Determinación de la factibilidad de la detección de estrategias de operación en el mercado de divisas colombiano utilizando la información del libro de órdenes

dc.contributorHernández Pérez, Germán Jairo
dc.contributorAlgoritmos y Combinatoria (Algos-Un)
dc.creatorCruz Moreno, Andrea Marcela
dc.date.accessioned2023-02-06T21:58:51Z
dc.date.accessioned2023-06-06T23:05:52Z
dc.date.available2023-02-06T21:58:51Z
dc.date.available2023-06-06T23:05:52Z
dc.date.created2023-02-06T21:58:51Z
dc.date.issued2022-12
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/83344
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6651084
dc.description.abstractThe detection of effective, i.e. profitable and efficient, trading strategies requires the identifi cation of predictive patterns in the information provided by the market. Usually the market information is presented as a time series collection of prices (open, close, high and low) and volume that are available with a particular time granularity. This depends on the informa tion provider, e.g. every transaction, every minute, every day, etc. depending on the access level. In this work we have access to an information tool that is not commonly available: The Limit Order Book information for the Colombian Bulk currency market. Order book data provides a valuable source of information in financial markets. For this reason, it is an excellent candidate for the construction of new trading tools and models. Order book representation is an still active study branch in quantitative finance. This work addresses the problem of information visualization of financial data from Colom bian Bulk Currency exchange using two approaches: a heatmap representation, and a Haar Wavelet based representation in order to filter high frequency noise. This requires dealing with a massive amount of data coming from the Colombian Forex Market Limit Order Book, a register with all the buy and sell intentions of the market’s participants. The experimental evaluation shows that the proposed strategies are able to identify frequent patterns within the presented visualizations tools. Furthermore, and more important, it is possible to associate some of those frequent patterns with a trend with a probability greater than 0.5. This result is useful in order to generate buy and sell signals for a trader. (Texto tomado de la fuente)
dc.description.abstractLa detección de estrategias comerciales efectivas, es decir, rentables y eficientes, requiere la identificación de patrones predictivos en la información proporcionada por el mercado. Normalmente el mercado la información se presenta como una colección de series temporales de precios (apertura, cierre, máximo y mínimo) y volumen que están disponibles con una granularidad de tiempo particular. Esto depende del proveedor de información, p. cada transacción, cada minuto, cada día, etc. dependiendo del acceso nivel. En este trabajo tenemos acceso a una herramienta de información que comúnmente no está disponible: El Límite Información del Libro de Órdenes para el mercado de divisas a granel colombiano. Los datos del libro de pedidos proporcionan una valiosa fuente de información en los mercados financieros. Por esta razón, es un excelente candidato para la construcción de nuevas herramientas y modelos comerciales. La representación del libro de pedidos es una rama de estudio todavía activa en las finanzas cuantitativas. Este trabajo aborda el problema de la visualización de información de datos financieros del cambio de divisas a granel de Colombia utilizando dos enfoques: una representación de mapa de calor y un Haar. Representación basada en wavelet para filtrar ruido de alta frecuencia. Esto requiere tratar con una gran cantidad de datos provenientes del Libro de Órdenes Límite del Mercado Forex de Colombia, un registro con todas las intenciones de compra y venta de los participantes del mercado. La evaluación experimental muestra que las estrategias propuestas son capaces de identificar frecuentes patrones dentro de las herramientas de visualización presentadas.
dc.languageeng
dc.publisherUniversidad Nacional de Colombia
dc.publisherBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisherFacultad de Ingeniería
dc.publisherBogotá - Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
dc.relationAhmed, M., Chai, A., Ding, X., Jiang, Y., Sun, Y. (2009). Statistical Arbitrage in High Frequency Trading Based on Limit Order Book Dynamics, 1-26.
dc.relationAhn, H.-J., Cai, J., Cheung, Y. L. (2005). Price clustering on the limit-order book: Evidence from the Stock Exchange of Hong Kong. Journal of Financial Markets, 8(4), 421-451.
dc.relationHilary Arksey and Lisa O’Malley, Scoping studies: towards a methodological framework, International Journal of Social Research Methodology, Vol.8, Number 1, 2005, 19-32.
dc.relationBates, R. G., Dempster, M. A. H., Romahi, Y. S. (2003). Evolutionary reinforcement learning in FX order book and order flow analysis. In 2003 IEEE International Confe rence on Computational Intelligence for Financial Engineering, 2003. Proceedings. (pp. 355-362). IEEE.
