The Use of Dynamically Optimised High Frequency Moving Average Strategies for Intraday Trading
This paper is motivated by the aspect of uncertainty in
financial decision making, and how artificial intelligence and soft
computing, with its uncertainty reducing aspects can be used for
algorithmic trading applications that trade in high frequency.
This paper presents an optimized high frequency trading system that
has been combined with various moving averages to produce a hybrid
system that outperforms trading systems that rely solely on moving
averages. The paper optimizes an adaptive neuro-fuzzy inference
system that takes both the price and its moving average as input,
learns to predict price movements from training data consisting of
intraday data, dynamically switches between the best performing
moving averages, and performs decision making of when to buy or
sell a certain currency in high frequency.
[1] A. Kablan (2009). "A Fuzzy Logic Momentum Analysis System for
Financial Brokerage", proceedings of the International Conference on
Financial Theory and Engineering. IEEEXplore, Vol 1, pp 57-62. ISBN:
978-0-7695-3949-2
[2] A. Kablan (2009). "Adaptive Neuro Fuzzy Systems for High Frequency
Trading and Forecasting", proceedings of the Third International
Conference on Advanced Engineering Computing and Applications in
Sciences. IEEEXplore, Vol 1, pp 105 - 110. ISBN: 978-0-7695-3829-7
[3] A. Kablan, W. L. Ng, (2010), "High Frequency Trading Strategy Using
the Hilbert Transform", 6th International Conference on Networked
Computing and Advanced Information Management. Vol 1, pp 466 -
471. ISBN: 978-89-88678-26-8
[4] A. Kablan, W. L. Ng, (2010), "High Frequency Trading using Fuzzy
Momentum Analysis", proceedings of the IAENG 2010 International
Conference of Financial Engineering ( ICFE ), London., Vol 1, pp 352-
357. ISBN: 978-988-17012-9-9
[5] A. Kablan, W. L. Ng, (2011), "High-Frequency Order Placement
Strategies with Fuzzy Logic and Fuzzy Inference", IAENG International
Journal of Computer Science, special issue.
[6] A. Kablan, W. L. Ng, (2011), "Intraday High-Frequency Forex Trading
with Adaptive Neuro-Fuzzy Inference Systems", International Journal of
Financial Markets and Derivatives.
[7] A. Kablan. Adaptive Neuro Fuzzy Inference Systems for High
Frequency Financial Trading and Forecasting. Proceedings of The Third
International Conference on Advanced Engineering Computing and
Applications in Sciences. 2009.
[8] Banik, S et. al. (2007), Modeling chaotic behavior of Dhaka Stock
Market Index values using the neuro-fuzzy model, 10th international
conference on Computer and information technology
[9] C. Tseng, Y. Lin. Financial Computational Intelligence. Society for
Computational Economics. Computing in Economics and Finance no.
42. 2005.
[10] Chang, S. S. L (1977). Application of fuzzy set theory to economics.
Kybernetes 6, pp 203-207
[11] Dacarogna, M. et al 2001, An Introduction to High-Frequency Finance,
Academic Press
[12] Dormale, A. V. (1997): The Power of Money, Macmillan Press, London
[13] E. Boehmer (2005) Dimensions of execution quality: recent evidence for
U.S. equity markets. Journal of Financial Economics 78, 553-582, 2005.
[14] E. F. Fama. (1970) Efficient capital markets: A review of theory and
empirical work. Journal of Finance, pages 383-417, May
[15] Grabbe, J. O. (1996): International Financial Markets, Englewood Hills,
Prentice Hall Inc.
[16] J. C. Bezdek, R. Krisnapuram, N. R. Pal. (1999). Fuzzy models and
algorithms for pattern recognition and image processing. Springer.
[17] JM. Griffin, F. Nardari, R. Stulz. "Stock market trading and market
conditions". NBER, Working Paper 10719, 1-48. 2004.
[18] Kablan, A, W. L. Ng, (2011 "High Frequency Trading using Fuzzy
Momentum Analysis", Springer Engineering Letters, London.
[19] Li, Y., Musilek, P and Wyard-Scott, L. Fuzzy logic in agent-based
game design. Annual Meeting of Fuzzy Information Processing 2004,
vol. 2, pp734-739, 2004.
[20] Lootsma, F. A. (1997). Fuzzy logic for planning and decision making.
Springer.
[21] M. Fedrizzi, W. Ostasiewicz (1993.). Towards fuzzy modelling in
economics. Fuzzy sets and systems, vol. 54, Issue 3, pp 259-268,
[22] Ormerod, P. (2000) Butterfly economics: A new general theory of social
and economic behaviour. Pantheon, New York.
