Literatura académica sobre el tema "Trading automatisé"
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Artículos de revistas sobre el tema "Trading automatisé"
Oetama, Raymond Sunardi y Raymond Sunardi Oetama. "DEVELOPING SALES FORCE AUTOMATION PROTOTYPE AT INDONESIAN FURNITURE TRADING COMPANY". IJISCS (International Journal of Information System and Computer Science) 7, n.º 3 (10 de octubre de 2023): 182. http://dx.doi.org/10.56327/ijiscs.v7i3.1537.
Texto completoAl-Sulaiman, Talal. "Review of Recent Research Directions and Practical Implementation of Low-Frequency Algorithmic Trading". American Journal of Financial Technology and Innovation 2, n.º 1 (26 de febrero de 2024): 1–14. http://dx.doi.org/10.54536/ajfti.v2i1.2354.
Texto completoLi, Yeti, Catherine Burns y Rui Hu. "Representing Stages and Levels of Automation on a Decision Ladder". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, n.º 1 (septiembre de 2016): 328–32. http://dx.doi.org/10.1177/1541931213601074.
Texto completoMaciel, Leandro y Rosangela Ballini. "Efeitos do pregão eletrônico sobre a eficiência e a volatilidade condicional no mercado de ações brasileiro". Brazilian Review of Finance 17, n.º 1 (15 de octubre de 2019): 80. http://dx.doi.org/10.12660/rbfin.v17n1.2019.76684.
Texto completoTzelgov, Joseph. "Trading automatic/nonautomatic for unconscious/conscious". Behavioral and Brain Sciences 25, n.º 3 (junio de 2002): 356–57. http://dx.doi.org/10.1017/s0140525x02500066.
Texto completoKazantsev, Dmitry A. y Natalya A. Mikhaleva. "PROCUREMENT AUTOMATION AS THE FUTURE OF THE CONTRACT SYSTEM". RUDN Journal of Law 24, n.º 1 (15 de diciembre de 2020): 137–57. http://dx.doi.org/10.22363/2313-2337-2020-24-1-137-157.
Texto completoTkachenko, Olexandr, Mykyta Kutsenko y Hlib Fleshner. "RODOFEBISU – Trading Support System". Digital Platform: Information Technologies in Sociocultural Sphere 5, n.º 2 (27 de diciembre de 2022): 387–400. http://dx.doi.org/10.31866/2617-796x.5.2.2022.270145.
Texto completoINAGAKI, Toshiyuki. "Adaptive Automation: Sharing and Trading of Control". Proceedings of the Transportation and Logistics Conference 2001.10 (2001): 79–84. http://dx.doi.org/10.1299/jsmetld.2001.10.79.
Texto completoKim, Jin-Gyeom y Bowon Lee. "Automatic P2P Energy Trading Model Based on Reinforcement Learning Using Long Short-Term Delayed Reward". Energies 13, n.º 20 (14 de octubre de 2020): 5359. http://dx.doi.org/10.3390/en13205359.
Texto completoPlotnikov, Arkadiy P., Victor P. Glazkov y Roman A. Shishlov. "Modification of long-term volatility trading methods based on a delta-neutral strategy". Vestnik of Samara University. Economics and Management 12, n.º 3 (25 de noviembre de 2021): 70–79. http://dx.doi.org/10.18287/2542-0461-2021-12-3-70-79.
Texto completoTesis sobre el tema "Trading automatisé"
Tran, Trung-Minh. "Contributions to Agent-Based Modeling and Its Application in Financial Market". Electronic Thesis or Diss., Université Paris sciences et lettres, 2023. http://www.theses.fr/2023UPSLP022.
Texto completoThe analysis of complex models such as financial markets helps managers to make reasonable policies and traders to choose effective trading strategies. Agent-based modeling is a computational methodology to model complex systems and analyze the influence of different assumptions on the behaviors of agents. In the scope of this thesis, we consider a financial market model that includes 3 types of agent: technical agents, fundamental agents and noise agents. We start with the technical agent with the challenge of optimizing a trading strategy based on technical analysis through an automated trading system. Then, the proposed optimization methods are applied with suitable objective functions to optimize the parameters for the ABM model. The study was conducted with a simple ABM model including only noise agents, then the model was extended to include different types of agents. The first part of the thesis investigates the trading behavior of technical agents. Different approaches are introduced such as: Genetic Algorithm, Bayesian Optimization and Deep Reinforcement Learning. The trading strategies are built based on a leading indicator, Relative Strength Index, and two lagging indicators, Bollinger Band and Moving Average Convergence-Divergence. Multiple experiments are performed in different markets including: cryptocurrency market, stock market and crypto futures market. The results show that optimized strategies from proposed approaches can generate higher returns than their typical form and Buy and Hold strategy. Using the results from the optimization of trading strategies, we propose a new approach to optimize the parameters of the agent-based model. The second part of the thesis presents an application of agent-based modeling to the stock market. As a result, we have shown that ABM models can be optimized using the Bayesian Optimization method with multiple objective functions. The stylized facts of the actual market can be reproduced by carefully constructing the objective functions of the agent. Our work includes the development of an environment, the behaviors of different agents and their interactions. Bayesian optimization method with Kolmogorov-Smirnov test as objective function has shown advantages and potential in estimating an optimal set of parameters for an artificial financial market model. The model we propose is capable of reproducing the stylized facts of the real market. Furthermore, a new stylized fact about the proportion of traders in the market is presented. With empirical data of the Dow Jones Industrial Average index, we found that fundamental traders account for 9%-11% of all traders in the stock market. In the future, more research will be done to improve the model and optimization methods, such as applying machine learning models, multi-agent reinforcement learning or considering the application in different markets and traded instruments
Larsen, Fredrik. "Automatic stock market trading based on Technical Analysis". Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8707.
