤波和Ｄ／Ａ变换三部分组成、ＴＭＳ３２０２０对Ａ／Ｄ转换器的采样采用内部定时器中断的方法，数字滤波使用功能很强的ＭＡＣＤ指令。     

Profitability Edge by Dynamic Back Testing Optimal Period Selection for Technical Parameters Optimization,in Trading Systems with Forecasting

Back testing process is widely used today in forecasting experiments tests. This method is to calculate the profitability of a trading system, applied to specific past period. The data which are used, correspond to that specific past period and are called “historical data” or “training data”. There is a plethora of trading systems, which include technical indicators, trend following indicators, oscillators, control indicators of price level, etc. It is common nowadays for calculations of technical indicator values to be used along with the prices of securities or shares, as training data in fuzzy, hybrid and support vector machine/regression (SVM/SVR) systems. Whether the data are used in fuzzy systems, or for SVM and SVR systems training, the historical data period selection on most occasions is devoid of validation (In this research we designate historical data as training data). We substantiate that such an expert trading system, has a profitability edge—with regard to future transactions—over currently applied trading strategies that merely implement parameters’ optimization. Thus not profitable trading systems can be turned into profitable. To that end, first and foremost, an optimal historical data period must be determined, secondarily a parameters optimization computation must be completed and finally the right conditions of parameters must be applied for optimal parameters’ selection. In this new approach, we develop an integrated dynamic computation algorithm, called the “d-BackTest PS Method”, for selection of optimal historical data period, periodically. In addition, we test conditions of parameters and values via back-testing, using multi agent technology, integrated in an automated trading expert system based on Moving Average Convergence Divergence (MACD) technical indicator. This dynamic computation algorithm can be used in Technical indicators, Fuzzy, SVR and SVM and hybrid forecasting systems. The outcome crystalizes in an autonomous intelligent trading system.