Tuncer, TunaKaya, UygarSefer, EmreAlacam, OnurHoşer, Tuğcan2023-06-122023-06-122022-11-02978-1-4503-9376-8http://hdl.handle.net/10679/8375https://doi.org/10.1145/3533271.3561738Artificial intelligence-based algorithmic trading has recently started to attract more attention. Among the techniques, deep learning-based methods such as transformers, convolutional neural networks, and patch embedding approaches have become quite popular inside the computer vision researchers. In this research, inspired by the state-of-the-art computer vision methods, we have come up with 2 approaches: DAPP (Deep Attention-based Price Prediction) and DPPP (Deep Patch-based Price Prediction) that are based on vision transformers and patch embedding-based convolutional neural networks respectively to predict asset price and direction from historical price data by capturing the image properties of the historical time-series dataset. Before applying attention-based architecture, we have transformed historical time series price dataset into two-dimensional images by using various number of different technical indicators. Each indicator creates data for a fixed number of days. Thus, we construct two-dimensional images of various dimensions. Then, we use original images valleys and hills to label each image as Hold, Buy, or Sell. We find our trained attention-based models to frequently provide better results for ETFs in comparison to the baseline convolutional architectures in terms of both accuracy and financial analysis metrics during longer testing periods.engopenAccesshttps://creativecommons.org/licenses/by/4.0/conferenceObject798600110323400001010.1145/3533271.3561738AttentionConvolutional neural networksDeep learningStock price predictionTransformers2-s2.0-85142532407