Abstract
In 2016, more than the 80% of transactions in the Forex market (where the world’s currencies trade) have been directed by robots. The design of profitable automatic trading systems is becoming a challenging process. This requires a strong synergy of economists and computer scientists. Our aim is to provide an optimization framework for trading systems that starting from a generic strategy, enhances its performances by exploiting mathematical constraints. Moreover, the growth of new markets requires suitable solutions integrating computer science tools with economic analysis. In this work, we mainly refer to an emerging market known as Binary Options. Starting from basic strategies used every day in the stock markets by professional traders, we show how optimization issues enhance the outcoming performances. Tests on the optimized algorithms are conducted on both historical and real time data.
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Acknowledgments
Special thanks go to Simone Ciancone for his expertise and useful discussions. The work has been supported in part by the GNCS-INdAM project 2018 “Anti-Social Networks”.
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Bigiotti, A., Navarra, A. (2019). Optimizing Automated Trading Systems. In: Antipova, T., Rocha, A. (eds) Digital Science. DSIC18 2018. Advances in Intelligent Systems and Computing, vol 850. Springer, Cham. https://doi.org/10.1007/978-3-030-02351-5_30
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DOI: https://doi.org/10.1007/978-3-030-02351-5_30
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