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Multivariate Statistical Model based Currency Market Profitability Binary Classifier

Published: 27 March 2018 Publication History

Abstract

In this paper, we carried out a detailed statistical predictive model to estimate the financial market stability based on historical data. The given stability indicator allows the trader to make the decision about which part of the day a financial instrument is profitable but not risky. This model is implemented as external indicator that can be integrated in the MetaTrader platform and uses historical and real time financial data coming, by streaming, from the trading broker. Using this custom indicator, the trader will save the lost time that he/she spent in front of screens waiting for a valid signal to trade, and avoid the high volatility periods of the day during which the market is unpredictable and risky. The algorithm can be also implemented in algorithmic trading robot as a trigger to turn it on at the best and right moments.

References

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A. A. B. Branquinho, C. R. Lopes, and A. C. E. Baffa. 2016. Probabilistic Planning for Multiple Stocks of Financial Markets. In 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). 501--508.
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Abir Jaafar Hussain Dhiya Al-Jumeily Martin Randles Haya Al-askar, David Lamb and Paul Fergus. 2015. Predicting financial time series data using artificial immune systemâĂŞinspired neural networks. International Journal of Artificial Intelligence and Soft Computing 5, 1 (2015), 45--68.
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N. Kanungsukkasem and T. Leelanupab. 2015. Finding potential influences of a specific financial market in Twitter. In 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE). 414--419.
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Walter Sun. 2003. Relationship between Trading Volume and Security Prices and Returns. MIT Laboratory for Information and Decision Systems P-2638. Massachusetts Institute of Technology.
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H. H. Tung, C. C. Cheng, Y. Y. Chen, Y. F. Chen, S. H. Huang, and A. P. Chen. 2016. Binary Classification and Data Analysis for Modeling Calendar Anomalies in Financial Markets. In 2016 7th International Conference on Cloud Computing and Big Data (CCBD). 116--121.
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Ciprian Chirilaa Viorica Chirilaa. 2015. Financial market stability: a quantile regression approach. 20 (2015), 125--130.

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  • (2019)Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial DataData10.3390/data40100194:1(19)Online publication date: 24-Jan-2019

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      cover image ACM Other conferences
      MedPRAI '18: Proceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence
      March 2018
      135 pages
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      • IAPR: International Association for Pattern Recognition

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      New York, NY, United States

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      Published: 27 March 2018

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      Author Tags

      1. Algorithmic Trading
      2. Binary Classification
      3. Data Analysis
      4. Financial Markets
      5. Predictive Analytic

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      • (2019)Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial DataData10.3390/data40100194:1(19)Online publication date: 24-Jan-2019

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