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Статті в журналах з теми "Expert trading system"
Chen, K. C., and Ting‐peng Liang. "PROTRADER: An Expert System for Program Trading." Managerial Finance 15, no. 5 (May 1989): 1–6. http://dx.doi.org/10.1108/eb013623.
Повний текст джерелаWang, Pin, and Hai Ping Huang. "Empirical Analysis on Expert Tendency System MA, MACD and MTM." Advanced Materials Research 926-930 (May 2014): 3802–5. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3802.
Повний текст джерелаSimutis, Rimvydas, and Saulius Masteika. "Intelligent Stock Trading Systems Using Fuzzy-Neural Networks and Evolutionary Programming Methods." Solid State Phenomena 97-98 (April 2004): 59–64. http://dx.doi.org/10.4028/www.scientific.net/ssp.97-98.59.
Повний текст джерелаHuang, Hai Ping, and Pin Wang. "Analyses on W&R and BIAS Expert System of Stock-Market Software." Applied Mechanics and Materials 433-435 (October 2013): 2391–94. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.2391.
Повний текст джерелаWu, Jimmy Ming-Tai, Lingyun Sun, Gautam Srivastava, Vicente Garcia Diaz, and Jerry Chun-Wei Lin. "A Stock Trading Expert System Established by the CNN-GA-Based Collaborative System." International Journal of Data Warehousing and Mining 18, no. 1 (January 1, 2022): 1–19. http://dx.doi.org/10.4018/ijdwm.309957.
Повний текст джерелаLee, Woonyeol, and Qiang Ma. "Discovering Expert Traders on Social Trading Services." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 2 (March 20, 2018): 224–35. http://dx.doi.org/10.20965/jaciii.2018.p0224.
Повний текст джерелаHuang, Hai Ping, and Pin Wang. "Discussions on Securities Software Expert System MA and RSI." Advanced Materials Research 798-799 (September 2013): 757–60. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.757.
Повний текст джерелаSimutis, R. "Human Skill Based Expert System for a Stock Trading Process." IFAC Proceedings Volumes 31, no. 24 (September 1998): 54–58. http://dx.doi.org/10.1016/s1474-6670(17)38506-3.
Повний текст джерелаTong-Seng, Quah, Chew-Lim Tan, Hoon-Heng Teh, and Bobby S. Sriniivasan. "Utilizing a neural logic expert system in currency option trading." Expert Systems with Applications 9, no. 2 (January 1995): 213–22. http://dx.doi.org/10.1016/0957-4174(94)00063-2.
Повний текст джерелаHuang, Xiao Ming, Hai Ping Huang, and Pin Wang. "Discussion on the Anti-Trend Expert Systems of RSI, BIAS, KDJ and W&R." Advanced Materials Research 926-930 (May 2014): 3786–89. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3786.
Повний текст джерелаДисертації з теми "Expert trading system"
Kaucic, Massimiliano. "Evolutionary computation for trading systems." Doctoral thesis, Università degli studi di Trieste, 2008. http://hdl.handle.net/10077/3093.