Texto completoThe theory of technical analysis suggests that future stock price developement can be foretold by analyzing historical price fluctuations and identifying repetitive patterns. A computerized system, able to produce trade recommendations based on different aspects of this theory, has been implemented. The system utilizes trading agents, trained using machine learning techniques, capable of producing unified buy and sell signals. It has been evaluated using actual trade data from the Oslo Børs stock exchange over the period 1999-2006. Compared to the simple strategy of buying and holding, some of the agents have proven to yield good results, both during years with extremely good stock market returns, as well as during times of recession. In spite of the positive performance, anomalous results do exist and call for cautionous use of the system’s recommendations. Combining them with fundamental analysis appears to be a safe approach to achieve succesful stock market trading.
Sauer, Václav. "Tvorba obchodní strategie pro měnový trh". Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2017. http://www.nusl.cz/ntk/nusl-318623.
Texto completoRaykhel, Ilya. "Real-time automatic price prediction for eBay online trading /". Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2697.pdf.
Texto completoRaykhel, Ilya Igorevitch. "Real-Time Automatic Price Prediction for eBay Online Trading". BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1631.
Texto completoTrnik, Erik. "Návrh a optimalizace automatického obchodního systému pro forex". Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2017. http://www.nusl.cz/ntk/nusl-318583.
Texto completoParro, Mattia <1991>. "Analisi tecnica e trading systems - sviluppo di un sistema di trading automatico basato sulla conformazione grafica a bandiera". Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/18525.
Texto completoPINTO, THIAGO REZENDE. "APPLICATION OF NONLINEAR MODELS FOR AUTOMATIC TRADING IN THE BRAZILIAN STOCK MARKET". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9141@1.
Texto completoEsta dissertação tem por objetivo comparar o desempenho de modelos não lineares de previsão de retornos em 10 ativos do mercado acionário brasileiro. Entre os modelos escolhidos, pode-se citar o STAR-Tree, que combina conceitos da metodologia STAR (Smooth Transition AutoRegression) e do algoritmo CART (Classification And Regression Trees), tendo como resultado final uma regressão com transição suave entre múltiplos regimes. A especificação do modelo é feita através de testes de hipótese do tipo Multiplicador de Lagrange que indicam o nó a ser dividido e a variável explicativa correspondente. A estimação dos parâmetros é feita pelo método de Mínimos Quadrados Não Lineares para determinar o valor dos parâmetros lineares e não lineares. Redes Neurais, modelos ARMAX (estes lineares) e ainda o método Naive também foram incluídos na análise. Os resultados das previsões foram avaliados a partir de medidas estatísticas e financeiras e se basearam em um negociador automático que informa o instante correto de assumir uma posição comprada ou vendida em cada ativo. Os melhores desempenhos foram alcançados pelas Redes Neurais, pelos modelos ARMAX e pela forma de previsão ARC (Adaptative Regime Combination) derivada da metodologia STAR-Tree, sendo ambos ainda superiores ao retorno das ações durante o período de teste
The goal of this dissertation is to compare the performance of non linear models to forecast return on 10 equities in the Brazilian Stock Market. Among the chosen ones, it can be cited the STAR-Tree, which matches concepts from the STAR (Smooth Transition AutoRegression) methodology and the CART (Classification And Regression Trees) algorithm, having as the resultant structure a regression with smooth transition among multiple regimes. The model specification is done by Lagrange Multiplier hypothesis tests that indicate the node to be splitted and the corresponding explanatory variable. The parameter estimation is done by the Non Linear Least Squares method that determine the linear and non linear parameters. Neural Netwoks, ARMAX models (these ones linear) and the Naive method were also included in the analysis. The forecasting results were calculated using statistical and financial measures and were based on an automatic negociator that signaled the right instant to take a short or a long position in each stock. The best results were reached by the Neural Networks, ARMAX models and ARC (Adaptative Regime Combination ) forecasting method derived from STAR-Tree, with all of them performing better then the equity return during the test period.
