傻博士信箱

各位,在新的一个千年来临之际,祝大家新年愉快! 一段时间以来,陆续收到来信询问有关目前常用股票软件数据文件的格式问题。 例如一位朋友问:我家里安装了端照卡与钱龙软件接受实时股票数据,经过很长时间炒股,我总结了一些判断行情与选股的经验,并建立了一些数学方法,但是我怎样在我编的分析程序中将钱龙的行情数据正确读入呢?     

傲群选股专家

软件简介: 《傲群选股专家》是北京傲群科技发展有限公司经多年研究开发的一套股票静态盘后分析软件。它与同类分析软件的最大不同之处在于它“以选股为主”的设计思路。因为目前沪深股市的上市公司总数已有近千家之多,如此众多的股票如果每天全部翻看将要耗费大量时间和精力,相信大多数人无法做到。如果能利用电脑超强的计算能力把每天值得关注的股票一次全部选出并分类列举出来,无疑会给用户带来极大的便利。     

炒家2000访问录

今天,但凡股票中包含了网络概念就是令人瞩目的高科技股,上升潜力不可限量;而通过网络来获取股票信息,利用智能选股进行分析和选股,来提高股民选股的命中率,使自己手中的资金得到最大程度的升值,这就是我们做这期“在线理财”主题的目的——帮助大家在扑朔迷离的股市里选出升值潜力最大的股票,让广大股民在上千只股票的海洋里有个罗盘,来判断自己航行的方向。希望大家在阅读完下面的文章之后能够有所收获。本期杂志上介绍的所有智能选股软件都收录在配套光盘“特别推荐”中,经过我们和作者的联系都能够进行一段时间的试用。在此特别感谢醉翁亭网站(http://www.tanfu.com)给予大力支持。     

赢政股市分析软件趋势全面把握

目前网上提供下载的各种股票分析软件大都为实时行情浏览软件,而赢证股市分析软件是一款专为广大中小投资者设计的股票盘后分析软件。     

擂台赛

本次擂台赛的题目是设计有关股票的小工具软件,来稿涉及的内容包括:从常用股票数据接受软件的数据文件提取数据与进行数据格式转换、股票交易赢损的计算工具、选股、自设定股票技术指标等等众多话题。 就股票预测及辅助抉择方面,我个人最感兴趣的话题之一是人工神经网络方法,这种方法用于预测已     

新软推荐

股民福音现在“股民”队伍不断壮大,凭炒股发家致富的人也越来越多,怎样才能成为“股坛”高人呢?当然得有诀窍,《股票之星》就是一本功能强大的秘笈,它的作图、预测、选股、计算和个人账簿功能令你轻松炒股,它最大的特色就是会告诉你任意一只股票的预测结果,就像一位经验丰富的股票专家随时给你指点。通过本软件转化通用数据、分析家、海融或钱龙数据为股星数据,这样,无论你是否上网,都可得到每日的股票数据。     

基于K-近邻法的股价曲线的模式识别分类研究

本文将模式识别分类技术应用于股票市场进行搜索选股。本项目采用股票价格走势来构造特征空间,依据样本股票价格走势类型对股票市场上的所有股票进行模式识别分类,并将所获得的分类结果中的第一类股票作为搜索选股的目标股票。通过对沪深两市股票进行的实证研究分析,结果表明:采用模式识别分类技术依据股票价格走势进行搜索选股是可行的,具有良好的实时性和较高的针对性,实用性较强,并可进一步改进。     

家庭大户室

三、投资理念与技术分析指标 股票市场是证券投资市场,也是金融投机的场所。在这里有着形形色色的参与者,更充斥着各式各样的阴谋。正确的投资理念和精堪的技术分析方法的结合是在这个市场生存和发展的必备条件! 下面应用“世纪龙”证券行情分析系统软件提供的基于经典分析理论的特色指标,开放式的结构进行自定义的技术分析指标及选股智能化功能,通过具体的实例来有效地研判行情。     

基于机器学习对优质股的选择

股票已经成为当下热门话题,而炒股即是卖出低于买入的股票价钱,从中获取利益。当今股市的变化无疑使风云涌动,其原因繁多复杂,风险和利益相伴而行,对股票的判断无疑考究股民的背景知识和经验,而且众多股民在选择股票时,大多受主观因素影响而导致利益受损。但是这几年来机器学习快速发展,我们可以通过机器学习来克服股民在选股时背景知识和经验的局限性。机器学习通过模仿股民选股的行为,对已有的背景知识、历史数据进行分析综合、归纳从而建立一个模型。在股票的选择中,本文采取机器学习中的随机森林算法对股票的各项因子进行分析归纳整合,经过训练得到选股模型,最终挑选出优质股。该领域无疑为股票投资开启了一扇新的大门。     