Повний текст джерелаEvolutionary computations, also called evolutionary algorithms, consist of several heuristics, which are able to solve optimization tasks by imitating some aspects of natural evolution. They may use different levels of abstraction, but they are always working on populations of possible solutions for a given task. The basic idea is that if only those individuals of a population which meet a certain selection criteria reproduce, while the remaining individuals die, the population will converge to those individuals that best meet the selection criteria. If imperfect reproduction is added the population can begin to explore the search space and will move to individuals that have an increased selection probability and that hand down this property to their descendants. These population dynamics follow the basic rule of the Darwinian evolution theory, which can be described in short as the “survival of the fittest”. Although evolutionary computations belong to a relative new research area, from a computational perspective they have already showed some promising features such as: • evolutionary methods reveal a remarkable balance between efficiency and efficacy; • evolutionary computations are well suited for parameter optimisation; • this type of algorithms allows a wide variety of extensions and constraints that cannot be provided in traditional methods; • evolutionary methods are easily combined with other optimization techniques and can also be extended to multi-objective optimization. From an economic perspective, these methods appear to be particularly well suited for a wide range of possible financial applications, in particular in this thesis I study evolutionary algorithms • for time series prediction; • to generate trading rules; • for portfolio selection. It is commonly believed that asset prices are not random, but are permeated by complex interrelations that often translate in assets mispricing and may give rise to potentially profitable opportunities. Classical financial approaches, such as dividend discount models or even capital asset pricing theories, are not able to capture these market complexities. Thus, in the last decades, researchers have employed intensive econometric and statistical modeling that examine the effects of a multitude of variables, such as price- earnings ratios, dividend yields, interest rate spreads and changes in foreign exchange rates, on a broad and variegated range of stocks at the same time. However, these models often result in complex functional forms difficult to manage or interpret and, in the worst case, are solely able to fit a given time series but are useless to predict it. Parallelly to quantitative approaches, other researchers have focused on the impact of investor psychology (in particular, herding and overreaction) and on the consequences of considering informed signals from management and analysts, such as share repurchases and analyst recommendations. These theories are guided by intuition and experience, and thus are difficult to be translated into a mathematical environment. Hence, the necessity to combine together these point of views in order to develop models that examine simultaneously hundreds of variables, including qualitative informations, and that have user friendly representations, is urged. To this end, the thesis focuses on the study of methodologies that satisfy these requirements by integrating economic insights, derived from academic and professional knowledge, and evolutionary computations. The main task of this work is to provide efficient algorithms based on the evolutionary paradigm of biological systems in order to compute optimal trading strategies for various profit objectives under economic and statistical constraints. The motivations for constructing such optimal strategies are: i) the necessity to overcome data-snooping and supervisorship bias in order to learn to predict good trading opportunities by using market and/or technical indicators as features on which to base the forecasting; ii) the feasibility of using these rules as benchmark for real trading systems; iii) the capability of ranking quantitatively various markets with respect to their profitability according to a given criterion, thus making possible portfolio allocations. More precisely, I present two algorithms that use artificial expert trading systems to predict financial time series, and a procedure to generate integrated neutral strategies for active portfolio management. The first algorithm is an automated procedure that simultaneously selects variables and detect outliers in a dynamic linear model using information criteria as objective functions and