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Journal articles on the topic 'Automated trading systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Păuna, Cristian. "Reliable Signals and Limit Conditions for Automated Trading Systems." Review of Economic and Business Studies 11, no. 2 (2018): 9–20. http://dx.doi.org/10.1515/rebs-2018-0070.

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Abstract Automated trading software is a significant part of the business intelligence system in a modern investment company today. The buy and sell orders are built and sent almost instantly by computers using special trading and computational strategies. The trading decisions are made by automated algorithms. In this paper it will be presented one of these mathematical models which generate trading signals based only on the time price series. The algorithm combines several known computing techniques to build a trading indicator to automate the trades. With this method, buy decisions on overs

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Kandaurov, Dmitry. "HISTORY, CURRENT STATE, AND PROSPECTS FOR THE DEVELOPMENT OF AUTOMATED TRADING SYSTEMS IN THE STOCK MARKET." Bulletin of the South Ural State University series "Economics and Management" 19, no. 1 (2025): 88–98. https://doi.org/10.14529/em250107.

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The paper deals with the rapid development of automated trading systems, which requires a systematisation of knowledge about the methods, models and technologies they use. The subject of the study is automated trading on the stock market. The study of the history of automated trading systems allows to formulate the main hypothesis of the study: the development of automated trading systems based on quan-tum computing and machine learning methods can significantly increase the efficiency of algorithmic trad-ing. The study aims to summarise the existing knowledge, models and methods used by stock

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Juricek, Jan. "The Use of Artificial Intelligence in Building Automated Trading Systems." International Journal of Computer Theory and Engineering 6, no. 4 (2014): 326–29. http://dx.doi.org/10.7763/ijcte.2014.v6.883.

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Popławski, Paweł. "Connectivity Solutions in Automated Trading." International Journal of Electronics and Telecommunications 61, no. 4 (2015): 403–8. http://dx.doi.org/10.2478/eletel-2015-0053.

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Abstract The study analyzes the architecture and deployment of direct market access (DMA) solutions for automated trading of securities. It provides an overview of automated trading systems including: trading floor architecture, trading environment connectivity, and DMA solutions. Among a range of factors influencing operational capacities, round-trip latency has been recognized as the key quality differentiator of an automated trading floor. The study identifies potential opportunity costs due to latency levels as a major driver of technological progress in trading in highly liquid market con

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Salam, Abdus, Cut Eva Wani, Nasir, et al. "Pelatihan dan Sosialisasi Automated Trading Systems & Autopilot Manual Trading." Kawanad : Jurnal Pengabdian kepada Masyarakat 2, no. 1 (2023): 64–71. http://dx.doi.org/10.56347/kjpkm.v2i1.102.

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Automated Trading Systems & Autopilot Manual Trading training for 6 days on 10-15 January 2023 provides an understanding of the concepts and workings of ATS and AMT as well as risk management in trading. This training is expected to make participants more independent in trading and able to maximize the use of ATS and AMT to gain profits in their investments. In addition, this training is expected to make a positive contribution in increasing financial literacy and investment in society. However, this needs to be done in a structured and comprehensive manner so that capital market participa

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Tran, Minh, Duc Pham-Hi, and Marc Bui. "Optimizing Automated Trading Systems with Deep Reinforcement Learning." Algorithms 16, no. 1 (2023): 23. http://dx.doi.org/10.3390/a16010023.

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In this paper, we propose a novel approach to optimize parameters for strategies in automated trading systems. Based on the framework of Reinforcement learning, our work includes the development of a learning environment, state representation, reward function, and learning algorithm for the cryptocurrency market. Considering two simple objective functions, cumulative return and Sharpe ratio, the results showed that Deep Reinforcement Learning approach with Double Deep Q-Network setting and the Bayesian Optimization approach can provide positive average returns. Among the settings being studied

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Zhang, Can, Zhanxin Zhou, and Ruibo Wu. "Optimization of Automated Trading Systems with Deep Learning Strategies." Journal of Industrial Engineering and Applied Science 2, no. 4 (2024): 8–14. https://doi.org/10.5281/zenodo.12780180.

