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High-frequency trading is a method of intervention on the financial markets that uses sophisticated software tools, and sometimes also hardware, with which to implement high-frequency negotiations, guided by mathematical algorithms, that act on markets for shares, options, bonds, derivative instruments, commodities, and so on. HFT strategies have r...
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Citations
... More recent literature review on the machine learning adapted to the forex can be found in [32]. A high impact of deep models based on neural networks like LSTM is also considered in high-frequency trading systems [24], [31], [33], [38]. ...
This paper presents a multi-criteria optimization approach for decision support in the forex market. Decision-makers using technical analysis encounter numerous signals generated by market indicators across various instruments‗currency pairs. However, typical online trading systems often lack the capability to validate these signals within a multi-criteria space. This paper introduces a system concept that evaluates signals according to different efficiency measures. A corresponding multi-criteria optimization problem is formulated and solved to select only Pareto-optimal decision variants concerning these efficiency measures. The proposed concept has been implemented using different efficiency measures in an experimental computer-based system and tested through numerical experiments, yielding positive results.
... The accurate prediction of the direction of exchange rate fluctuations has greater practical relevance and application value. 4. Most studies (Samuels & Sekkel, 2017;Das et al., 2019;Rundo, 2019;Yıldırım et al., 2021;Solat & Tsang, 2021;Feng et al., 2023) have overlooked the robustness of models in handling uncertainty shocks. If a forecasting model performs well only under specific conditions but fails or shows instability when confronted with uncertainty, its practicability is constrained. ...
The foreign exchange market significantly affects international trade, making accurate exchange rate predictions essential for investors, businesses, and government organizations. In this study, we propose an AI-driven ensemble model called the multi-model dynamic prediction system (MMDPS) to enhance exchange rate forecasting accuracy, prediction direction consistency, and prediction robustness. To that end, we implemented a series of measures. First, the submodels of MMDPS apply fuzzy prediction, chaos prediction, deep learning, and linear time-series forecasting methods to enhance the information extraction capability of MMDPS. Second, we introduced an innovative hybrid wavelet denoising method to enhance the ability of MMDPS to handle noise in exchange rate data. Third, we employed a novel multi-objective metaheuristic optimization algorithm to determine the optimal combination weights, optimizing across the dimensions of predictive accuracy and directional consistency. This approach further enhances the generalization capability and predictive robustness of MMDPS. Finally, we incorporated into MMDPS multiple variables that are closely related to exchange rates and can rapidly reflect market changes, such as panic indices and commodity futures prices, to enhance the model’s predictive robustness against uncertainty shocks. The predictive performance of MMDPS was validated using 15 datasets with varying frequencies. The results of our experiments demonstrate that MMDPS significantly outperforms the benchmark models in terms of prediction accuracy, consistency in prediction direction, and predictive robustness. Furthermore, MMDPS can significantly enhance investor returns and mitigate investment risks, which underscore its critical application value.
... Figure 1 presents an overview of the architecture and functional concept of A-Trader. 1 W3C SOAP, https://www.w3.org/TR/soap/ The main goal of the Supervisor Agent (SA) is to generate profitable trading advice to achieve a specific rate of return and reduce investment risk. ...
Trading decisions often encounter risk and uncertainty complexities, significantly influencing their overall performance. Recognizing the intricacies of this challenge, computational models within information systems have become essential to support and augment trading decisions. The paper introduces the concepts of trading software agents, investment strategies, and evaluation functions that automate the selection of the most suitable strategy in near real-time, offering the potential to enhance trading effectiveness. This approach holds the promise of significantly increasing the effectiveness of investments. The research also seeks to discern how changing market conditions influence the performance of these strategies, emphasizing that no single agent or strategy universally outperforms the rest. In summary, the overarching objective of this research is to contribute to the realm of financial decision-making by introducing a pragmatic platform and strategies tailored for traders, investors, and market participants in the FOREX market. Ultimately, this endeavor aims to empower people with more informed and productive trading decisions. The contributions of this work extend beyond the theoretical realm, demonstrating a commitment to address the practical challenges faced by traders and investors in real-time decision-making within the financial markets. This multidimensional approach to financial decision support promises to enhance investment effectiveness and contribute to the broader field of algorithmic trading.