diagnostic tests as constraints for the distributional properties of errors. The novelties are the automatic implementation of econometric conditions in the model selection step, making possible a better exploration of the solution space on one hand, and the use of evolutionary computations to efficiently generate a reduction procedure from a very large number of independent variables on the other hand. In the second algorithm, the novelty is given by the definition of evolutionary learning in financial terms and its use in a multi-objective genetic algorithm in order to generate technical trading systems. The last tool is based on a trading strategy on six assets, where future movements of each variable are obtained by an evolutionary procedure that integrates various types of financial variables. The contribution is given by the introduction of a genetic algorithm to optimize trading signals parameters and the way in which different informations are represented and collected. In order to compare the contribution of this work to “classical” techniques and theories, the thesis is divided into three parts. The first part, titled Background, collects Chapters 2 and 3. Its purpose is to provide an introduction to search/optimization evolutionary techniques on one hand, and to the theories that relate the predictability in financial markets with the concept of efficiency proposed over time by scholars on the other hand. More precisely, Chapter 2 introduces the basic concepts and major areas of evolutionary computation. It presents a brief history of three major types of evolutionary algorithms, i.e. evolution strategies, evolutionary programming and genetic algorithms, and points out similarities and differences among them. Moreover it gives an overview of genetic algorithms and describes classical and genetic multi-objective optimization techniques. Chapter 3 first presents an overview of the literature on the predictability of financial time series. In particular, the extent to which the efficiency paradigm is affected by the introduction of new theories, such as behavioral finance, is described in order to justify the market forecasting methodologies developed by practitioners and academics in the last decades. Then, a description of the econometric and financial techniques that will be used in conjunction with evolutionary algorithms in the successive chapters is provided. Special attention is paid to economic implications, in order to highlight merits and shortcomings from a practitioner perspective. The second part of the thesis, titled Trading Systems, is devoted to the description of two procedures I have developed in order to generate artificial trading strategies on the basis of evolutionary algorithms, and it groups Chapters 4 and 5. In particular, chapter 4 presents a genetic algorithm for variable selection by minimizing the error in a multiple regression model. Measures of errors such as ME, RMSE, MAE, Theil’s inequality coefficient and CDC are analyzed choosing models based on AIC, BIC, ICOMP and similar criteria. Two components of penalty functions are taken in analysis- level of significance and Durbin Watson statistics. Asymptotic properties of functions are tested on several financial variables including stocks, bonds, returns, composite prices indices from the US and the EU economies. Variables with outliers that distort the efficiency and consistency of estimators are removed to solve masking and smearing problems that they may cause in estimations. Two examples complete the chapter. In both cases, models are designed to produce short-term forecasts for the excess returns of the MSCI Europe Energy sector on the MSCI Europe index and a recursive estimation- window is used to shed light on their predictability performances. In the first application the data-set is obtained by a reduction procedure from a very large number of leading macro indicators and financial variables stacked at various lags, while in the second the complete set of 1-month lagged variables is considered. Results show a promising capability to predict excess sector returns through the selection, using the proposed methodology, of most valuable predictors. In Chapter 5 the paradigm of evolutionary learning is defined and applied in the context of technical trading rules for stock timing. A new genetic algorithm is developed by integrating statistical learning methods and bootstrap to a multi-objective non dominated sorting algorithm with variable string length, making possible to evaluate statistical and economic criteria at the same time. Subsequently, the chapter discusses a practical case, represented by a simple trading strategy where total