dc.relationBloomfield, R., O’Hara, M., Saar, G. (2005). The ✭✭make or take✮✮ decision in an electronic market: Evidence on the evolution of liquidity. Journal of Financial Economics, 75(1), 165-199.
dc.relationBookMap by VeloxPro. (2015, October 8). Retrieved from http://www.bookmap.com/.
dc.relationBreymann W., Dias A., Embrechts P.: Dependence structures for multivariate high frequency data in finance. Quantitative Finance Vol. 3, Iss. 1. (2003).
dc.relationChen, M.; Ebert, D.; Hagen, H.; Laramee, R.S.; van Liere, R.; Ma, K.-L.; Ri barsky, W.; Scheuermann, G.; Silver, D., ”Data, Information, and Knowledge in Visualization,C¸ omputer Graphics and Applications, IEEE , vol.29, no.1, pp.12,19, Jan.- Feb. 2009.
dc.relationCheng, W., Liu, S., Jiao, H., Qiu, W. (2009). How Does Limit Order Book Informa tion Affect Trading Strategy and Market Quality: Simulations of an Agent-Based Stock Market. In 2009 International Conference on Management and Service Science (pp. 1-4). IEEE.
dc.relationChristensen H. L., Turner R. E., Hill S. I., Godsill S.J.: Rebuilding The Limit Or der Book: Bayesian Inference on Hidden States. Quantitative Finance. pp. 1779-1799J. (2013).
dc.relationCont, Stoikov, and Talreja: A Stochastic Model for Order Book Dynamics. Operations Research Vol. 58, No. 3, May - June 2010, pp. 549 - 563 issn 0030 - 364X eissn 1526 - 5463 10 5803 0549 in.
dc.relationCruz, A., Nino. J., Sandoval. J., Rincon, J., Hernandez, G.: Market Trend Visual Bag of Words Informative Patterns in Limit Order Books. International Conference on Com puter Science Proceedings. San Diego, California, U.S.A. (2016).
dc.relationDanielsson J., Payne R.: Real trading patterns and prices in spot foreign exchange mar kets, Journal of International Money and Finance, Volume 21, Issue 2, April 2002, Pa ges 203-222, ISSN 0261-5606, http://dx.doi.org/10.1016/S0261-5606(01)00043-2. (2002).
dc.relationLopez-Monroy A. P., Gomez, M. M., Escalante, H. J., Cruz-Roa, A. and Gonzalez, F. A. Bag-of-visual-ngrams for histopathology image classification, in Proc. of SPIE 8922, 2013, p. 89220P.
dc.relationDonoho, D. and Johnstone, I. Ideal spatial adaptation via wavelet shrinkage. Biometrika, 81(3):425-455, 1994.
dc.relationEisler, Z., Kertesz, J., Lillo, F.: The Limit Order Book on Different Time Scales, ar Xiv.org, Quantitative Finance Papers 0705.4023, May 2007. [Online]. Available: http: //ideas.repec.org/p/arx/papers/0705.4023.html (2007).
dc.relationFarmer, J. Doyne and Patelli, Paolo and Zovko, Ilija I., The Predictive Power of Zero Intelligence in Financial Markets (February 9, 2004). AFA 2004 San Diego Meetings.
dc.relationFletcher, T., Hussain, Z., Shawe-Taylor, J. (2010). Multiple Kernel Learning on the Limit Order Book. In WAPA (pp. 167-174).
dc.relationForeign Exchange Transaction Electronic System (Set-FX), http://www.set-fx.com/ index.html
dc.relationForni, M., Lippi, M. (2001). The generalized dynamic factor model: Representation theory. ECONOMETRIC THEORY, 17(6), 1113-1141.
dc.relationGabor,D. Theory of communication. J. IEE, 93:429-457, 1946
dc.relationGould, M. D., Porter, M. A., Williams, S., McDonald, M., Fenn, D. J. and Howison, S. D. Limit order books. Quantitative Finance, Vol. 13, No. 11, 1709-1742. 2013.
dc.relationHall, A. D., Hautsch, N. (2007). Modelling the buy and sell intensity in a limit order book market. Journal of Financial Markets, 10(3), 249-286.
dc.relationHarris, Zellig S. Distributional structure. Word, Vol 10, 1954, 146-162.