... Meanwhile, the application of reinforcement learning in conjunction with RNNs has gained attention in financial forecasting. Rundo [153] combined RL with LSTM to develop a trading strategy that maximizes returns. Their model learned optimal trading actions through interactions with the market environment, resulting in a robust and adaptive financial forecasting system. ...
Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state networks (ESNs), peephole LSTM, and stacked LSTM. The study examines the application of RNNs to different domains, including natural language processing (NLP), speech recognition, time series forecasting, autonomous vehicles, and anomaly detection. Additionally, the study discusses recent innovations, such as the integration of attention mechanisms and the development of hybrid models that combine RNNs with convolutional neural networks (CNNs) and transformer architectures. This review aims to provide ML researchers and practitioners with a comprehensive overview of the current state and future directions of RNN research.
... These findings show that the algorithm can be used as a more precise instrument to forecast the Fintech index (Liu et al., 2021). High frequency trading (HFT) algorithms are robust and effective, this is shown by the fact that the whole prediction system, which includes the deep learning block with RL framework corrections, can boost trend forecast accuracy to roughly 85% (Rundo, 2019). Additional insights on network connections may be gained by utilizing the high-resolution information contained in high-frequency intraday trading data sets. ...
The fintech segment is currently one of the most rapidly growing industries, attracting numerous investors who anticipate substantial returns in the future. Notably, not only individual retail investors but also mutual fund agencies are actively engaged in predicting stock prices within this sector to maximize their trading gains. The purpose of the study is to formulate stock forecasting models for top three Fintech Companies of India i.e., Policy Bazar, One 97 Communications Paytm Ltd., and Niyogin Ltd. Using Random Forest model with high-frequency data in Python. The literature review section also proves that this study is a novel piece of work as none of the existing research study focused on predicting stock prices of Fintech Companies of India using Random Forest model. The data is extracted from www.moneycontrol.com and www.kotaksecurities.com, for the period from 1st October, 2022 – 30th September, 2023. The study deals about 293,280 data points i.e., 3 companies @ 97,760 each. It has been found that the forecasting model of random forest provides very successful results for prediction as the co-efficient of determination of all the selected companies is more than 95%.
... 2016); (Mourelatos et al., Sep. 2018); (Zhu et al., Dec. 2018); (Azhikodan et al., 2019); (Attanasio et al., 2019); (Paiva et al., Jan. 2019); (Sarmento and Horta, Nov. 2020); (Tsai et al., 2020); (Wu et al., Oct. 2020); (Sun et al., Oct. 2020); ; (Makarov et al., 2021); (Kim, Aug. 2021); (Liu, Jan. 2022); (Nasirtafreshi, 2022); (Jaquart et al., Nov. 2022); (Gupta et al., 2022); (Murtza et al., 2022) 8.5 15 (Chu and Chan, 2018); (Hushani, 2019); (Rundo, Oct. 2019); (Lucarelli and Borrotti, May 2019); (Yuan et al., 2020); (Badr et al., 2020); (Weng et al., Aug. 2020); (Brim, Jan. 2020); (Betancourt and Chen, 2021); ; (Nalmpantis et al., 2021); (Hwang et al., Dec. 2023); (Xu and Zhang, Jun. 2023); (Ayitey Junior et al., 2022); (Zhang et al., 2023) 8.75 10 (Maratkhan et al., 2019); ; (Sattarov, et al., 2020); (Taroon et al., 2020); (Xu and Tan, Nov. 2020); (Chen and Huang, Nov. 2021); (Borrageiro et al., 2022); (Kuo et al., 2021); (Corletto et al., 2021); (Nan et al., 2022); (Chantarakasemchit and Nuchitprasitchai, 2021) 9 20 (Żbikowski, 2016); (Almahdi and Yang, Nov. 2017); (Shin et al., 2019); (Tsantekidis et al., Jul. ...