funds are invested in either the S&P 500 Composite Index or in 3-month Treasury Bills. In this application, the most informative technical indicators are selected from a set of almost 5000 signals by the algorithm. Successively, these signals are combined into a unique trading signal by a learning method. I test the expert weighting solution obtained by the plurality voting committee, the Bayesian model averaging and Boosting procedures with data from the the S&P 500 Composite Index, in three market phases, up-trend, down- trend and sideways-movements, covering the period 2000–2006. In the third part, titled Portfolio Selection, I explain how portfolio optimization models may be constructed on the basis of evolutionary algorithms and on the signals produced by artificial trading systems. First, market neutral strategies from an economic point of view are introduced, highlighting their risks and benefits and focusing on their quantitative formulation. Then, a description of the GA-Integrated Neutral tool, a MATLAB set of functions based on genetic algorithms for active portfolio management, is given. The algorithm specializes in the parameter optimization of trading signals for an integrated market neutral strategy. The chapter concludes showing an application of the tool as a support to decisions in the Absolute Return Interest Rate Strategies sub-fund of Generali Investments.
Gli “algoritmi evolutivi”, noti anche come “evolutionary computations” comprendono varie tecniche di ottimizzazione per la risoluzione di problemi, mediante alcuni aspetti suggeriti dall’evoluzione naturale. Tali metodologie sono accomunate dal fatto che non considerano un’unica soluzione alla volta, bens`ı trattano intere popolazioni di possibili soluzioni per un dato problema. L’idea sottostante `e che, se un algoritmo fa evolvere solamente gli individui di una data popolazione che soddisfano a un certo criterio di selezione, e lascia morire i restanti, la popolazione converger`a agli individui che meglio soddisfano il criterio di selezione. Con una selezione non ottimale, cio`e una che ammette pure soluzioni sub-ottimali, la popolazione rappresenter` a meglio l’intero spazio di ricerca e sar`a in grado di individuare in modo pi`u consistente gli individui migliori da far evolvere. Queste dinamiche interne alle popolazioni seguono i principi Darwiniani dell’evoluzione, che si possono sinteticamente riassumere nella dicitura “la sopravvivenza del più adatto”. Sebbene gli algoritmi evolutivi siano un’area di ricerca relativamente nuova, dal punto di vista computazionale hanno dimostrato alcune caratteristiche interessanti fra cui le seguenti: • permettono un notevole equilibrio tra efficienza ed efficacia; • sono particolarmente indicati per la configurazione dei parametri in problemi di ottimizzazione; • consentono una flessibilit`a nella definizione matematica dei problemi e dei vincoli che non si trova nei metodi tradizionali; • possono facilmente essere integrati con altre tecniche di ottimizzazione ed essere essere modificati per risolvere problemi multi-obiettivo. Dal un punto di vista economico, l’applicazione di queste procedure pu`o risultare utile specialmente in campo finanziario. In particolare, nella mia tesi ho studiato degli algoritmi evolutivi per • la previsione di serie storiche finanziarie; • la costruzione di regole di trading; • la selezione di portafogli. Da un punto di vista pi`u ampio, lo scopo di questa ricerca `e dunque l’analisi dell’evoluzione e della complessit`a dei mercati finanziari. In tal senso, dal momento che i prezzi non seguono andamenti puramente casuali, ma sono governati da un insieme molto articolato di eventi correlati, i modelli e le teorie classiche, come i dividend discount model e le varie capital asset pricing theories, non sono pi`u sufficienti per determinare potenziali opportunit`a di profitto. A tal fine, negli ultimi decenni, alcuni ricercatori hanno sviluppato una vasta gamma di modelli econometrici e statistici in grado di esaminare contemporaneamente le relazioni e gli effetti di centinaia di variabili, come ad esempio, price-earnings ratios, dividendi, differenziali fra tassi di interesse e variazioni dei tassi di cambio, per una vasta gamma di assets. Comunque, questo approccio, che fa largo impiego di strumenti di calcolo, spesso porta a dei modelli troppo complicati per essere gestiti o interpretati, e, nel peggiore dei casi, pur essendo ottimi per descrivere situazioni passate, risultano inutili per fare previsioni. Parallelamente a questi approcci quantitativi, si `e manifestato un grande interesse sulla psicologia degli investitori e sulle conseguenze derivanti dalle opinioni di esperti