dc.relationHayes, Adam. Forex Trading Strategy, 2021.https://www.investopedia.com/terms/ forex/f/forex-trading-strategies.asp
dc.relationHsin, P.-H., Wang, M.-C. (2007). Information Indicators of Limit Order Book and Optimal Dynamic Order Submission Strategy. In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007) (pp. 197-197). IEEE.
dc.relationHuang, H., Kercheval, A. N. (2012). A generalized birth-death stochastic model for high-frequency order book dynamics. Quantitative Finance, 12(4), 547-557.
dc.relationHuang, R., Polak, T. (2011). LOBSTER: Limit Order Book Reconstruction System. Available at SSRN 1977207.
dc.relationIntegrated Latin American Market (MILA), http://www.mercadomila.com
dc.relationJian Jiang, Wing Lon Ng. (2010). Capturing order book dynamics with Kalman filters.
dc.relationJiang, G., Wang, S., Dong, H. (2011). A Survey of Limit Order Book Modeling in Continuous Auction Market. In 2011 3rd International Workshop on Intelligent Systems and Applications (pp. 1-4). IEEE.
dc.relationJiang, J., Ng, W. L. (2009a). Revealing Intraday Market Efficiency – Estimating Diurnal Price Densities in Limit Order Books. In 2009 International Conference on Information and Financial Engineering (pp. 8-12). IEEE.
dc.relationJiaqi Wang, Zhang, C. (2006). Dynamic Focus Strategies for Electronic Trade Execution in Limit Order Markets. In The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE-06) (pp. 26-26). IEEE.
dc.relationKercheval,Alec N. and Zhang,Yuan. Modelling high-frequency limit order book dyna mics with support vector machines,Quantitative Finance, volume 15, number 8, pp.1315- 1329. 2015.
dc.relationKirilenko, A., Kyle, A. S. (2011). The Flash Crash : The Impact of High Frequency Trading on an Electronic Market.
dc.relationKrishnamurthy, V., Aryan, A. (2012). Quickest detection of market shocks in agent based models of the order book. In 2012 IEEE 51st IEEE Conference on Decision and Control (CDC) (pp. 1480-1485). IEEE.
dc.relationLee, S.-Y., Poon, W.-Y., Song, X.-Y. (2007). Bayesian analysis of the factor model with finance applications. QUANTITATIVE FINANCE, 7(3), 343-356.
dc.relationLee, W.-B., Choe, H. (n.d.-a). Short-term return predictability of information in the open limit order book. Asia-Pacific Journal of Financial Studies (2007) vol. 36, number 6, pp. 963-1007.
dc.relationLi, Y., Zhang, X. (2009). A Comparative Study of Information Content of Limit Order Book before and after Transparency Was Increased: Evidence from Shenzhen Stock Exchange. In 2009 International Conference on Management and Service Science (pp. 1-4). IEEE.
dc.relationLopez-Monroy A. P., Gomez, M. M., Escalante, H. J., Cruz-Roa, A. and Gonzalez, F. A. Bag-of-visual-ngrams for histopathology image classification, in Proc. of SPIE 8922, 2013, p. 89220P.
dc.relationMallat Stephane. A Wavelet Tour of Signal Processing: The Sparse Way. Elsevier. Third Edition. 2009.
dc.relationMalik, Azeem and Ng, Wing Lon, (2014), Intraday liquidity patterns in limit order books, Studies in Economics and Finance, 31, issue 1, p. 46-71.
dc.relationMoorhead, R.J.; Zhifan Zhu, ”Signal processing aspects of scientific visualization,”Signal Processing Magazine, IEEE , vol.12, no.5, pp.20,41, Sep 1995. DOI: 10.1109/79.410438.
dc.relationNarasimhan, Priya (Carnegie Mellon University). (2006). Fault-Tolerant Distribu ted Systems [Course Material]. Retrieved from https://www.ece.cmu.edu/~ece749/ teams-06/team3/.
dc.relationNYSE Arcabook for Options Client Specification for NYSE Arca Options and Nyse Amex Options Exchanges. 2014 NYSE Euronext. Technical Report. (2014).
dc.relationOnorato, M., Altman, E. I. (2005). An integrated pricing model for defaultable loans and bonds. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 163(1), 65-82.
dc.relationPalguna, D., Pollak, I. (2012). Non-parametric prediction of the mid-price dynamics in a limit order book. In 2012 IEEE Statistical Signal Processing Workshop (SSP) (pp. 896-899). IEEE.
