Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading

Lei, Kai; Zhang, Bing; Yu, Li; Yang, Min; Shen, Ying

doi:10.1016/j.eswa.2019.112872

Cited by 85 publications

(60 citation statements)

References 19 publications

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“…The evaluation metrics, total profits (TP), annualized rate of return (AR), annualized Sharpe ratio (SR) and transaction times (TT), highlighted that the proposed TFJ-DRL outperforms other competitors. Also, the reported results in [48] confirmed that the proposed framework could be applied on real data stock market to achieve reliable results. Further interesting results in the field of HFT trading algorithms and time-series forecasts, have been recently obtained by applying some deep-learning approaches based on the morphological-temporal analysis of the data initially applied in the medical and industrial sector for the study of one-dimensional aperiodic physiological signals (very close to financial time-series) [56][57][58].…”

Section: Discussionsupporting

confidence: 64%

“…In order to confirm the effectiveness of DL, we reported performance results of two surveyed works [17,48] based on applying a recurrent model (LSTM) and the RL approach, respectively. The work of Fischer et al, proposed a LSTM-based model.…”

Section: Discussionmentioning

confidence: 99%

“…In terms of annualized risk-return, LSTM achieves highest returns (0.8229) compared to random forest, DNN, etc. We reported results of [48] in which the authors applied a time-driven feature-aware jointly deep reinforcement-learning model (TFJ-DRL) to improve the financial signal representation learning. They considered stocks from S&P 500 to evaluate the proposed model which takes advantages of Reinforcement Learning paradigm.…”

Section: Discussionmentioning

confidence: 99%

“…In [48] the authors presented a time-driven feature-aware jointly deep reinforcement learning (RL) model (TFJ-DLR) in order to learn features representation considering noisy financial time series. Furthermore, the proposed method is developed to perform a decision-making system by taking advantage of environmental representation.…”

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

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Machine Learning for Quantitative Finance Applications: A Survey

et al. 2019

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The analysis of financial data represents a challenge that researchers had to deal with. The rethinking of the basis of financial markets has led to an urgent demand for developing innovative models to understand financial assets. In the past few decades, researchers have proposed several systems based on traditional approaches, such as autoregressive integrated moving average (ARIMA) and the exponential smoothing model, in order to devise an accurate data representation. Despite their efficacy, the existing works face some drawbacks due to poor performance when managing a large amount of data with intrinsic complexity, high dimensionality and casual dynamicity. Furthermore, these approaches are not suitable for understanding hidden relationships (dependencies) between data. This paper proposes a review of some of the most significant works providing an exhaustive overview of recent machine learning (ML) techniques in the field of quantitative finance showing that these methods outperform traditional approaches. Finally, the paper also presents comparative studies about the effectiveness of several ML-based systems.

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Section: Discussionsupporting

confidence: 64%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

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Machine Learning for Quantitative Finance Applications: A Survey

et al. 2019

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“…Lien Minh et al [53] also claim that Stock2Vec is very efficient in financial datasets. Lei et al [37] with combining deep learning models and reinforcement learning models develop a time-driven feature-aware (TDFA) jointly deep reinforcement learning model (TFJ-DRL) for financial time-series forecasting in algorithmic trading. Preeti et al [51] introduce an extreme learning machine (ELM)-auto-encoder (AE) model to find the patterns in the financial time series.…”

Section: Other Algorithmsmentioning

confidence: 99%

Data Science in Economics

Nosratabadi¹,

Mosavi²,

Duan³

et al. 2020

Preprint

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This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The novel data science methods and applications are investigated in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide range of economics research, from stock market, marketing, E-commerce, to corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on the advancement of hybrid models. On the other hand, based on the accuracy metric it is also reported that the hybrid models outperform other learning algorithms. It is further expected that the trends would go toward the advancements of sophisticated hybrid deep learning models.

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A hybrid multicategory framework for generating automated trading systems

Kumar

Sahoo

Thakur

2023

Concurrency and Computation

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This work proposes a framework for producing automatic trading systems that suggest making trading decisions in financial markets. The trading systems thus generated utilize multicategory classifiers. A collection of important technical indicators are considered as input features for the underlying multicategory classifiers. The proposed trading systems also have an option to use random forest (RF) algorithms for feature selection. The trading range breakout strategy is used to train the proposed trading systems thus generating “BUY/SELL/WAIT” trading signals on daily open prices. The performances of the proposed trading systems are evaluated over five future indices. Empirical findings suggest that the day trading systems based on the proposed multicategory SVM classifiers along with the RF technique outperform the day trading systems built using the multicategory classifiers taken from the literature. The trading range breakout strategy‐based systems are found to be superior to the traditional BUY‐HOLD strategy and that of RF‐PSVM (BUY/SELL) based strategy.

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Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading

Cited by 85 publications

References 19 publications

Machine Learning for Quantitative Finance Applications: A Survey

Machine Learning for Quantitative Finance Applications: A Survey

Data Science in Economics

A hybrid multicategory framework for generating automated trading systems

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