Artificial Intelligence (AI) approaches have been increasingly used in financial markets as technology advances. In this research paper, we conduct a Systematic Literature Review (SLR) that studies financial trading approaches through AI techniques. It reviews 143 research articles that implemented AI techniques in financial trading markets. Accordingly, it presents several findings and observations after reviewing the papers from the following perspectives: the financial trading market and the asset type, the trading analysis type considered along with the AI technique, and the AI techniques utilized in the trading market, the estimation and performance metrics of the proposed models. The selected research articles were published between 2015 and 2023, and this review addresses four RQs. After analyzing the selected research articles, we observed 8 financial markets used in building predictive models. Moreover, we found that technical analysis is more adopted compared to fundamental analysis. Furthermore, 16% of the selected research articles entirely automate the trading process. In addition, we identified 40 different AI techniques that are used as standalone and hybrid models. Among these techniques, deep learning techniques are the most frequently used in financial trading markets. Building prediction models for financial markets using AI is a promising field of research, and academics have already deployed several machine learning models. As a result of this evaluation, we provide recommendations and guidance to researchers.
... In the literature, there is a large bibliography used different techniques and methods [4]- [6] shows success. The model proposed in this paper is characterized by using various contributing techniques in the aim to obtain a good prediction accuracy of currency exchange rates, the added value of this model consists in combining several types of machine learning (supervised learning and reinforcement learning (RL)) [7], [8]. Optimization method [9] and also technical analysis [10], [11] through the relative strength index (RSI) indicator. ...
... It was also shown that the model obtained from the training procedure can then be harnessed for profitable trading in atest dataset. Rundo [8] proposes in this work the use of an algorithm based both on supervised deep learning and on a RL algorithm for forecasting the short-term trend in the currency Forex market to maximize the return on investment in an high-frequency trading (HFT) algorithm. The trading system has been validated over several financial years and on the EUR/USD cross confirming the high performance in terms of return of investment (98.23%) in addition to a reduced drawdown (15.97%) which confirms its financial sustainability. ...
... Following the most of conclusions reached from the bibliographic research, the combination of two or more techniques can show better results [8], [9], [21] in terms of the exchange rate prediction accuracy, the proposed model in this paper combines more than one method to predict exchange rates: − Multiple regression for its ability to determine the relative influence of one or more predictor variables on the value of the criterion. − Simulated annealing to avoid getting locking in a local optimum. ...
Foreign exchange market refers to the market in which currencies from around the world are traded. It allows investors to buy or sell a currency of their choice. Forex interests several categories of stakeholders, such as companies that carry out international contracts, large institutional investors, via the main banks, which carry out transactions on this market for speculative purposes. One of the most important aspects in the Forex market is knowing when to invest by buying, selling, and this through the recorded trend of a currency pair, but given the characteristics of the Forex market namely its chaotic, noisy and not stationary nature, prediction becomes a big challenge for traders when it comes to predicting accuracy. This paper aims to predict the right action to be taken at a certain moment through the development of a model that combines multiple techniques such multiple regression, simulated annealing meta-heuristics, reinforcement learning and technical indicators.
... Despite the advantages of using deep reinforcement learning for trading, current literature [7,8,9,10,11] is limited to using the historical data related to one particular security being traded with very few exceptions [12,13]. The tendency to heavily depend on the historical data of one particular security may not be as effective as observing the time series of multiple securities where more useful relationships and trading signals can be exploited. ...
... [10] also proposed an algorithm that can trade IF contracts as well as silver (AG) and sugar (SU) contracts, and ES was also studied in [11] along with two stocks (JPM and MSFT). Other studies such as [12] focused on foreign exchanges (EUR/USD) and [13] focused on trading the 30 stocks held in the Dow Jones Industrial Average. Starting from futures contracts of various assets to stocks and foreign exchanges, current literature studied applications of deep reinforcement learning for trading financial securities in various markets. ...
