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A financial trading system with optimized indicator setting, trading rule definition, and signal aggregation through Particle Swarm Optimization

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Abstract

Algorithmic trading, a widespread practice in the financial industry, is based on the automatic signal generation based on trading rules of one or more technical analysis indicators. Generally, the parameters for computing the indicators (such as the time windows), the trading rules (converting the indicator into a trading signal) and the weights for signal aggregation (for combining the signals from a plurality of indicators) are established by the trader based on her experience and are treated as fixed inputs of the trading algorithm. In recent literature, simple optimization systems are introduced by varying only one category of parameters at a time, that is only the indicators setting, only the trading rules definition, or only the signal aggregation while keeping the remaining parameters fixed. Our research goes further and proposes an automated trading system based on simultaneous optimization of the three categories of parameters. More precisely, we consider four technical indicators widely used in financial practice, the Exponential Moving Average, the Relative Strength Index, the Moving Average Convergence/Divergence, and the Bollinger Bands and we determine the optimal signal aggregation, trading rule definition and indicator setting using the Particle Swarm Optimization metaheuristic over a commonly used fitness function, that is the net capital at the end of the trading period. We apply our trading system to the Italian index FTSE MIB and to a set of financial stocks belonging to the FTSE MIB over a multi-year period for training and testing. We generally achieve superior performance both in sample and out of sample, using a standard technical analysis system as a benchmark. Furthermore, we successfully verify the ability of the optimized trading system to accurately classify the stock price trends.

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Data availability

The data that support the findings of this study are available from the corresponding author upon request.

Notes

  1. For pity’s sake, we take \(r_f=0\%\) and we compute the standard deviation on yearly basis.

  2. For a thorough discussion, see Fletcher (2000, Sections 12.3 and 14.3).

  3. We have downloaded the closing prices free of charge from the financial provider “Yahoo Finance”, at the link https://finance.yahoo.com/.

  4. As already reported, in running PSO we use \(\phi _1=\phi _2=1.49618\), \(w^0=0.7298\) and \(\epsilon =10^{-2}\), which are generally suggested in literature.

  5. As mentioned in Sect. 3.2, the trading period spans from January 2, 2007, to May 31, 2022.

  6. The only two not favorable average values are both associated with the stock Atlantia in experiments 1 and 2, for the evaluation indicator Precision. This outcome, jointly with the results presented in Sect. 3.4, show the effectiveness of the optimized trading system.

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Funding

 The study was partly supported by the Department of Economics, Ca' Foscari University of Venice, through the Research Grant titled "Combining optimization metaheuristics and artificial intelligence to design quasi-real-time trading strategies" (Decree of the Director of the Department of Economics, Ca' Foscari University of Venice, Rep. 850/2021, Prot. 101205 - VII/16, September 14, 2021).

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Authors and Affiliations

  1. Department of Economics, Ca’ Foscari University of Venice, Venice, Italy

    Marco Corazza & Claudio Pizzi

  2. Department of Economics, Engineering, Society, and Business Organization, University of Tuscia, Viterbo, Italy

    Andrea Marchioni

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  1. Marco Corazza
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All the authors contributed to the study conception and design, material preparation, data collection and analysis. The final and the previous versions of the manuscript were written and approved by all the authors.

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Correspondence to Andrea Marchioni.

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Corazza, M., Pizzi, C. & Marchioni, A. A financial trading system with optimized indicator setting, trading rule definition, and signal aggregation through Particle Swarm Optimization. Comput Manag Sci 21, 26 (2024). https://doi.org/10.1007/s10287-024-00506-1

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