[23] Q. Song, B. S. Chissom. Forecasting enrollments with fuzzy time-series.
Part II. Fuzzy Sets and Systems 62, pp 1-8, 1994
[24] Roger Jang, J.S., ANFIS: "Adaptive network-based fuzzy inference
system", IEEE Transactions on Systems, Man and Cybernetics, 23 (3)
(1993) 665-685.
[25] S. Chabaa and A. Zeroual. Predicting Packet Transmission Data over IP
Networks Using Adaptive Neuro-Fuzzy Inference Systems. Journal of
Computer Science Vol. 5(2), pp 123-130, 2009.
[26] Schulmeister, S, 2009, ÔÇÿA General Financial Transaction Tax: A Short
Cut of the Pros, the Cons and a Proposal-, WIFO Working Paper No
344.
[27] T. Hellström and K. Holmstrom. Predicting the Stock Market. Technical
Report Ima-TOM-1997-07, Center of Mathematical Modeling,
Department of Mathematics and Physis, Malardalen University,
Vasteras, Sweden, August 1998.
[28] T. Takagi and M. Sugeno. Fuzzy identification of systems and its
application to modeling and control, IEEE Transactions on Systems,
Man and Cybernetics, Vol. 15, pp 116-132, 1985.
[29] Takagi T., and Sugeno, M., "Fuzzy identification of systems and its
application to modeling and control", IEEE Transactions on Systems,
Man and Cybernetics, 15 (1985) 116-132.
[30] Wilson, R., and Sharda, R., "Bankruptcy prediction using neural
networks", Decision Support Systems, 11 (1994) 545-557.
[31] Yeh, S., Landsman, W. R., Miller, B. L., & Peasnell, K. V.(2011). Do
investors understand really dirty surplus?. The Accounting Review,
86(1), 237-258.
[32] Yoon, Y., Guimaraes, T., and Swales, G., "Integrating neural networks
with rule-based expert systems", Decision Support Systems, 11 (1994)
497-507.
[33] Zadeh, L. Fuzzy sets. Information and Control, vol. 8, pp 338-353,
1965.
[34] Zadeh, L.A., "Fuzzy sets", Information Control, 8 (1965) 338-353.
[1] A. Kablan (2009). "A Fuzzy Logic Momentum Analysis System for
Financial Brokerage", proceedings of the International Conference on
Financial Theory and Engineering. IEEEXplore, Vol 1, pp 57-62. ISBN:
978-0-7695-3949-2
[2] A. Kablan (2009). "Adaptive Neuro Fuzzy Systems for High Frequency
Trading and Forecasting", proceedings of the Third International
Conference on Advanced Engineering Computing and Applications in
Sciences. IEEEXplore, Vol 1, pp 105 - 110. ISBN: 978-0-7695-3829-7
[3] A. Kablan, W. L. Ng, (2010), "High Frequency Trading Strategy Using
the Hilbert Transform", 6th International Conference on Networked
Computing and Advanced Information Management. Vol 1, pp 466 -
471. ISBN: 978-89-88678-26-8
[4] A. Kablan, W. L. Ng, (2010), "High Frequency Trading using Fuzzy
Momentum Analysis", proceedings of the IAENG 2010 International
Conference of Financial Engineering ( ICFE ), London., Vol 1, pp 352-
357. ISBN: 978-988-17012-9-9
[5] A. Kablan, W. L. Ng, (2011), "High-Frequency Order Placement
Strategies with Fuzzy Logic and Fuzzy Inference", IAENG International
Journal of Computer Science, special issue.
[6] A. Kablan, W. L. Ng, (2011), "Intraday High-Frequency Forex Trading
with Adaptive Neuro-Fuzzy Inference Systems", International Journal of
Financial Markets and Derivatives.
[7] A. Kablan. Adaptive Neuro Fuzzy Inference Systems for High
Frequency Financial Trading and Forecasting. Proceedings of The Third
International Conference on Advanced Engineering Computing and
Applications in Sciences. 2009.
[8] Banik, S et. al. (2007), Modeling chaotic behavior of Dhaka Stock
Market Index values using the neuro-fuzzy model, 10th international
conference on Computer and information technology
[9] C. Tseng, Y. Lin. Financial Computational Intelligence. Society for
Computational Economics. Computing in Economics and Finance no.
42. 2005.
[10] Chang, S. S. L (1977). Application of fuzzy set theory to economics.
Kybernetes 6, pp 203-207
[11] Dacarogna, M. et al 2001, An Introduction to High-Frequency Finance,
Academic Press
[12] Dormale, A. V. (1997): The Power of Money, Macmillan Press, London
[13] E. Boehmer (2005) Dimensions of execution quality: recent evidence for
U.S. equity markets. Journal of Financial Economics 78, 553-582, 2005.