dc.relationPascual, R., Veredas, D. (2009). What pieces of limit order book information matter in explaining order choice by patient and impatient traders? Quantitative Finance, 9(5), 527-545.
dc.relationRajaraman, Anand and Ullman, Jeffrey David. Mining of Massive Datasets. Cambridge University Press. New York, NY, USA. 2011.
dc.relationRanaldo, A. (2004a). Order aggressiveness in limit order book markets. Journal of Financial Markets, 7(1), 53-74.
dc.relationRecord Neil, Currency overlay. Wiley Finance. England. 2003.
dc.relationRussell,Jeffrey R. and Kim,Taejin .A New Model for Limit Order Book Dyna mics,”Volatility and Time Series Econometrics : Essays in Honor of Robert F. Engle. Oxford ; New York: Oxford University Press, 2010.
dc.relationSettlements, I. (2010). Triennial Central Bank Survey Report on global foreign exchange market activity in 2010 (pp. 1-95).
dc.relationSivic, J. and Zisserman., A. (2003). Video google: A text retrieval approach to object matching in videos. In Proceedings of the International Conference on Computer Vision, ICCV.
dc.relationSong, N., Ching, W.-K., Siu, T.-K., Yiu, C. (2012). Optimal Submission Problem in a Limit Order Boovk with VaR Constraints. In 2012 Fifth International Joint Conference on Computational Sciences and Optimization (pp. 266-270). IEEE.
dc.relationMERCADO Next day. Manual de usuario. SET ICAP FX. https://set-icap.com/ Manuales/Manual_de_Usuario_Next_Day.pdf (2016).
dc.relationTodd, A.; Scherer, W.; Beling, P.; Paddrik, M.; Haynes, R., ✭✭Visualizations for sense making in financial market regulation✮✮, Big Data (Big Data), 2014 IEEE International Conference on , vol., no., pp.730,735, 27-30 Oct. 2014.
dc.relationVasquez Linares, Mario. Gonzalez Osorio, Fabio Augusto and Hernandez Losada, Die go Fernando. Mining Candlesticks Patterns on Stock Series: A Fuzzy Logic Approach. Advanced Data Mining and Applications. Lecture Notes in Computer Science. Springer Berlin Heidelberg. 2009. pp. 661-670.
dc.relationVvedenskaya, N., Suhov, Y., Belitsky, V. (2011). A non-linear model of limit order book dynamics. In 2011 IEEE International Symposium on Information Theory Proceedings (pp. 1260-1262). IEEE.
dc.relationWang, M.-C., Zu, L.-P., Kuo, C.-J. (2008). The state of the electronic limit order book, order aggressiveness and price formation. Asia-Pacific Journal of Financial Studies, 37(2).
dc.relationWang Yanhong, Liu Shancun. (2011). An empirical heterogeneous trading strategy model in the Shanghai stock market of China. In MSIE 2011 (pp. 227-230). IEEE.
dc.relationWeinberger, Kilian and Dasgupta, Anirban and Langford, John and Smola, Alex and Attenberg, Josh. Feature Hashing for Large Scale Multitask Learning. Proceedings of the 26th Annual International Conference on Machine Learning. ACM. Montreal, Quebec, Canada. 2009. pp. 1113-1120.
dc.relationWhigham, P. A., Withanawasam, R., Crack, T., Premachandra, I. M. (2010). Evolving trading strategies for a limit-order book generator. In IEEE Congress on Evolutionary Computation (pp. 1-8). IEEE.
dc.relationYang, S., Paddrik, M., Hayes, R., Todd, A., Kirilenko, A., Beling, P., Scherer, W. (2012). Behavior based learning in identifying High Frequency Trading strategies. In 2012 IEEE Conference on Computational Intelligence for Financial Engineering Economics (CIFEr) (pp. 1-8). IEEE.
dc.relationYu, Y. (2006). The Limit Order Book Information and the Order Submission Strategy: A Model Explanation. In 2006 International Conference on Service Systems and Service Management (Vol. 1, pp. 687-691). IEEE.
dc.relationAlgorithmic Trading Challenge. (2012, January 8). Retrieved from https://www. kaggle.com/c/AlgorithmicTradingChallenge/details/Background/
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleDeterminación de la factibilidad de la detección de estrategias de operación en el mercado de divisas colombiano utilizando la información del libro de órdenes
dc.typeTrabajo de grado - Maestría


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