CHIP俱乐部

清华永新信息接收卡大家评 清华永新卡外观简洁大方，接收股市行情的速度，２Ｍ／帧，没有误码，安装方便。因为之前没有使用过接收卡、本人直接下载网上证券接收系统，直接进行在线炒股，网费每月约为１００元左右即可。清华永新股票分析软件方便，实用，可智能选股，希望以后再增加智能选股，股市预警功能。 惠健 清华永新卡的补齐历史数据所用时间较短，第一次补齐历史数据所用的时间约１５分钟，股市接收速度也很快，与外置卡相比使用比较简单，方便。接收卡中附赠的清华永新股票分析软件，我认为其窗口显示较直观，操作性强与其它软件…     

基于Pytorch的LSTM模型对股价的分析与预测

股价预测一直都是股票投资者重点关注和重点研究的方向,针对股价具有高度非线性、高噪声、动态性等问题,提出一种基于自组织特征映射(SOM)神经网络和长短期记忆网络(LSTM)共同应用的股价预测方法。第一步聚类,使用python语言实现改进的自组织特征映射神经网络算法,将187支股票分成三类,三类股票以盈利能力大小进行聚类,并且求出每一类所包含的股票代码;第二步预测,基于Pytorch深度学习框架构造长短期记忆网络模型,分别对每一类中随机的3支股票进行股价预测,再通过均方误差和决定系数对预测结果进行评价。结果表明,在使用相同的预测模型对不同盈利能力的股票做股价预测时,盈利能力越大的股票,预测精度越高。此研究可以为投资者筛选出盈利能力更大的股票,并且在提高股价预测精度上也具有一定的贡献。     

基于深度迁移学习的多尺度股票预测

股票市场不仅是上市公司的重要融资渠道,也是重要的投资市场,股票预测一直受到人们的关注。为了充分利用来自不同股票价格的信息,提高股票的预测效果,提出一种多尺度股票价格预测模型TL-EMD-LSTM-MA（TELM）。TELM模型通过经验模态分解将收盘价分解为多个时间尺度分量,不同时间尺度分量震荡频率不同,反映了不同的周期性信息;根据分量的震荡频率选择不同方法进行预测,高频分量利用深度迁移学习的方法训练堆叠LSTM,低频分量利用移动平均法进行预测;将所有分量的预测值相加作为收盘价的最终预测输出。通过深度迁移学习训练的堆叠LSTM,包含来自不同股票的信息,具备更多行业或市场的知识,能有效降低预测误差。利用移动平均法预测低频分量,更有效捕获股票的总体趋势。对中国A股市场内500支股票以及上证指数、深证成指等指数进行预测,结果表明,与其他模型相比,TELM预测误差最低,拟合优度最高。根据TELM预测的股票收盘价模拟股票交易过程,结果表明TELM投资风险低、收益高。     

基于生成式对抗神经网络的股票预测研究

针对股票价格具有非线性、非平稳的特点,提出一种结合自注意力机制和残差网络的生成式对抗神经网络模型（SAR-GAN）。该模型的生成器（generator）由长短期记忆网络（LSTM）层、自注意力机制层、残差层等构建而成,用于生成所预测股票的价格;判别器（discriminator）用于鉴别生成的股票价格与真实的股票价格。为验证模型良好的泛化性,选取上证指数及不同股票市场的热点行业龙头股票进行预测实验。实验结果表明,与LSTM、GRU、CNN-LSTM、CNN-GRU等模型相比,SAR-GAN模型能不同程度地减少预测误差。     

An integrated TOPSIS crow search based classifier ensemble: In application to stock index price movement prediction

Predicting future stock index price movement has always been a fascinating research area both for the investors who wish to yield a profit by trading stocks and for the researchers who attempt to expose the buried information from the complex stock market time series data. This prediction problem can be addressed as a binary classification problem with two class labels, one for the increasing movement and other for the decreasing movement. In literature, a wide range of classifiers has been tested for this application. As the performance of individual classifier varies for a diverse dataset with respect to different performance measures, it is impractical to acknowledge a specific classifier to be the best one. Hence, designing an efficient classifier ensemble instead of an individual classifier is fetching increasing attention from many researchers. Again selection of base classifiers and deciding their preferences in ensemble with respect to a variety of performance criteria can be considered as a Multi Criteria Decision Making (MCDM) problem. In this paper, an integrated TOPSIS Crow Search based weighted voting classifier ensemble is proposed for stock index price movement prediction. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), one of the popular MCDM techniques, is suggested for ranking and selecting a set of base classifiers for the ensemble whereas the weights of the classifiers used in the ensemble are tuned by the Crow Search method. The proposed ensemble model is validated for prediction of stock index price over the historical prices of BSE SENSEX, S&P500 and NIFTY 50 stock indices. The model has shown better performance compared to individual classifiers and other ensemble models such as majority voting, weighted voting, differential evolution and particle swarm optimization based classifier ensemble.     

