معاملات مالی الگوریتمی با شبکه های عصبی پیچشی عمیق
ترجمه نشده

معاملات مالی الگوریتمی با شبکه های عصبی پیچشی عمیق

عنوان فارسی مقاله: معاملات مالی الگوریتمی با شبکه های عصبی پیچشی عمیق: سری زمانی برای رویکرد تبدیل تصویر
عنوان انگلیسی مقاله: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach
مجله/کنفرانس: محاسبات نرم کاربردی - Applied Soft Computing
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: هوش مصنوعی، مهندسی الگوریتم ها و محاسبات، مهندسی نرم افزار
کلمات کلیدی فارسی: معاملات الگوریتمی، یادگیری عمیق، شبکه های عصبی پیچشی، پیش بینی مالی، بازار سهام، تحلیل فنی
کلمات کلیدی انگلیسی: Algorithmic trading، Deep learning، Convolutional neural networks، Financial forecasting، Stock market، Technical analysis
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.asoc.2018.04.024
دانشگاه: TOBB University of Economics and Technology, Ankara 06560, Turkey
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 6/031 در سال 2018
شاخص H_index: 110 در سال 2019
شاخص SJR: 1/216 در سال 2018
شناسه ISSN: 1568-4946
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 14
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
کد محصول: E11311
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Related work


3- Model features and convolutional neural network (CNN)


4- Method


5- Performance evaluation


6- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


Computational intelligence techniques for financial trading systems have always been quite popular. In the last decade, deep learning models start getting more attention, especially within the image processing community. In this study, we propose a novel algorithmic trading model CNN-TA using a 2-D convolutional neural network based on image processing properties. In order to convert financial time series into 2-D images, 15 different technical indicators each with different parameter selections are utilized. Each indicator instance generates data for a 15 day period. As a result, 15 × 15 sized 2-D images are constructed. Each image is then labeled as Buy, Sell or Hold depending on the hills and valleys of the original time series. The results indicate that when compared with the Buy & Hold Strategy and other common trading systems over a long out-of-sample period, the trained model provides better results for stocks and ETFs.


Introduction


Stock market forecasting based on computational intelligence models have been part of stock trading systems for the last few decades. At the same time, more financial instruments, such as ETFs, options, leveraged systems (like forex) have been introduced for individual investors and traders. As a result, trading systems based on autonomous intelligent decision making models are getting more attention in various different financial markets globally [1]. In recent years, deep learning based prediction/classification models started emerging as the best performance achievers in various applications, outperforming classical computational intelligence methods like SVM. However, image processing and vision based problems dominate the type of applications that these deep learning models outperform the other techniques [2]. In literature, deep learning methods have started appearing on financial studies. There are some implementations of deep learning techniques such as recurrent neural network (RNN) [3], convolutional neural network (CNN) [4], and long short term memory (LSTM) [5]. In particular, the application of deep neural networks on financial forecasting models have been very limited. CNNs have been by far, the most commonly adapted deep learning model [2]. Meanwhile, majority of the CNN implementations in the literature were chosen for addressing computer vision and image analysis challenges. With successful implementations of CNN models, the model error rates keep dropping over years. Despite being one of the early proposed models, AlexNet achieved ∼50–55% success rate. More recently, different versions of Inception (v3, v4) and ResNet (v50, v101, v152) algorithms achieved approximately ∼75–80% success rate [2]. Nowadays, almost all computer vision researchers, one way or another, implement CNN in image classification problems. In this study, we propose a novel approach that converts 1-D financial time series into a 2-D image-like data representation in order to be able to utilize the power of deep convolutional neural network for an algorithmic trading system. In order to come up with such a representation, 15 different technical indicator instances with various parameter settings each with a 15 day span are adapted to represent the values in each column. Likewise, x axis consists of the time series of 15 days worth of data for each particular technical indicator at each row.Also the rows are ordered in such a way that similar indicators are clustered together to accomplish the locality requirements along the y-axis.

  • اشتراک گذاری در

دیدگاه خود را بنویسید:

تاکنون دیدگاهی برای این نوشته ارسال نشده است

معاملات مالی الگوریتمی با شبکه های عصبی پیچشی عمیق
نوشته های مرتبط
مقالات جدید
لوگوی رسانه های برخط

logo-samandehi

پیوندها