e analisti nelle dinamiche del mercato. Questi studi sono difficilmente traducibili in modelli matematici e si basano principalmente sull’intuizione e sull’esperienza. Da qui la necessit` a di combinare insieme questi due punti di vista, al fine di sviluppare modelli che siano in grado da una parte di trattare contemporaneamente un elevato numero di variabili in modo efficiente e, dall’altra, di incorporare informazioni e opinioni qualitative. La tesi affronta queste tematiche integrando le conoscenze economiche, sia accademiche che professionali, con gli algoritmi evolutivi. Pi`u pecisamente, il principale obiettivo di questo lavoro `e lo sviluppo di algoritmi efficienti basati sul paradigma dell’evoluzione dei sistemi biologici al fine di determinare strategie di trading ottimali in termini di profitto e di vincoli economici e statistici. Le ragioni che motivano lo studio di tali strategie ottimali sono: i) la necessit`a di risolvere i problemi di data-snooping e supervivorship bias al fine di ottenere regole di investimento vantaggiose utilizzando indicatori di mercato e/o tecnici per la previsione; ii) la possibilità di impiegare queste regole come benchmark per sistemi di trading reali; iii) la capacit`a di individuare gli asset pi`u vantaggiosi in termini di profitto, o di altri criteri, rendendo possibile una migliore allocazione di risorse nei portafogli. In particolare, nella tesi descrivo due algoritmi che impiegano sistemi di trading artificiali per predire serie storiche finanziarie e una procedura di calcolo per strategie integrate neutral market per la gestione attiva di portafogli. Il primo algoritmo `e una procedura automatica che seleziona le variabili e simultaneamente determina gli outlier in un modello dinamico lineare utilizzando criteri informazionali come funzioni obiettivo e test diagnostici come vincoli per le caratteristiche delle distribuzioni degli errori. Le novit`a del metodo sono da una parte l’implementazione automatica di condizioni econometriche nella fase di selezione, consentendo una migliore analisi dello EVOLUTIONARY COMPUTATIONS FOR TRADING SYSTEMS 3 spazio delle soluzioni, e dall’altra parte, l’introduzione di una procedura di riduzione evolutiva capace di riconoscere in modo efficiente le variabili pi`u informative. Nel secondo algoritmo, le novità sono costituite dalla definizione dell’apprendimento evolutivo in termini finanziari e dall’applicazione di un algoritmo genetico multi-obiettivo per la costruzione di sistemi di trading basati su indicatori tecnici. L’ultimo metodo proposto si basa su una strategia di trading su sei assets, in cui le dinamiche future di ciascuna variabile sono ottenute impiegando una procedura evolutiva che integra diverse tipologie di variabili finanziarie. Il contributo è dato dall’impiego di un algoritmo genetico per ottimizzare i parametri negli indicatori tecnici e dal modo in cui le differenti informazioni sono presentate e collegate. La tesi `e organizzata in tre parti. La prima parte, intitolata Background, comprende i Capitoli 2 e 3, ed è intesa a fornire un’introduzione alle tecniche di ricerca/ottimizzazione su base evolutiva da una parte, e alle teorie che si occupano di efficienza e prevedibilit`a dei mercati finanziari dall’altra. Più precisamente, il Capitolo 2 introduce i concetti base e i maggiori campi di studio della computazione evolutiva. In tal senso, si dà una breve presentazione storica di tre dei maggiori tipi di algoritmi evolutivi, ciò e le strategie evolutive, la programmazione evolutiva e gli algoritmi genetici, evidenziandone caratteri comuni e differenze. Il capitolo si chiude con una panoramica sugli algoritmi genetici e sulle tecniche classiche e genetiche di ottimizzazione multi-obiettivo. Il Capitolo 3 affronta nel dettaglio la problematica della prevedibilit`a delle serie storiche finanziarie mettendo in luce, in particolare, quanto il paradigma dell’efficienza sia influenzato dalle pi`u recenti teorie finanziarie, come ad esempio la finanza comportamentale. Lo scopo è quello di dare una giustificazione su basi teoriche per le metodologie di previsione sviluppate nella tesi. Segue una descrizione dei metodi econometrici e di analisi tecnica che nei capitoli successivi verrano impiegati assieme agli algoritmi evolutivi. Una particolare attenzione è data alle implicazioni economiche, al fine di evidenziare i loro meriti e i loro difetti da un punto di vista pratico. La seconda parte, intitolata Trading Systems, raggruppa i Capitoli 4 e 5 ed è dedicata alla descrizione di due procedure che ho sviluppato per generare sistemi di trading artificiali sulla base di algoritmi evolutivi. In particolare, il Capitolo 4 presenta un