Download or read book Evolutionary Algorithms in Optimization of Technical Rules for Automated Stock Trading written by Harish K. Subramanian and published by . This book was released on 2004 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: The effectiveness of technical analysis indicators as a means of predicting future price levels and enhancing trading profitability in stock markets is an issue constantly under review. It is an area that has been researched and its profitability examined in foreign exchange trade [1], portfolio management [2] and day trading [3]. Their use has been advocated by many traders [4], [5] and the uses of these charting and analysis techniques are being scrutinized [6], [7]. However, despite their popularity among human traders, a number of popular technical trading rules can be loss-making when applied individually, typically because human technical traders use combinations [8], [9] of a broad range of these technical indicators. Moreover, successful traders tend to adapt to market conditions by varying the weight they give to certain trading rules and dropping some of them as they are deemed to be loss-making. In this thesis, we try to emulate such a strategy by developing trading systems consisting of rules based on combinations of different indicators, and evaluating their profitability in a simulated economy. We propose and empirically examine two schemes, using evolutionary algorithms (genetic algorithm and genetic programming), of optimizing the combination of technical rules. A multiple model approach [10a] is used to control agent behavior and encourage unwinding of share position to ensure a zero final share position (as is essential within the framework that our experiments are run in). Evaluation of the evolutionary composite technical trading strategies leads us to believe that there is substantial merit in such evolutionary designs (particularly the weighted majority model), provided the right learning parameters are used. To explore this possibility, we evaluated a fitness function measure limiting only downside volatility, and compared its behavior and benefits with the classical Sharpe ratio, which uses a measure of standard deviation. The improved performance of the new fitness function strengthens our claim that a weighted majority approach could indeed be useful, albeit with a more sophisticated fitness function

Download or read book Natural Computing in Computational Finance written by Anthony Brabazon and published by Springer Science & Business Media. This book was released on 2008-05-09 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural Computing in Computational Finance is a innovative volume containing fifteen chapters which illustrate cutting-edge applications of natural computing or agent-based modeling in modern computational finance. Following an introductory chapter the book is organized into three sections. The first section deals with optimization applications of natural computing demonstrating the application of a broad range of algorithms including, genetic algorithms, differential evolution, evolution strategies, quantum-inspired evolutionary algorithms and bacterial foraging algorithms to multiple financial applications including portfolio optimization, fund allocation and asset pricing. The second section explores the use of natural computing methodologies such as genetic programming, neural network hybrids and fuzzy-evolutionary hybrids for model induction in order to construct market trading, credit scoring and market prediction systems. The final section illustrates a range of agent-based applications including the modeling of payment card and financial markets. Each chapter provides an introduction to the relevant natural computing methodology as well as providing a clear description of the financial application addressed. The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, in the fields of both natural computing and finance.

Download or read book Computational Intelligence Techniques for Trading and Investment written by Christian Dunis and published by Routledge. This book was released on 2014-03-26 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational intelligence, a sub-branch of artificial intelligence, is a field which draws on the natural world and adaptive mechanisms in order to study behaviour in changing complex environments. This book provides an interdisciplinary view of current technological advances and challenges concerning the application of computational intelligence techniques to financial time-series forecasting, trading and investment. The book is divided into five parts. The first part introduces the most important computational intelligence and financial trading concepts, while also presenting the most important methodologies from these different domains. The second part is devoted to the application of traditional computational intelligence techniques to the fields of financial forecasting and trading, and the third part explores the applications of artificial neural networks in these domains. The fourth part delves into novel evolutionary-based hybrid methodologies for trading and portfolio management, while the fifth part presents the applications of advanced computational intelligence modelling techniques in financial forecasting and trading. This volume will be useful for graduate and postgraduate students of finance, computational finance, financial engineering and computer science. Practitioners, traders and financial analysts will also benefit from this book.

Download or read book Applications of Evolutionary Computing written by Mario Giacobini and published by Springer Science & Business Media. This book was released on 2008-03-14 with total page 724 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of eight European workshops on the Theory and Applications of Evolutionary Computation, EvoWorkshops 2008, held in Naples, Italy, in March 2008 within the scope of the EvoStar 2008 event. The 57 revised full papers and 18 revised short papers presented were carefully reviewed and selected from a total of 133 submissions. In accordance with the eight workshops covered, the papers are organized in topical sections on application of nature-inspired techniques to telecommunication networks and other connected systems, evolutionary computation in finance and economics, bio-inspired heuristics for design automation, evolutionary computation in image analysis and signal processing, evolutionary and biologically inspired music, sound, art and design, bio-inspired algorithms for continuous parameter optimization, evolutionary algorithms in stochastic and dynamic environments, theory and applications of evolutionary computation, and on evolutionary computation in transportation and logistics.

