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Using strongly typed genetic programming to combine technical and sentiment analysis for algorithmic trading

Christodoulaki, Eva and Kampouridis, Michael (2022) Using strongly typed genetic programming to combine technical and sentiment analysis for algorithmic trading. In: IEEE Congress on Evolutionary Computation 2022, 2022-07-18 - 2022-07-23, Padova, Italy. (In Press)

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Abstract

Algorithmic trading has become an increasingly thriving research area and a lot of focus has been given on indicators from technical and sentiment analysis. In this paper, we examine the advantages of combining features from both technical and sentiment analysis. To do this, we use two different genetic programming algorithms (GP). The first algorithm allows trees to contain technical and/or sentiment analysis indicators without any constraints. The second algorithm introduces technical and sentiment analysis types through a strongly typed GP, whereby one branch of a given tree contains only technical analysis indicators and another branch of the same tree contains only sentiment analysis features. This allows for better exploration and exploitation of the search space of the indicators. We perform experiments on 10 international stocks and compare the above two GPs’ performances. Our goal is to demonstrate that the combination of the indicators leads to improved financial performance. Our results show that the strongly typed GP is able to rank first in terms of Sharpe ratio and statistically outperform all other algorithms in terms of rate of return.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: _not provided_
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 08 Aug 2022 13:21
Last Modified: 23 Sep 2022 19:53
URI: http://repository.essex.ac.uk/id/eprint/32799

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