[14] E. F. Fama. (1970) Efficient capital markets: A review of theory and
empirical work. Journal of Finance, pages 383-417, May
[15] Grabbe, J. O. (1996): International Financial Markets, Englewood Hills,
Prentice Hall Inc.
[16] J. C. Bezdek, R. Krisnapuram, N. R. Pal. (1999). Fuzzy models and
algorithms for pattern recognition and image processing. Springer.
[17] JM. Griffin, F. Nardari, R. Stulz. "Stock market trading and market
conditions". NBER, Working Paper 10719, 1-48. 2004.
[18] Kablan, A, W. L. Ng, (2011 "High Frequency Trading using Fuzzy
Momentum Analysis", Springer Engineering Letters, London.
[19] Li, Y., Musilek, P and Wyard-Scott, L. Fuzzy logic in agent-based
game design. Annual Meeting of Fuzzy Information Processing 2004,
vol. 2, pp734-739, 2004.
[20] Lootsma, F. A. (1997). Fuzzy logic for planning and decision making.
Springer.
[21] M. Fedrizzi, W. Ostasiewicz (1993.). Towards fuzzy modelling in
economics. Fuzzy sets and systems, vol. 54, Issue 3, pp 259-268,
[22] Ormerod, P. (2000) Butterfly economics: A new general theory of social
and economic behaviour. Pantheon, New York.
[23] Q. Song, B. S. Chissom. Forecasting enrollments with fuzzy time-series.
Part II. Fuzzy Sets and Systems 62, pp 1-8, 1994
[24] Roger Jang, J.S., ANFIS: "Adaptive network-based fuzzy inference
system", IEEE Transactions on Systems, Man and Cybernetics, 23 (3)
(1993) 665-685.
[25] S. Chabaa and A. Zeroual. Predicting Packet Transmission Data over IP
Networks Using Adaptive Neuro-Fuzzy Inference Systems. Journal of
Computer Science Vol. 5(2), pp 123-130, 2009.
[26] Schulmeister, S, 2009, ÔÇÿA General Financial Transaction Tax: A Short
Cut of the Pros, the Cons and a Proposal-, WIFO Working Paper No
344.
[27] T. Hellström and K. Holmstrom. Predicting the Stock Market. Technical
Report Ima-TOM-1997-07, Center of Mathematical Modeling,
Department of Mathematics and Physis, Malardalen University,
Vasteras, Sweden, August 1998.
[28] T. Takagi and M. Sugeno. Fuzzy identification of systems and its
application to modeling and control, IEEE Transactions on Systems,
Man and Cybernetics, Vol. 15, pp 116-132, 1985.
[29] Takagi T., and Sugeno, M., "Fuzzy identification of systems and its
application to modeling and control", IEEE Transactions on Systems,
Man and Cybernetics, 15 (1985) 116-132.
[30] Wilson, R., and Sharda, R., "Bankruptcy prediction using neural
networks", Decision Support Systems, 11 (1994) 545-557.
[31] Yeh, S., Landsman, W. R., Miller, B. L., & Peasnell, K. V.(2011). Do
investors understand really dirty surplus?. The Accounting Review,
86(1), 237-258.
[32] Yoon, Y., Guimaraes, T., and Swales, G., "Integrating neural networks
with rule-based expert systems", Decision Support Systems, 11 (1994)
497-507.
[33] Zadeh, L. Fuzzy sets. Information and Control, vol. 8, pp 338-353,
1965.
[34] Zadeh, L.A., "Fuzzy sets", Information Control, 8 (1965) 338-353.
@article{"International Journal of Business, Human and Social Sciences:49829", author = "Abdalla Kablan and Joseph Falzon", title = "The Use of Dynamically Optimised High Frequency Moving Average Strategies for Intraday Trading", abstract = "This paper is motivated by the aspect of uncertainty in
financial decision making, and how artificial intelligence and soft
computing, with its uncertainty reducing aspects can be used for
algorithmic trading applications that trade in high frequency.
This paper presents an optimized high frequency trading system that
has been combined with various moving averages to produce a hybrid
system that outperforms trading systems that rely solely on moving
averages. The paper optimizes an adaptive neuro-fuzzy inference
system that takes both the price and its moving average as input,
learns to predict price movements from training data consisting of
intraday data, dynamically switches between the best performing
moving averages, and performs decision making of when to buy or
sell a certain currency in high frequency.", keywords = "Financial decision making, High frequency trading,
Adaprive neuro-fuzzy systems, moving average strategy.", volume = "6", number = "8", pages = "1969-7", }
