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# Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach.

Applied Soft Computing, (2018): 525-538

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关键词

摘要

•We converted 1-D financial technical analysis data to 2-D images for classification.•We used 2-D deep convolutional neural network for trend forecasting.•We propose a robust algorithmic trading model that works in any market condition.•To best of our knowledge, 2-D CNN with TA has not been used for financial trading before.•Model outperf...更多

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简介

- Stock market forecasting based on computational intelligence models have been part of stock trading systems for the last few decades.
- As a result, trading systems based on autonomous intelligent decision making models are getting more attention in various different financial markets globally [1].
- Deep learning based prediction/classification models started emerging as the best performance achievers in various applications, outperforming classical computational intelligence methods like SVM.
- The application of deep neural networks on financial forecasting models have been very limited

重点内容

- Stock market forecasting based on computational intelligence models have been part of stock trading systems for the last few decades
- Deep learning based prediction/classification models started emerging as the best performance achievers in various applications, outperforming classical computational intelligence methods like support vector machines (SVM)
- Image processing and vision based problems dominate the type of applications that these deep learning models outperform the other techniques [2]
- 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
- We utilized a 2-D Deep Convolutional Neural Network model to be used with financial stock market data and technical analysis indicators for developing an algorithmic trading system

方法

- For the algorithmic trading model, the authors propose a novel method that uses CNN to determine the “Buy” and “Sell” points in stock prices using 15 different technical indicators with different time intervals and parameter selections for each daily stock price time series to create images.
- As can be seen in Fig. 2, the proposed method is divided into five main steps: dataset extract/transform, labeling data, image creation, CNN analysis and financial evaluation phases.
- Dataset extract/transform phase, the downloaded prices are normalized according to the adjusted close prices

结果

- The results indicate CNN-TA trading performance is significantly better than all models over the long run (2007–2017) for both Dow30 stocks and ETFs.

结论

- The authors utilized a 2-D Deep Convolutional Neural Network model to be used with financial stock market data and technical analysis indicators for developing an algorithmic trading system.
- The authors used Dow Jones 30 stock prices and ETFs as the financial time series data.
- The results indicate this novel approach performs very well against Buy & Hold and other models over long periods of out-of-sample test periods.
- The authors will analyze the correlations between selected indicators in order to create more meaningful images so that the learning models can better associate the Buy–Sell–Hold signals and come up with more profitable trading models

- Table1: Selected ETFs and their descriptions
- Table2: Confusion matrix of test data (Dow-30)
- Table3: Evaluation of test data (Dow-30)
- Table4: Confusion matrix of test data (ETFs)
- Table5: Evaluation of test data (ETFs)
- Table6: Comparison of annualized returns of the proposed system (CNN-TA) with BaH, RSI, SMA, LSTM, MLP Reg. models (ETFs – test period: 2007–2017)
- Table7: Comparison of annualized returns of the proposed system (CNN-TA) with BaH, RSI, SMA, LSTM, MLP Reg. models (ETFs test period: 2007–2012)
- Table8: Comparison of annualized returns of the proposed system (CNN-TA) with BaH, RSI, SMA, LSTM, MLP Reg. models (DOW30 – test period: 2007–2017)
- Table9: Comparison of annualized returns of the proposed system (CNN-TA) with BaH, RSI, SMA,LSTM, MLP Reg. Models (DOW30 – test period: 2007–2012)
- Table10: TTest results and average results of the proposed CNN-TA model for Dow30
- Table11: TTest results and average results of the proposed CNN-TA model for ETFs
- Table12: TTest results of annualized return of Dow30 stocks
- Table13: TTest results of annualized return of ETFs

相关工作

- 2.1. Time series data analytics

In literature, there are different adapted methodologies for time series data analysis. These can be listed as follows: statistical and mathematical analysis, signal processing, extracting features, pattern recognition, and machine learning. Statistical and mathematical analysis in time series data can be achieved through determining the mathematical parameters such as maximum, minimum, average, moving average, variance, covariance, standard deviation, autocorrelation, crosscorrelation and convolution in the sliding window [6]. Curve fitting, regression analysis, autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), Bayesian analysis, Kalman filter methods are the mathematical methods that are generally used to analyze and forecast time series data in literature [7]. In addition, signal processing methods such as Fourier and wavelet transforms are used to analyze the time series data. Discrete Fourier transform (DFT), discrete wavelet transform (DWT), piecewise aggregate approximation (PAA) are also used to analyze time series data to extract features and find the similarities within the data [8]. Unlike traditional approaches, machine learning models are also used in analyzing time series data and predictions. Machine learning algorithms that are mostly used in time series data analytics are listed as follows: clustering algorithms [9], hidden Markov models [10], support vector machines (SVM) [11,12,13], artificial neural networks (ANNs) [14,15,16,17] self organizing maps (SOM) [18,19,20].

基金

- This study was funded by The Scientific and Technological Research Council of Turkey (TUBITAK) under grant number 215E248

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- S. Yue, Imbalanced Malware Images Classification: A CNN Based Approach, 2017 arXiv:1708.08042. Omer Berat Sezer received the B.S. degree in Electrical and Electronics Engineering from Middle East Technical University NCC, Ankara, Turkey, in 2009, with an emphasis on telecommunications and computers; M.S. degree in Electrical and Electronics Engineering from Middle East Technical University, Ankara, Turkey, in 2013,with an emphasis on computer networks. He is currently a Ph.D. candidate at Department of Computer Engineering of TOBB University of Economics and Technology, in Ankara, Turkey and he is also working as a senior researcher and software engineer at The Scientific and Technological Research Council of Turkey – Space Technologies Research Institute, in Ankara, Turkey. His research interests are machine learning, Internet of things, big data and time series data analytics.

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