algortimo genetico per la selezione di variabili attraverso la minimizzazione dell’errore in un modello di regressione multipla. Misure di errore, quali il ME, il RMSE, il MAE, il coefficiente di Theil e il CDC sono analizzate a partire da modelli selezionati sulla scorta di criteri informazionali, come ad esempio AIC, BIC, ICOMP. A livello di vincoli diagnostici, ho considerato una funzione di penalità a due componenti e la statistica di Durbin Watson. Il programma impiega variabili finanziarie di vario tipo, come rendimenti di titoli, bond e prezzi di indici composti ottenuti dalle economie Statunitense ed Europea. Nel caso le serie storiche 4 MASSIMILIANO KAUCIC considerate presentino outliers che distorcono l’efficienza e la consistenza degli stimatori, l’algoritmo `e in grado di individuarle e rimuoverle dalla serie, risolvendo il problema di masking and smearing. Il capitolo si conclude con due applicazioni, in cui i modelli sono progettati per produrre previsioni di breve periodo per l’extra rendimento del settore MSCI Europe Energy sull’indice MSCI Europe e una procedura di tipo recursive estimation-window è utilizzata per evidenziarne le performance previsionali. Nel primo esempio, l’insieme dei dati `e ottenuto estraendo le variabili di interesse da un considerevole numero di indicatori di tipo macro e da variabili finanziarie ritardate rispetto alla variabile dipendente. Nel secondo esempio ho invece considerato l’intero insieme di variabili ritardate di 1 mese. I risultati mostrano una notevole capacità previsiva per l’extra rendimento, individuando gli indicatori maggiormente informativi. Nel Capitolo 5, il concetto di apprendimento evolutivo viene definito ed applicato alla costruzione di regole di trading su indicatori tecnici per lo stock timing. In tal senso, ho sviluppato un algoritmo che integra metodi di apprendimento statistico e di boostrap con un particolare algoritmo multi-obiettivo. La procedura derivante è in grado di valutare contemporaneamente criteri economici e statistici. Per descrivere il suo funzionamento, ho considerato un semplice esempio di trading in cui tutto il capitale è investito in un indice (che nel caso trattato è l’indice S&P 500 Composite) o in un titolo a basso rischio (nell’esempio, i Treasury Bills a 3 mesi). Il segnale finale di trading `e il risultato della selezione degli indicatori tecnici pi`u informativi a partire da un insieme di circa 5000 indicatori e la loro conseguente integrazione mediante un metodo di apprendimento (il plurality voting committee, il bayesian model averaging o il Boosting). L’analisi è stata condotta sull’intervallo temporale dal 2000 al 2006, suddiviso in tre sottoperiodi: il primo rappresenta l’indice in una fase al rialzo, il secondo in una fase al ribasso e il terzo sottoperiodo considera il mercato in una fase di trend non chiara. Nella terza parte, intitolata Portfolio Selection, spiego come si possano costruire modelli di ottimizzazione di portafogli sfruttando le tecniche della computazione evolutiva e i segnali prodotti da sistemi di trading artificiali. A tal fine, dapprima descrivo il significato economico delle strategie neutral market, evidenziandone rischi e benefici, successivamente introduco il GAIntegrated Neutral tool, un insieme di funzioni MATLAB che ho scritto per la gestione attiva di portafogli impiegando algoritmi evolutivi. La procedura calcola la configurazone ottimale dei segnali di trading per una strategia integrata neutral market. Il capitolo si conclude mostrando un’applicazione del tool come supporto alle decisioni nel fondo Absolute Return Interest Rate Strategies di Generali Investments.
XXI Ciclo
1979
Herich, Martin. "Využití automatických obchodních systémů na komoditních trzích." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2015. http://www.nusl.cz/ntk/nusl-224995.
Повний текст джерелаKundračík, Roman. "Návrh a optimalizace obchodní strategie na platformě MetaTrader." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2016. http://www.nusl.cz/ntk/nusl-241558.
Повний текст джерелаShiravi-Khozani, Abdolhossein. "The legal aspect of international countertrade, with reference to the Australian Legal System." Title page, contents and abstract only, 1997. http://web4.library.adelaide.edu.au/theses/09PH/09phs5577.pdf.
Повний текст джерелаVlček, Tomáš. "Podpora v rozhodování pro investičního experta na měnových trzích." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2014. http://www.nusl.cz/ntk/nusl-224707.
Повний текст джерелаMICHNIUK, KAROLINA. "PATTERN RECOGNITION APPLIED TO CHART ANALYSIS. EVIDENCE FROM INTRADAY INTERNATIONAL STOCK MARKETS." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/78837.
