A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters

Sezer, Ömer Berat; Özbayoğlu, A. Murat; Doğdu, Erdoğan

doi:10.1016/j.procs.2017.09.031

Cited by 100 publications

(47 citation statements)

References 12 publications

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“…In [192], several classical, ML models and DBN were compared for trend forecasting. In [193], technical analysis indicator's (RSI) buy & sell limits were optimized with GA which was used for buy-sell signals. After optimization, DMLP was also used for function approximation.…”

Section: Trend Forecastingmentioning

confidence: 99%

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

Self Cite

795

294

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Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.

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Section: Trend Forecastingmentioning

confidence: 99%

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

Self Cite

795

294

View full text Add to dashboard Cite

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“…According to Omer Berat Sezer et al [38], the combination of genetic algorithms and neural networks together in a stock trading system permits to get better results when compared with Buy & Hold and other trading systems for a wide range of stocks even for relatively longer periods. Their proposed system was based on the use of optimized technical analysis feature parameter values as input features for neural network stock trading system.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression

Chihab

Bousbaa

Chihab

et al. 2019

Applied Computational Intelligence and Soft Computing

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In the Forex market, the price of the currencies increases and decreases rapidly based on many economic and political factors such as commercial balance, the growth index, the inflation rate, and the employment indicators. Having a good strategy to buy and sell can make a profit from the above changes. A successful strategy in Forex should take into consideration the relation between benefits and risks. In this work, we propose an intraweek foreign exchange speculation strategy for currency markets based on a combination of technical indicators. This system has a two-level decision and is composed of the Probit regression model and rules discovery using Random Forest. There are two minimum requirements for a trading strategy: a rule to enter the market and a rule to exit it. Our proposed system, to enter the currency market, should validate two conditions. First, it should validate Random Forest access rules over the following week while in the second one the predicted value of the next day using Probit should be positive. To exit the currency market just one negative warning from Probit or Random Forest is enough. This system was used to develop dynamic portfolio trading systems. The profitability of the model was examined for USD/(EUR, JYN, BRP) variation within the period from January 2014 to January 2016. The proposed system allows improving the prediction accuracy. This indicates a good prediction of the behavior market and it helps to identify the good times to enter it or to leave it.

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“…In the research [26], a novel approach is brought to the literature by using GA to optimise technical indicator levels in order to take position of buy, sell or hold. The authors chose Dow 30 index's daily stock prices in the time interval between 1996 and 2016 and calculated technical analysis indicators called relative strength index (RSI) and simple moving average (SMA).…”

Section: B Optimisation Of Nn Input Layermentioning

confidence: 99%

Review for Optimisation of Neural Networks With Genetic Algorithms and Design of Experiments in Stock Market Prediction

Midilli

Parshutin

2019

ITMS

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Neural networks are commonly used methods in stock market predictions. From the earlier studies in the literature, the requirement of optimising neural networks has been emphasised to increase the profitability, accuracy and performance of neural networks in exchange rate prediction. The paper proposes a literature review of two techniques to optimise neural networks in stock market predictions: genetic algorithms and design of experiments. These two methods have been discussed in three approaches to optimise the following aspects of neural networks: variables, input layer and hyper-parameters.

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A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters

Cited by 100 publications

References 12 publications

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression

Review for Optimisation of Neural Networks With Genetic Algorithms and Design of Experiments in Stock Market Prediction

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