Algorithmic Silver Trading via Fine-Tuned CNN-Based Image Classification and Relative Strength Index-Guided Price Direction Prediction

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dc.contributor.authorAltuntas, Yahya
dc.contributor.authorOkumus, Fatih
dc.contributor.authorKocamaz, Adnan Fatih
dc.date.accessioned2026-04-04T13:30:55Z
dc.date.available2026-04-04T13:30:55Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractPredicting short-term buy and sell signals in financial markets remains a significant challenge for algorithmic trading. This difficulty stems from the data's inherent volatility and noise, which often leads to spurious signals and poor trading performance. This paper presents a novel algorithmic trading model for silver that combines fine-tuned Convolutional Neural Networks (CNNs) with a decision filter based on the Relative Strength Index (RSI). The technique allows for the prediction of buy and sell points by turning time series data into chart images. Daily silver price per ounce data were turned into chart images using technical analysis indicators. Four pre-trained CNNs, namely AlexNet, VGG16, GoogLeNet, and ResNet-50, were fine-tuned using the generated image dataset to find the best architecture based on classification and financial performance. The models were evaluated using walk-forward validation with an expanding window. This validation method made the tests more realistic and the performance evaluation more robust under different market conditions. Fine-tuned VGG16 with the RSI filter had the best cost-adjusted profitability, with a cumulative return of 115.03% over five years. This was nearly double the 61.62% return of a buy-and-hold strategy. This outperformance is especially impressive because the evaluation period was mostly upward, which makes it harder to beat passive benchmarks. Adding the RSI filter also helped models make more disciplined decisions. This reduced transactions with low confidence. In general, the results show that pre-trained CNNs fine-tuned on visual representations, when supplemented with domain-specific heuristics, can provide strong and cost-effective solutions for algorithmic trading, even when realistic cost assumptions are used.
dc.identifier.doi10.3390/sym17081338
dc.identifier.issn2073-8994
dc.identifier.issue8
dc.identifier.orcid0000-0002-7729-8322
dc.identifier.orcid0000-0003-3046-9558
dc.identifier.orcid0000-0002-7472-8251
dc.identifier.scopus2-s2.0-105014438534
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/sym17081338
dc.identifier.urihttps://hdl.handle.net/11616/108473
dc.identifier.volume17
dc.identifier.wosWOS:001558293400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSymmetry-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectalgorithmic trading
dc.subjectfinancial time series forecasting
dc.subjectdeep learning
dc.subjectconvolutional neural networks
dc.titleAlgorithmic Silver Trading via Fine-Tuned CNN-Based Image Classification and Relative Strength Index-Guided Price Direction Prediction
dc.typeArticle

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