An intelligent financial portfolio trading strategy using deep Q-learning

Park, Hyung Jun; Sim, Min Kyu; Choi, Dong Gu

doi:10.1016/j.eswa.2020.113573

Cited by 70 publications

(49 citation statements)

References 34 publications

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“…Currently, many approaches have been proposed for the TS optimization, and they can be divided into two categories that are the TS optimization without and with SLTPs. For the TS optimization without SLTPs, many approaches have been proposed to solve the TS parameter optimization [14,26,34], incorporating TS in stock trading [1,3,20,25,33,36], and the TSP optimization [4,5,32,24].…”

Section: Review Of Trading Strategy Optimizationmentioning

confidence: 99%

“…Results showed that their approach can not only outperform the Turtle trading strategy but also has more stable returns [36]. Part et al proposed an intelligent financial portfolio trading strategy using the deep Q-learning [33]. In their approach, the deep Q-learning is employed to train the intelligent agent and identify the optimal trading action.…”

Section: Review Of Trading Strategy Optimizationmentioning

confidence: 99%

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RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

Chen

Díaz

et al. 2021

Electron Commer Res

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It is always difficult and challenge to obtain suitable trading signals for the desired securities in financial markets. The popular way to deal with it is through the use of trading strategies (TSs) made up of technical or fundamental indicators. Due to the different properties of TSs, an algorithm was proposed to find trading signals by obtaining the group trading strategy portfolio (GTSP), which is composed of strategy groups that can be employed to generate various TS portfolios (TSP) instead of a single TS. The stop-loss and take-profit points (SLTP) are widely utilized by shareholders to avoid massive losses. However, the appropriate SLTP is hard to set by users. Therefore, in this paper, the algorithm, namely GTSP-SLTP algorithm, is proposed to not only obtain a reliable GTSP but also find appropriate SLTP using the grouping genetic algorithm. A chromosome is encoded by the generated SLTP and GTSP along with the weights for strategy groups that are the SLTP, grouping, weight, and strategy parts. To assess the goodness of a chromosome, the evaluation function that consists of the group balance, weight balance, risk factor, and profit factor, is employed. Genetic operators are then performed to produce new solutions for next population. The genetic process is performed iteratively until the stop conditions have achieved. Last but not the least, empirical experiments were conducted on three financial datasets with different trends and a case study is also given to reveal the effectiveness and robustness of the designed GTSP-SLTP algorithm.

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Section: Review Of Trading Strategy Optimizationmentioning

confidence: 99%

Section: Review Of Trading Strategy Optimizationmentioning

confidence: 99%

RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

Chen

Díaz

et al. 2021

Electron Commer Res

View full text Add to dashboard Cite

show abstract

“…To improve the prediction performance, several techniques are proposed by stock prediction researcher. Combining techniques (ensemble) using several learning methods [46], [47], [48], [49], [31], [11], [4], [5]. Using boost algorithm (Boosting Method) [50], [51], [13], [34], [52].…”

Section: Proposed Methods Improvements and Modification For Stock Predictionsmentioning

confidence: 99%

“…The third type of work (Classification), classifies indicator data from a stock as "Buy", "Hold", or "Sell" through deep learning and neural network based classifications [31], [32], [33], [34], [16]. Prediction results can also represent an "Up" or "Down" trend so that investors can make decisions on investment entry positions by applying two single non-linear classifiers ANN, SVM and one RF ensemble approach to predict the direction of the next day's movement [35], [5], [36].…”

Section: Using a Clustering Algorithm Shares Will Be Grouped Against An Investment Decision Making Criterion (Clustering)mentioning

confidence: 99%

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Wiranata

Djunaidy

2021

Jurnal Ilmu Komputer dan Informasi

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This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization.

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“…To deal with this issue, they proposed a Bayesian approach for hyper-parameter tuning. Park et al [19] proposed an LSTM [20] network trained with deep Q-learning for stock market trading. Lei et al [21] proposed a similar approach using a GRU network trained with Policy Gradient.…”

Section: Literature Reviewmentioning

confidence: 99%

Reinforcement Learning with Self-Attention Networks for Cryptocurrency Trading

Betancourt

Chen

2021

Applied Sciences

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This work presents an application of self-attention networks for cryptocurrency trading. Cryptocurrencies are extremely volatile and unpredictable. Thus, cryptocurrency trading is challenging and involves higher risks than trading traditional financial assets such as stocks. To overcome the aforementioned problems, we propose a deep reinforcement learning (DRL) approach for cryptocurrency trading. The proposed trading system contains a self-attention network trained using an actor-critic DRL algorithm. Cryptocurrency markets contain hundreds of assets, allowing greater investment diversification, which can be accomplished if all the assets are analyzed against one another. Self-attention networks are suitable for dealing with the problem because the attention mechanism can process long sequences of data and focus on the most relevant parts of the inputs. Transaction fees are also considered in formulating the studied problem. Systems that perform trades in high frequencies cannot overlook this issue, since, after many trades, small fees can add up to significant expenses. To validate the proposed approach, a DRL environment is built using data from an important cryptocurrency market. We test our method against a state-of-the-art baseline in two different experiments. The experimental results show the proposed approach can obtain higher daily profits and has several advantages over existing methods.

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An intelligent financial portfolio trading strategy using deep Q-learning

Cited by 70 publications

References 34 publications

RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Reinforcement Learning with Self-Attention Networks for Cryptocurrency Trading

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