Download or read book Genetic Algorithms in Applications written by Rustem Popa and published by BoD – Books on Demand. This book was released on 2012-03-21 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Algorithms (GAs) are one of several techniques in the family of Evolutionary Algorithms - algorithms that search for solutions to optimization problems by "evolving" better and better solutions. Genetic Algorithms have been applied in science, engineering, business and social sciences. This book consists of 16 chapters organized into five sections. The first section deals with some applications in automatic control, the second section contains several applications in scheduling of resources, and the third section introduces some applications in electrical and electronics engineering. The next section illustrates some examples of character recognition and multi-criteria classification, and the last one deals with trading systems. These evolutionary techniques may be useful to engineers and scientists in various fields of specialization, who need some optimization techniques in their work and who may be using Genetic Algorithms in their applications for the first time. These applications may be useful to many other people who are getting familiar with the subject of Genetic Algorithms.

Download or read book An Evolutionary Approach to Optimization of Compound Stock Trading Indicators Used to Confirm Buy Signals written by Allan Wayne Teeples and published by . This book was released on 2010 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis examines the application of genetic algorithms to the optimization of a composite set of technical indicator filters to confirm or reject buy signals in stock trading, based on probabilistic values derived from historical data. The simplicity of the design, which gives each filter within the composite filter the ability to act independently of the other filters, is outlined, and the cumulative indirect effect each filter has on all the others is discussed. This system is contrasted with the complexity of systems from previous research that attempt to merge several indicator filters together by giving each one a weight as a percentage of the whole, or which build a decision tree based rule comprised of several indicators. The detrimental effects of short-term market fluctuations on the effectiveness of the optimization are considered, and attempts to mitigate these effects by reducing the length of the optimization interval are discussed. Finally, the optimized indicators are used in simulated trading, using historical data. The results from the simulation are compared with the annual returns of the NASDAQ - 100 Index on a yearly basis over a period of four years. The comparison shows that the composite indicator filter is proficient enough at filtering out inferior buy signals to substantially outperform the NASDAQ - 100 Index during each year of the simulation.

Download or read book Proceedings of the XV International symposium Symorg 2016 written by Ondrej Jaško and published by University of Belgrade, Faculty of Organizational Sciences . This book was released on 2016-06-03 with total page 1520 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Using Genetic Algorithms to Find Technical Trading Rules written by Franklin Allen and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A genetic algorithm is used to learn technical trading rules for Standard and Poor's composite stock index using data from 1963-69. In the out-of-sample test period 1970-1989 the rules are able to identify periods to be in the indexwhen returns are positive and volatility is low and out when the reverse is true. Compared to a simple buy-and-hold strategy, they lead to positive excess returns after transaction costs in the period of 1970-89. Using data for other periods since 1929, the rules can identify high returns and low volatility but do not lead to excess returns after transaction costs. The results are compared to benchmark models of a random walk, an autoregressive model, and a GARCH-AR model. Bootstrapping simulations indicate that none of these models of stock returns can explain the findings.

Download or read book Mining Optimal Technical Trading Rules with Genetic Algorithms written by Rujun Shen and published by . This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Mining Optimal Technical Trading Rules With Genetic Algorithms" by Rujun, Shen, 沈汝君, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: In recent years technical trading rules are widely known by more and more people, not only the academics many investors also learn to apply them in financial markets. One approach of constructing technical trading rules is to use technical indicators, such as moving average(MA) and filter rules. These trading rules are widely used possibly because the technical indicators are simple to compute and can be programmed easily. An alternative approach of constructing technical trading rules is to rely on some chart patterns. However, the patterns and signals detected by these rules are often made by the visual inspection through human eyes. As for as I know, there are no universally acceptable methods of constructing the chart patterns. In 2000, Prof. Andrew Lo and his colleagues are the first ones who define five pairs of chart patterns mathematically. They are Head-and-Shoulders(HS) & Inverted Headand- Shoulders(IHS), Broadening tops(BTOP) & bottoms(BBOT), Triangle tops(TTOP) & bottoms(TBOT), Rectangle tops(RTOP) & bottoms( RBOT) and Double tops(DTOP) & bottoms(DBOT). The basic formulation of a chart pattern consists of two steps: detection of (i) extreme points of a price series; and (ii) shape of the pattern. In Lo et al.(2000), the method of kernel smoothing was used to identify the extreme points. It was admitted by Lo et al. (2000) that the optimal bandwidth used in kernel method is not the best choice and the expert judgement is needed in detecting the bandwidth. In addition, their work considered chart pattern detection only but no buy/sell signal detection. It should be noted that it is possible to have a chart pattern formed without a signal detected, but in this case no transaction will be made. In this thesis, I propose a new class of technical trading rules which aims to resolve the above problems. More specifically, each chart pattern is parameterized by a set of parameters which governs the shape of the pattern, the entry and exit signals of trades. Then the optimal set of parameters can be determined by using genetic algorithms (GAs). The advantage of GA is that they can deal with a high-dimensional optimization problems no matter the parameters to be optimized are continuous or discrete. In addition, GA can also be convenient to use in the situation that the fitness function is not differentiable or has a multi-modal surface. DOI: 10.5353/th_b4787001 Subjects: Stocks - Prices - Statistical methods Investments - Statistical methods Genetic algorithms