Повний текст джерелаEl análisis técnico es una forma sofisticada de técnica de predicción cuya popularidad ha ido variando en el mundo académico y de los negocios. En el pasado, los usuarios eran bastante escépticos respecto de las reglas técnicas de trading y su performance. Todo esto, se encuentra sustentado por la aceptación de la hipótesis del mercado eficiente y descubrimientos empíricos mixtos sobre el análisis técnico, que se mencionan en un número amplio de estudios. El patrón bandera es visto como uno de los patrones gráficos más significativo y difundido entre los analistas técnicos de mercado. El presente estudio valida una regla de trading basada en el desarrollo futuro del reconocimiento gráfico del patrón bandera. La pregunta de investigación se centra en si el análisis técnico basado en el patrón bandera puede batir los índices internacionales de mercado y probar, de esta manera, la ineficiencia de dichos mercados. Los mercados observados son representados por los correspondientes índices DAX (Alemania), DJIA (Estados Unidos) e IBEX (España). El diseño de la regla de trading presenta varios cambios y novedades con respecto a trabajos académicos previos. La amplia muestra usada al considerar los datos intradía, junto con la configuración de algunas variables y la consideración del riesgo, confirman que la regla de trading proporciona mejores, y más ajustadas al riesgo, rentabilidades positivas que la estrategia de buy-and-hold que se utiliza como referencia. Los resultados positivos corroboran la robustez de las conclusiones a las que también se llegan en otros trabajos.
L'anàlisi tècnica és una forma sofisticada de tècnica de predicció, la popularitat de la qual ha anat variant al món acadèmic i dels negocis. En el passat, els usuaris eren bastant escèptics respecte de les regles tècniques de trading i la seva performance. Tot això, es troba sustentat per l'acceptació de la hipòtesi del mercat eficient i descobriments empírics mixts sobre l'anàlisi tècnica, que s'esmenten en un nombre ampli d'estudis. El patró bandera és vist com un dels patrons gràfics més significatiu i difós entre els analistes tècnics de mercat. El present estudi valida una regla de trading basada en el desenvolupament futur del reconeixement gràfic del patró bandera. La pregunta de recerca se centra en si l'anàlisi tècnica basada en el patró bandera pot batre els índexs internacionals de mercat i provar, d'aquesta manera, la ineficiència d'aquests mercats. Els mercats observats són representats pels corresponents índexs DAX (Alemanya), *DJIA (Estats Units) i IBEX (Espanya). El disseny de la regla de trading presenta diversos canvis i novetats pel que fa a treballs acadèmics previs. L'àmplia mostra usada en considerar les dades intradia, juntament amb la configuració d'algunes variables i la consideració del risc, confirmen que la regla de trading proporciona millors, i més ajustades al risc, rendibilitats positives que l'estratègia de buy-and-hold que s'utilitza com a referència. Els resultats positius corroboren la robustesa de les conclusions a les quals també s'arriben en altres treballs.
Michniuk, K. (2017). PATTERN RECOGNITION APPLIED TO CHART ANALYSIS. EVIDENCE FROM INTRADAY INTERNATIONAL STOCK MARKETS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/78837
TESIS
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Повний текст джерелаThesis (Ph.D.) -- University of Adelaide, Institute for International Trade, 2017.
Книги з теми "Expert trading system"
UNCTAD/GATT, International Trade Centre, ed. International marketing and the trading system. Geneva, Switzerland: ITC, 2001.
Знайти повний текст джерелаProfitable patterns for stock trading. Greenville, SC: Traders Press, 1999.
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Знайти повний текст джерелаYoung, Andrew R. Expert advisor programming: Creating automated trading systems in MQL for MetaTrader 4. Nashville, TN: Edgehill Pub., 2010.
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Знайти повний текст джерелаЧастини книг з теми "Expert trading system"
Lipinski, Piotr, and Jerzy J. Korczak. "Performance Measures in an Evolutionary Stock Trading Expert System." In Computational Science - ICCS 2004, 835–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25944-2_108.
Повний текст джерелаLam, Sze Sing, Kai Pui Lam, and Hoi Shing Ng. "Genetic Fuzzy Expert Trading System for Nasdaq Stock Market Timing." In Genetic Algorithms and Genetic Programming in Computational Finance, 197–217. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0835-9_9.
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Повний текст джерелаAbouloula, Khalid, Ali Ou-Yassine, and Salah-ddine Krit. "The management of deep learning algorithms to enhance momentum trading strategies during the time frame to quick detect market of smart money." In Expert Systems in Finance, 203–16. 1 Edition. | New York : Routledge, 2019. | Series: Banking, money and international finance: Routledge, 2019. http://dx.doi.org/10.4324/9780429024061-14.