Using a Stochastic Complexity Measure to Check the Efficient Market Hypothesis

The weak form of the Efficient Market Hypothesis (EMH) states that current market price reflects fully the information from past prices and rules out prediction based on price data alone. No recent test of time series of stock returns rejects this weak-form hypothesis. This research offers another test of the weak form of the EHM that leads to different conclusions for some time series.The stochastic complexity of a time series is a measure of the number of bits needed to represent and reproduce the information in the time series. In an efficient market, compression of the time series is not possible, because there are no patterns and the stochastic complexity is high. In this research, Rissanen's context tree algorithm is used to identify recurring patterns in the data, and use them for compression. The weak form of the EMH is tested for 13 international stock indices and for all the stocks that comprise the Tel-Aviv 25 index (TA25), using sliding windows of 50, 75, and 100 consecutive daily returns. Statistically significant compression is detected in ten of the international stock index series. In the aggregate, 60% to 84% of the TA25 stocks tested demonstrate compressibility beyond randomness. This indicates potential market inefficiency.     

Application of Optimistic and Pessimistic OWA and DEA Methods in Stock Selection

One of the main objectives of fund managers in financial service industry is to select superior stocks by analyzing financial ratios. This paper proposes a novel methodology for stock selection by integrating optimistic and pessimistic ordered weighted averaging (OWA) and data envelopment analysis (DEA) methods. The paper first reveals the drawback of using the standard DEA models for stocks evaluation and then proposes a new method by using the OWA operator. Unlike the classical DEA, the proposed method in this paper does not involve the specification of inputs and outputs. The paper incorporates optimistic and pessimistic scenarios and generates interval OWA scores for all stocks. This is followed by using appropriate interval DEA models for selecting superior stocks. The proposed method in this paper is applied to identify high financial performance stocks in the Tehran stock market.     

The economics of stock touting during Internet‐based pump and dump campaigns

Information systems have facilitated the increase in relevance of financial markets. Nevertheless, the rise of the Internet has eased information‐based financial market manipulations. In this study, we examine the phenomenon of stock touting during pump and dump campaigns, in which deceivers advertise stocks to profit from an increased price level. We observe that the positive prospects promised are not confirmed by corporate disclosures and financial news. Furthermore, manipulators select targeted financial instruments based on specific stock and company characteristics. Manipulators avoid signals of anomaly and prefer unknown stocks. We find that stock touting has a positive market impact but that it is followed by a large decline in stock price in the subsequent days, causing investors to lose substantial amounts of their investments. We consider the impact of information generation, information content, and information presentation on the corresponding market reaction. Interestingly, information generation influences the demand for the stock, but information content and information presentation drive the willingness to pay. Our results are highly relevant for Internet users, software vendors, and market surveillance authorities, as a deep understanding of such information‐based manipulations is necessary to develop appropriate countermeasures.     

Trading With a Stock Chart Heuristic

The efficient market hypothesis (EMH) is a cornerstone of financial economics. The EMH asserts that security prices fully reflect all available information and that the stock market prices securities at their fair values. Therefore, investors cannot consistently ldquobeat the marketrdquo because stocks reside in perpetual equilibrium, making research efforts futile. This flies in the face of the conventional nonacademic wisdom that astute analysts can beat the market using technical or fundamental stock analysis. The purpose of this research is to partially assess whether technical analysts, who predict future stock prices by analyzing past stock prices, can consistently achieve a trading return that outperforms the stock market average return. This is tested using knowlege engineering experimentation with one price history pattern - the ldquobull flag stock chartrdquo - which signals technical analysts of a future stock market price increase. A recognizer for the stock chart pattern is built using a template-matching technique from pattern recognition. The recognizer and associated trading rules are then tested by simulating trading on over 35 years of daily closing price data for the New York stock exchange composite index. The experiment is then replicated using the horizontal rotation or mirror image pattern of the ldquobull flagrdquo (or ldquobear flagrdquo stock chart) that signals a future stock market decrease. Results are systematic, statistically significant, and fail to confirm the null hypothesis based on a corollary to the EMH: that profit realized from trading determined by this heuristic method is no better than what would be realized from trading decisions based on random choice.     

Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index – Case study of PETR4, Petrobras,Brazil

Predicting the direction of stock price changes is an important factor, as it contributes to the development of effective strategies for stock exchange transactions and attracts much interest in incorporating variables historical series into the mathematical models or computer algorithms in order to produce estimations of expected price fluctuations. The purpose of this study is to build a neural model for the financial market, allowing predictions of stocks closing prices future behavior negotiated in BM&FBOVESPA in the short term, using the economic and financial theory, combining technical analysis, fundamental analysis and analysis of time series, to predict price behavior, addressing the percentage of correct predictions of price series direction (POCID or Prediction of Change in Direction). The aim of this work is to understand the information available in the financial market and identify the variables that drive stock prices. The methodology presented may be adapted to other companies and their stock. Petrobras stock PETR4, traded in BM&FBOVESPA, was used as a case study. As part of this effort, configurations with different window sizes were designed, and the best performance was achieved with a window size of 3, which the POCID index of correct direction predictions was 93.62% for the test set and 87.50% for a validation set.