Prediction of Gold Price Direction using Convolutional Neural Networks and a Transfer Learning Approach

Abstract

With the financial time series forecasting, it is aimed to increase the profitability by making the right buying-selling decisions for financial assets. The prices of financial assets are in a fragile structure affected by many factors. Therefore, financial time series forecasting is a very challenging task that has attracted attention by researchers from different disciplines for many years. In this study, 15 years of historical price data covering the years 2007 – 2021 are used to predict the daily ounce gold price direction. Gold price data has been converted into graphic images with the help of candlestick charts and technical analysis indicators. In this way, gold price direction prediction is reduced to a 2-class image classification problem. AlexNet, one of the pioneering pre-trained convolutional neural network models for the classification of images, was fine-tuned and adapted. According to the experimental results, the classification performance of the proposed approach was measured as 53.8%, 66.97%, 37.54% and 42.05% for accuracy, sensitivity, specificity and f-score performance metrics, respectively. In addition, profitability analyzes of the trading strategy based on the predictions of the proposed approach were also made and compared with the Relative Strength Index and Buy and Hold investment strategies, which are frequently used by investors. According to the market simulation results carried out during the 3-year maturity, the proposed approach yielded better results than other investment strategies with a profit rate of 51,77%.

Keywords

• financial time series forecasting
• algorithmic trading
• price direction predicting
• deep learning
• convolutional neural networks.

Evrişimsel Sinir Ağları ve Transfer Öğrenme Yaklaşımı Kullanılarak Altın Fiyat Yönünün Tahmini

Abstract

Finansal zaman serisi tahmini ile finansal varlıklar için doğru alım-satım kararları vererek karlılığın arttırılması amaçlanmaktadır. Finansal varlıkların fiyatları pek çok faktörden etkilenen kırılgan bir yapıdadır. Bu nedenle, finansal zaman serisi tahmini uzun yıllardır farklı disiplinlerden araştırmacılar tarafından ilgi gören oldukça zorlu bir görevdir. Bu çalışmada, günlük ons altın fiyat yönünün tahmini için 2007 – 2021 yıllarını kapsayan 15 yıllık tarihsel fiyat verisi kullanılmıştır. Altın fiyat verileri mum grafikleri ve teknik analiz göstergeleri yardımıyla grafik görüntülere dönüştürülmüştür. Bu sayede altın fiyat yön tahmini 2-sınıflı görüntü sınıflandırma problemine indirgenmiştir. Görüntülerin sınıflandırılması için öncü ön-eğitimli evrişimsel sinir ağı modellerinden AlexNet ince-ayarlanarak adapte edilmiştir. Gerçekleştirilen deney sonuçlarına göre, önerilen yaklaşımın sınıflandırma performansı doğruluk, duyarlılık, hassasiyet ve f-ölçütü performans metrikleri için sırasıyla %53,8, %66,97, %37,54 ve %42,05 olarak ölçülmüştür. Ayrıca önerilen yaklaşımın tahminlerine dayalı gerçekleştirilen ticaret stratejisinin karlılık analizleri de yapılmış ve yatırımcılar tarafından sıklıkla kullanılan Göreceli Güç Endeksi ve Al ve Tut yatırım stratejileri ile karşılaştırılmıştır. 3 yıllık vade boyunca gerçekleştirilen piyasa benzetim sonuçlarına göre önerilen yaklaşım %51,77 kar oranıyla diğer yatırım stratejilerinden daha iyi sonuç vermiştir.

Keywords

• finansal zaman serisi tahmini
• algoritmik ticaret
• fiyat yön tahmini
• derin öğrenme
• evrişimsel sinir ağları.

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