Investment Opportunities Forecasting: Extending the Grammar of a GP-based Tool

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

et al. 2014

Self Cite

Abstract-Guided Local Search is a powerful meta-heuristic algorithm that has been applied to a successful Genetic Programming Financial Forecasting tool called EDDIE. Although previous research has shown that it has significantly improved the performance of EDDIE, it also increased its computational cost to a high extent. This paper presents an attempt to deal with this issue by combining Guided Local Search with Fast Local Search, an algorithm that has shown in the past to be able to significantly reduce the computational cost of Guided Local Search. Results show that EDDIE's computational cost has been reduced by an impressive 75%, while at the same time there is no cost to the predictive performance of the algorithm.

Section: E Gls/gfls Parametersmentioning

confidence: 99%

“…Results in [3], [9] have shown that better solutions have been reached by EDDIE-8 thanks to its extended grammar. However, this new grammar led to an enlarged search space, which could potentially lead to ineffective search.…”

Section: Introductionmentioning

confidence: 99%

Guided Fast Local Search for speeding up a financial forecasting algorithm

Shao

Smonou

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

et al. 2014

Self Cite

“…However, while the above approach returned positive predictive results [13,12], it also dramatically increased the search space of EDDIE. This then led the algorithm to occasionally miss good solutions, due to ineffective search.…”

Section: Brief Historymentioning

confidence: 99%

“…GECCO '14, July 12-16, 2014 further improved its predictive performance. However, these features also led to a greatly increased execution time [13,11], making the algorithm impractical to run in big scale experiments.…”

Section: Introductionmentioning

confidence: 99%

Working with OpenCL to speed up a genetic programming financial forecasting algorithm

Brookhouse

Otero

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

2014

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The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device.

“…Results in [1], [4] showed that thanks to its extended grammar, ED8 could reach new and improved solutions. However, it was observed that occasionally the performance could be compromised, as a consequence of this new grammar.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics application on a financial forecasting problem

Smonou

2013 IEEE Congress on Evolutionary Computation

Tsang

2013

Self Cite

EDDIE is a Genetic Programming (GP) tool, which is used to tackle problems in the field of financial forecasting. The novelty of EDDIE is in its grammar, which allows the GP to look in the space of technical analysis indicators, instead of using prespecified ones, as it normally happens in the literature. The advantage of this is that EDDIE is not constrained to use prespecified indicators; instead, thanks to its grammar, it can choose any indicators within a pre-defined range, leading to new solutions that might have never been discovered before. However, a disadvantage of the above approach is that the algorithm's search space is dramatically larger, and as a result good solutions can sometimes be missed due to ineffective search. This paper presents an attempt to deal with this issue by applying to the GP three different meta-heuristics, namely Simulated Annealing, Tabu Search, and Guided Local Search. Results show that the algorithm's performance significantly improves, thus making the combination of Genetic Programming and meta-heuristics an effective financial forecasting approach.