Download or read book Advanced Trading Rules written by Emmanual Acar and published by Elsevier. This book was released on 2002-05-23 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Trading Rules is the essential guide to state of the art techniques currently used by the very best financial traders, analysts and fund managers. The editors have brought together the world's leading professional and academic experts to explain how to understand, develop and apply cutting edge trading rules and systems. It is indispensable reading if you are involved in the derivatives, fixed income, foreign exchange and equities markets. Advanced Trading Rules demonstrates how to apply econometrics, computer modelling, technical and quantitative analysis to generate superior returns, showing how you can stay ahead of the curve by finding out why certain methods succeed or fail. Profit from this book by understanding how to use: stochastic properties of trading strategies; technical indicators; neural networks; genetic algorithms; quantitative techniques; charts. Financial markets professionals will discover a wealth of applicable ideas and methods to help them to improve their performance and profits. Students and academics working in this area will also benefit from the rigorous and theoretically sound analysis of this dynamic and exciting area of finance. The essential guide to state of the art techniques currently used by the very best financial traders, analysts and fund managers Provides a complete overview of cutting edge financial markets trading rules, including new material on technical analysis and evaluation Demonstrates how to apply econometrics, computer modeling, technical and quantitative analysis to generate superior returns

Download or read book Information Systems for Global Financial Markets Emerging Developments and Effects written by Yap, Alexander Y. and published by IGI Global. This book was released on 2011-11-30 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book offers focused research on the systems and technologies that provide intelligence and expertise to traders and investors and facilitate the agile ordering processes, networking, and regulation of global financial electronic markets"--Provided by publisher.

Download or read book An Introduction to High Frequency Finance written by Ramazan Gençay and published by Elsevier. This book was released on 2001-05-29 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Liquid markets generate hundreds or thousands of ticks (the minimum change in price a security can have, either up or down) every business day. Data vendors such as Reuters transmit more than 275,000 prices per day for foreign exchange spot rates alone. Thus, high-frequency data can be a fundamental object of study, as traders make decisions by observing high-frequency or tick-by-tick data. Yet most studies published in financial literature deal with low frequency, regularly spaced data. For a variety of reasons, high-frequency data are becoming a way for understanding market microstructure. This book discusses the best mathematical models and tools for dealing with such vast amounts of data. This book provides a framework for the analysis, modeling, and inference of high frequency financial time series. With particular emphasis on foreign exchange markets, as well as currency, interest rate, and bond futures markets, this unified view of high frequency time series methods investigates the price formation process and concludes by reviewing techniques for constructing systematic trading models for financial assets.

Download or read book Automated Option Trading written by Sergey Izraylevich Ph.D. and published by FT Press. This book was released on 2012-03-12 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first and only book of its kind, Automated Options Trading describes a comprehensive, step-by-step process for creating automated options trading systems. Using the authors’ techniques, sophisticated traders can create powerful frameworks for the consistent, disciplined realization of well-defined, formalized, and carefully-tested trading strategies based on their specific requirements. Unlike other books on automated trading, this book focuses specifically on the unique requirements of options, reflecting philosophy, logic, quantitative tools, and valuation procedures that are completely different from those used in conventional automated trading algorithms. Every facet of the authors’ approach is optimized for options, including strategy development and optimization; capital allocation; risk management; performance measurement; back-testing and walk-forward analysis; and trade execution. The authors’ system reflects a continuous process of valuation, structuring and long-term management of investment portfolios (not just individual instruments), introducing systematic approaches for handling portfolios containing option combinations related to different underlying assets. With these techniques, it is finally possible to effectively automate options trading at the portfolio level. This book will be an indispensable resource for serious options traders working individually, in hedge funds, or in other institutions.