EPPRECHT, CAMILA ROSA. "MEAN AND REALIZED VOLATILITY SMOOTH TRANSITION MODELS APPLIED TO RETURN FORECASTING AND AUTOMATIC TRADING". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13209@1.
Texto completoCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
O principal objetivo desta dissertação é comparar o desempenho de modelos lineares e não-lineares de previsão de retornos de 23 ativos do mercado acionário americano. Propõe-se o modelo STAR-Tree Heterocedástico, que faz uso da metodologia do STAR-Tree (Smooth Transition AutoRegression Tree) aplicada a séries temporais heterocedásticas. Com a disponibilidade de dados de retorno e da volatilidade realizada de ações intra-diários, as séries de retornos são transformadas através da divisão de cada retorno pela sua volatilidade realizada. A série transformada apresenta variância constante. O modelo é uma combinação da metodologia STAR (Smooth Transition AutoRegression) e do algoritmo CART (Classification and Regression Tree). O modelo resultante pode ser interpretado como uma regressão de múltiplos regimes com transição suave. A especificação do modelo é feita através de testes de Multiplicadores de Lagrange, que indicam o nó a ser dividido e a variável de transição correspondente. Os modelos de comparação usados são o modelo Média, o método Naive, modelos lineares ARX e Redes Neurais. As previsões dos modelos foram avaliadas através de medidas estatísticas e financeiras. Os resultados financeiros baseam-se em uma regra de negociação automática que informa o momento de comprar e vender cada ativo. O modelo STAR-Tree Heterocedástico teve resultados estatísticos equivalentes aos dos outros modelos, porém apresentou um desempenho financeiro superior para a maioria das séries. A volatilidade realizada também foi estimada usando a metodologia STAR-Tree, e sua previsão foi utilizada para fazer uma análise de alavancagem financeira.
The main goal of this dissertation is to compare the performance of linear and nonlinear models to forecast 23 assets of the American Stocks Market. The Heteroscedastic STAR-Tree Model is proposed using the STAR- Tree (Smooth Transition AutoRegression Tree) methodology applied to heteroscedastic time series. As assets returns and realized volatility intraday data are available, the returns series are transformed by dividing each return by its realized volatility, which gives homocedastic series. The model is a combination of the STAR (Smooth Transition AutoRegression) methodology and the CART (Classification and Regression Tree) algorithm. The resulting model can be interpreted as a smooth transition multiple regime regression. The model specification is done by Lagrange Multiplier tests that indicate the node to be split and the corresponding transition variable. The comparison models used are the Mean model, Naive method, ARX linear models and Neural Networks. The forecasting models were evaluated through statistical and financial measures. The financial results are based on an automatic trading rule that signals buy and hold moments in each stock. The Heteroscedastic STAR-Tree Model statistical performance was equivalent to the other models, however its financial performance was superior for most of the series. The STAR-Tree methodology was also applied for forecasting the realized volatility, and the forecasts were used in financial leverage analysis.
Myslivec, Oldřich. "Využití technické analýzy při tvorbě obchodních systémů". Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-11194.
Texto completoLibros sobre el tema "Trading automatisé"
Chan, Jacinta. Automation of Trading Machine for Traders. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9945-9.
Texto completoStock exchange automation. New York: Garland, 1994.
Buscar texto completoService, U. S. Customs. Customer satisfaction report: Commercial importers. [Washington, DC]: U.S. Customs Service, 1997.
Buscar texto completoService, U. S. Customs. Customer satisfaction report: Commercial importers. [Washington, DC]: U.S. Customs Service, 1997.
Buscar texto completoService, U. S. Customs. Customer satisfaction report: Commercial importers. [Washington, DC]: U.S. Customs Service, 1997.
Buscar texto completoCommittee, President's Management Council (U S. ). Electronic Processes Initiatives. Electronic commerce for buyers and sellers: A strategic plan for electronic federal purchasing and payment. [Washington, D.C: The Council?], 1998.
Buscar texto completoAmerican Bar Association. Committee on Law and Accounting. y American Bar Association. Committee on Federal Regulation of Securities., eds. The MD&A and the year 2000. [Chicago, Ill.]: ABA Section of Business Law, 1999.
Buscar texto completoPLC, Euromoney Institutional Investor. Best practice in foreign exchange markets. 3a ed. [New York]: FX Alliance, 2007.
Buscar texto completoMunshi, Jamal. Stock Exchange Automation. Taylor & Francis Group, 2017.
Buscar texto completoChan, Jacinta. Automation of Trading Machine for Traders: How to Develop Trading Models. Palgrave Pivot, 2019.
