Abstract
This work concerns the optimization of a Trading Systems (TS) based on a small set of Technical Analysis (TA) indicators. Usually, in TA the values of the parameters (window lengths and thresholds) of these indicators are fixed by professional experience. Here, we propose to design the parametric configuration according to historical data, optimizing some performance measures subjected to proper constraints using a Particle Swarm Optimization-based metaheuristic. In particular, such an optimization procedure is applied to obtain both the optimal parameter values and the optimal weighting of the trading signals from the considered TA indicators, in order to provide an optimal trading decision. The use of a metaheuristic is necessary since the involved optimization problem is strongly nonlinear, nondifferentiable and mixed-integer. The proposed TS is optimized using the daily adjusted closing returns of seven Italian stocks coming from different industries and of two stock market indices.
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Notes
- 1.
Notice that \(e(\cdot )\) is a function of \(S(\cdot )\), that \(S(\cdot )\) is a function of \(signal_i(\cdot )\), and that \(signal_i(\cdot )\) is a function of the i-th indicator.
- 2.
Notice that we performed our procedure 100 times, so we could apply the central limit theorem according to which the sample mean is asymptotically normally distributed.
References
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Corazza, M., Parpinel, F., Pizzi, C. (2021). Trading System Mixed-Integer Optimization by PSO. In: Corazza, M., Gilli, M., Perna, C., Pizzi, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-78965-7_24
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DOI: https://doi.org/10.1007/978-3-030-78965-7_24
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