Download or read book Stock Exchange Trading Using Grid Pattern Optimized by A Genetic Algorithm with Speciation written by Tiago Martins and published by Springer Nature. This book was released on 2021-07-08 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a genetic algorithm that optimizes a grid template pattern detector to find the best point to trade in the SP 500. The pattern detector is based on a template using a grid of weights with a fixed size. The template takes in consideration not only the closing price but also the open, high, and low values of the price during the period under testing in contrast to the traditional methods of analysing only the closing price. Each cell of the grid encompasses a score, and these are optimized by an evolutionary genetic algorithm that takes genetic diversity into consideration through a speciation routine, giving time for each individual of the population to be optimized within its own niche. With this method, the system is able to present better results and improves the results compared with other template approaches. The tests considered real data from the stock market and against state-of-the-art solutions, namely the ones using a grid of weights which does not have a fixed size and non-speciated approaches. During the testing period, the presented solution had a return of 21.3% compared to 10.9% of the existing approaches. The use of speciation was able to increase the returns of some results as genetic diversity was taken into consideration.

Download or read book Computational Intelligence written by Juan Julián Merelo and published by Springer Nature. This book was released on 2021-07-01 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: This present book includes a set of selected revised and extended versions of the best papers presented at the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) – held in Vienna, Austria, from 17 to 19 September 2019. The authors focus on three outstanding fields of Computational Intelligence through the selected panel, namely Evolutionary Computation, Fuzzy Computation and Neural Computation. Besides presenting the recent advances of the selected areas, the book aims to aggregate new and innovative solutions for confirmed researchers and, on the other hand, to provide a source of information and/or inspiration for young interested researchers or learners in the ever-expanding and current filed of Computational Intelligence. It constitutes a precious provision of knowledge for individual researchers as well as represents a valuable sustenance for collective use in academic libraries (of universities and engineering schools) relating innovative techniques in various fields of applications.

Download or read book Intelligent Data Engineering and Automated Learning IDEAL 2017 written by Hujun Yin and published by Springer. This book was released on 2017-10-23 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017, held in Guilin, China, in October/November 2017. The 65 full papers presented were carefully reviewed and selected from 110 submissions. These papers provided a sample of latest research outcomes in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, deep learning neural networks, probabilistic modelling, particle swarm intelligence, big data analytics, and applications in image recognition, regression, classification, clustering, medical and biological modelling and prediction, text processing and social media analysis.

Download or read book Genetic Algorithms and Investment Strategies written by Richard J. Bauer and published by John Wiley & Sons. This book was released on 1994-03-31 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: When you combine nature's efficiency and the computer's speed, thefinancial possibilities are almost limitless. Today's traders andinvestment analysts require faster, sleeker weaponry in today'sruthless financial marketplace. Battles are now waged at computerspeed, with skirmishes lasting not days or weeks, but mere hours.In his series of influential articles, Richard Bauer has shown whythese professionals must add new computerized decision-making toolsto their arsenal if they are to succeed. In Genetic Algorithms andInvestment Strategies, he uniquely focuses on the most powerfulweapon of all, revealing how the speed, power, and flexibility ofGAs can help them consistently devise winning investmentstrategies. The only book to demonstrate how GAs can workeffectively in the world of finance, it first describes thebiological and historical bases of GAs as well as othercomputerized approaches such as neural networks and chaos theory.It goes on to compare their uses, advantages, and overallsuperiority of GAs. In subsequently presenting a basic optimizationproblem, Genetic Algorithms and Investment Strategies outlines theessential steps involved in using a GA and shows how it mimicsnature's evolutionary process by moving quickly toward anear-optimal solution. Introduced to advanced variations ofessential GA procedures, readers soon learn how GAs can be usedto: * Solve large, complex problems and smaller sets of problems * Serve the needs of traders with widely different investmentphilosophies * Develop sound market timing trading rules in the stock and bondmarkets * Select profitable individual stocks and bonds * Devise powerful portfolio management systems Complete with information on relevant software programs, a glossaryof GA terminology, and an extensive bibliography coveringcomputerized approaches and market timing, Genetic Algorithms andInvestment Strategies unveils in clear, nontechnical language aremarkably efficient strategic decision-making process that, whenimaginatively used, enables traders and investment analysts to reapsignificant financial rewards.