Buscar texto completoCapítulos de libros sobre el tema "Trading automatisé"
Kera, Denisa Reshef. "Governance ‘Trading Zones’". En Algorithms and Automation, 192–202. London: Routledge India, 2023. http://dx.doi.org/10.4324/9781003189411-17.
Texto completoConlan, Chris. "Organizing and Automating Scripts". En Automated Trading with R, 155–60. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2178-5_10.
Texto completoBensdorp, Laurens. "The Automation of Tharp Think". En Trading Beyond the Matrix, 25–35. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119204770.ch2.
Texto completoChan, Jacinta. "Introduction to Model Trading". En Automation of Trading Machine for Traders, 1–18. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9945-9_1.
Texto completoChan, Jacinta. "Technical Indicators: Market Technicians Trading Tools". En Automation of Trading Machine for Traders, 19–36. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9945-9_2.
Texto completoChan, Jacinta. "Development of Technical Algorithm Trading Systems". En Automation of Trading Machine for Traders, 45–66. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9945-9_4.
Texto completoChan, Jacinta. "Development of Artificial Intelligence Algorithm Trading Systems". En Automation of Trading Machine for Traders, 67–79. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9945-9_5.
Texto completoChan, Jacinta. "Market Data Analysis". En Automation of Trading Machine for Traders, 37–43. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9945-9_3.
Texto completoChan, Jacinta. "Test Results of the Profitability of New Trading Model". En Automation of Trading Machine for Traders, 81–88. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9945-9_6.
Texto completoChan, Jacinta. "Evaluation and Stops". En Automation of Trading Machine for Traders, 89–104. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9945-9_7.
Texto completoActas de conferencias sobre el tema "Trading automatisé"
Padovani, Matheus Rosisca y João Roberto Bertini Junior. "A stock trading algorithm based on trend forecasting and time series classification". En Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18272.
Texto completoSihananto, Andreas Nugroho, Anggraini Puspita Sari, Muhammad Eko Prasetyo, Mochammad Yanuar Fitroni, Wahyu Nugroho Gultom y Henni Endah Wahanani. "Reinforcement Learning for Automatic Cryptocurrency Trading". En 2022 IEEE 8th Information Technology International Seminar (ITIS). IEEE, 2022. http://dx.doi.org/10.1109/itis57155.2022.10010206.
Texto completoLi, Ziqian, Caixia Wang, Xiaoning Ye, Wei Wang y Shuang Hao. "China's Green Certificate Trading Mode Design and Trading Volume Evaluation Model Establishment". En 2019 Chinese Automation Congress (CAC). IEEE, 2019. http://dx.doi.org/10.1109/cac48633.2019.8996576.
Texto completoDekker, Pieter y Vasilios Andrikopoulos. "Automating Bulk Commodity Trading Using Smart Contracts". En 2020 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS). IEEE, 2020. http://dx.doi.org/10.1109/dapps49028.2020.00006.
Texto completoLi, Yi, Yuanjie Ni, Wei Liu y Wenxing Yan. "Two Patterns of Knowledge Trading". En Control and Automation 2014. Science & Engineering Research Support soCiety, 2014. http://dx.doi.org/10.14257/astl.2014.76.22.
Texto completoAbouloula, Khalid y Salah-ddine Krit. "Using a Robot Trader for Automatic Trading". En the Fourth International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3234698.3234701.
Texto completoGulin, S. V. y A. G. Pirkin. "FEATURES OF BUSINESS-PROCESSES IN THE CREATION OF ELECTROTECHNOLOGICAL SYSTEMS FOR THE AGRICULTURAL INDUSTRIAL COMPLEX". En INNOVATIVE TECHNOLOGIES IN SCIENCE AND EDUCATION. DSTU-Print, 2020. http://dx.doi.org/10.23947/itno.2020.357-362.
Texto completoChau, Tan, Minh-Tri Nguyen, Duc-Vu Ngo, Anh-Duc T. Nguyen y Trong-Hop Do. "Deep Reinforcement Learning methods for Automation Forex Trading". En 2022 RIVF International Conference on Computing and Communication Technologies (RIVF). IEEE, 2022. http://dx.doi.org/10.1109/rivf55975.2022.10013861.
Texto completoKaur, Inderpreet, Yashica Chauhan, Utsav Gupta y Sagar Malik. "Investigating the Use of Automation in Cryptocurrency Trading". En 2024 2nd International Conference on Disruptive Technologies (ICDT). IEEE, 2024. http://dx.doi.org/10.1109/icdt61202.2024.10489073.
Texto completoWorasucheep, Chukiat, Sirapop Nuannimnoi, Ratchanon Khamvichit y Papon Attagonwantana. "An automatic stock trading system using Particle Swarm Optimization". En 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2017. http://dx.doi.org/10.1109/ecticon.2017.8096283.
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