... Although [9] studied both the futures market and stock market, only two futures contracts and three stocks were studied. Likewise, [10] only studied three futures contracts, [11] only studied one futures contract and two stocks, and [12] focused on one specific foreign exchange. In fact, the 30 stocks studied in [13] cannot be considered enough because those 30 stocks held in the Dow Jones Industrial Average are mainly value stocks having similar market behavior and volatility. ...
This paper proposes generalized deep reinforcement learning with multivariate state space, discrete rewards, and adaptive synchronization for trading any stock held in the S&P 500. Specifically, the proposed trading model observes the daily historical data of a stock held in the S&P 500 and multiple market-indicating securities (SPY, IEF, EUR=X, GSG), selects a trading action, and observes a discrete reward that is based on the correctness of the selected action and independent of the volatility of stocks. The proposed trading model’s reward-maximizing behavior is optimized by using a standard deep q-network (DQN) with adaptive synchronization that stabilizes and enables to track learning performance on generalizing new experiences from each stock. The proposed trading model was trained on the top 50 holdings of the S&P 500 and tested on the top 100 holdings of the S&P 500 starting from 2006 to 2022. Experimental results suggest that the proposed trading model significantly outperforms the 100% long-strategy benchmark in terms of annualized return, Sharpe ratio, and maximum drawdown.
... The classification is carried out in the training set, and the threshold λ and ρ are chosen for each stock so that the two groups have the same size (Rundo 2019). These thresholds are then applied to the test set to classify the returns, and the mean and variance of each group are calculated (Yoshihara et al. 2014). ...
The role of the stock market in the whole financial market is indispensable. How to obtain the actual trading income and maximize the interests in the trading process has been a problem studied by scholars and financial practitioners for a long time. Deep learning network can extract features from a large number of original data, which has potential advantages for stock market prediction. Based on the Shanghai and Shenzhen stock markets from 2019 to 2021, we use LSTM models, optimized on in-sample period and tested on out-of-sample period, using rolling window approach. We select the right hyperparameters at the beginning of our tests, use RBM preprocessing data, then use LSTM model to obtain expected stock return, to effectively predict future stock market analysis and predictive behavior. Finally, we perform a sensitivity analysis of the main parameters and hyperparameters of the model.
... It proved to outperform the return from the standard buy-and-hold strategy with the Kuala Lumpur Composite Index (KLCI) as a proxy for the stock market [12]. In recent years, [13] has proved that the deep LSTM can predict short term trend in foreign exchange rates and leads to increased profits and reduced drawdowns. ...
Algorithmic trading is a common topic researched in the neural network due to the abundance of data available. It is a phenomenon where an approximately linear relationship exists between two or more independent variables. It is especially prevalent in financial data due to the interrelated nature of the data. The existing feature selection methods are not efficient enough in solving such a problem due to the potential loss of essential and relevant information. These methods are also not able to consider the interaction between features. Therefore, we proposed two improvements to apply to the Long Short-Term Memory neural network (LSTM) in this study. It is the Multicollinearity Reduction Module (MRM) based on correlation-embedded attention to mitigate multicollinearity without removing features. The motivation of the improvements is to allow the model to predict using the relevance and redundancy within the data. The first contribution of the paper is allowing a neural network to mitigate the effects of multicollinearity without removing any variables. The second contribution is improving trading returns when our proposed mechanisms are applied to an LSTM. This study compared the classification performance between LSTM models with and without the correlation-embedded attention module. The experimental result reveals that a neural network that can learn the relevance and redundancy of the financial data to improve the desired classification performance. Furthermore, the trading returns of our proposed module are 46.82% higher without sacrificing training time. Moreover, the MRM is designed to be a standalone module and is interoperable with existing models.