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Повний текст джерелаde Paiva Abreu, Marcelo, and Winston Fritsch. "Obstacles to Brazilian Export Growth and the Present Multilateral Trade Negotiations." In Developing Countries and the Global Trading System, 437–59. London: Palgrave Macmillan UK, 1989. http://dx.doi.org/10.1007/978-1-349-20417-5_22.
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Повний текст джерелаOyejide, T. Ademola. "Resource Exports, Adjustment Problems and Liberalization Prospects in Nigeria." In Developing Countries and the Global Trading System, 298–315. London: Palgrave Macmillan UK, 1989. http://dx.doi.org/10.1007/978-1-349-20417-5_15.
Повний текст джерелаТези доповідей конференцій з теми "Expert trading system"
Camporeale, Cecilia, Antonio De Nicola, Vittorio Rosato, Maria Luisa Villani, and Umberto Ciorba. "Semantic Modeling of the Emissions Trading System." In 2013 24th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2013. http://dx.doi.org/10.1109/dexa.2013.35.
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Повний текст джерелаLee, Woonyeol, and Qiang Ma. "Whom to Follow on social trading services? A system to support discovering expert traders." In 2015 Tenth International Conference on Digital Information Management (ICDIM). IEEE, 2015. http://dx.doi.org/10.1109/icdim.2015.7381884.
Повний текст джерелаLopes, Fernando, Hugo Algarvio, and Helder Coelho. "Agent-Based Simulation of Retail Electricity Markets: Bilateral Trading Players." In 2013 24th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2013. http://dx.doi.org/10.1109/dexa.2013.50.
Повний текст джерелаAlgarvio, Hugo, Fernando Lopes, Jorge A. M. Sousa, and Joao Lagarto. "Power Producers Trading Electricity in Both Pool and Forward Markets." In 2014 25th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2014. http://dx.doi.org/10.1109/dexa.2014.41.
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Повний текст джерелаBOBOC, Dan, Maria Claudia DIACONEASA, Valentin PĂUNA, and Marilena POTÂRNICHE. "THE IMAGE OF THE ROMANIAN TRADE BALANCE EVOLUTION BETWEEN 2009 AND 2019." In Competitiveness of Agro-Food and Environmental Economy. Editura ASE, 2022. http://dx.doi.org/10.24818/cafee/2020/9/09.
Повний текст джерелаSavic Radovanovic, Radoslava, Aleksandra Aleksic-Agelidis, and Jelena Aleksic Radojkovic. "ZAKONSKI PROPISI U ORGANSKOJ PROIZVODNJI-NACIONALNA I EU REGULATIVA." In XXVI savetovanje o biotehnologiji sa međunarodnim učešćem. University of Kragujevac, Faculty of Agronomy, 2021. http://dx.doi.org/10.46793/sbt26.459sr.
Повний текст джерелаCieślik, Ewa. "THE CENTRAL AND EASTERN EUROPEAN ECONOMIES IN THE ERA OF INDUSTRY 4.0 AND CHINESE DIGITAL SILK ROAD." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2022. http://dx.doi.org/10.47063/ebtsf.2022.0018.
Повний текст джерелаЗвіти організацій з теми "Expert trading system"
Zholdayakova, Saule, Yerdaulet Abuov, Daulet Zhakupov, Botakoz Suleimenova, and Alisa Kim. Toward a Hydrogen Economy in Kazakhstan. Asian Development Bank Institute, October 2022. http://dx.doi.org/10.56506/iwlu3832.
Повний текст джерелаBolton, Laura. Criminal Activity and Deforestation in Latin America. Institute of Development Studies (IDS), December 2020. http://dx.doi.org/10.19088/k4d.2021.003.
Повний текст джерелаPlant Protection and Quarantine: Helping U.S. Agriculture Thrive--Across the Country and Around the World, 2016 Annual Report. U.S. Department of Agriculture, Animal and Plant Health Inspection Service, March 2017. http://dx.doi.org/10.32747/2017.7207241.aphis.
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