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Automated trading systems have revolutionized the financial markets by executing trades at speeds and frequencies far beyond human capabilities. The integration of deep learning strategies into these systems promises to enhance their performance by better predicting market movements and making more informed trading decisions. This paper explores various deep learning techniques applied to automated trading systems, examining their effectiveness, implementation challenges, and potential benefits. Specifically, we investigate the use of Convolutional Neural Networks (CNNs) for pattern recognitio

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Rushinek, Avi, and Sara Rushinek. "Automated portfolio trading systems forensic forecast audit." International Journal of Auditing Technology 2, no. 1 (2014): 55. http://dx.doi.org/10.1504/ijaudit.2014.064318.

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K, Suraj, Yogesh K, Malavya Manivarnnan, and Dr Mahesh Kumar Sarva. "Exploring Youth Perspectives on Algorithmic Trading: Knowledge, Trust, and Adoption." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43789.

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The increasing adoption of algorithmic trading has significantly transformed financial markets by enabling automated decision-making and high-speed trade execution. While institutional investors and hedge funds have widely embraced this technology, its understanding and acceptance among young retail investors, particularly those aged 18 to 25, remain relatively unexplored. As digital trading platforms and fintech innovations continue to gain popularity, assessing the awareness, perception, and preferences of youth regarding algorithmic trading is crucial. This study aims to examine the extent

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ALIEV, Beilak N. "Unification of automated trading systems using the principle of interface segregation." Financial Analytics: Science and Experience 17, no. 3 (2024): 359–66. http://dx.doi.org/10.24891/fa.17.3.359.

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Subject. The article discusses a model for unification of trading strategies architecture based on segregated software interfaces, which will improve the quality of testing of research works aimed at the development of quantitative trading strategies. Objectives. The purpose of the study is to formulate proposals for unification of the architecture of trading strategies for automatic trading systems based on the interface segregation principle. Methods. The study employs the method of empirical observation, analysis of analytical and expert information, practices of software engineering in sys

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Koliechkina, Liudmyla, and Vitalii Vuzii. "Analysis of the application of game theory methods in financial markets and opportunities for creating automatic exchange trading systems." Modeling and Information Systems in Economics, no. 103 (December 5, 2023): 104–16. https://doi.org/10.33111/mise.103.9.

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This discussion delves into the application of game theory principles, particularly Nash equilibrium, in the design of automated systems within financial markets. It explores various instances, including oligopolistic competition using the Cournot model, the impact of cooperation and competition among companies, and the dynamics of financial markets influenced by investor behaviors. Additionally, the narrative unfolds the integration of game theory in analyzing investment decisions, risk management, and the development of automated trading systems, such as High-Frequency Trading (HFT) algorith

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Czupryna, Marcin, and Paweł Oleksy. "The Effect of an Electronic Exchange on Prices and Return Volatility in the Fine Wine Market." e-Finanse 14, no. 4 (2018): 22–35. http://dx.doi.org/10.2478/fiqf-2018-0025.

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AbstractFine wine has become an attractive alternative asset class in recent decades. In our study, we take the market microstructural perspective and verify how innovations in trading infrastructure affect the fine wine market. More specifically, we examine the average prices and the return volatility of fine wines traded on three different trading systems: automated electronic exchange, auctions and over-the-counter agreements (the OTC market). Our findings confirm an important role of a fully automated, cost-effective wine exchange in improving pricing efficiency and reducing market risk. T

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Rzаiеvа, Svitlаnа, Dmуtrо Rzаiеv, Valeriy Kraskevich, Andrii Roskladka, and Volodymir Gamaliy. "AUTOMATED LOGISTIC FLOW SYSTEM FOR TRADING ENTERPRISE." Cybersecurity: Education, Science, Technique 3, no. 7 (2020): 72–84. http://dx.doi.org/10.28925/2663-4023.2020.7.7284.

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The article addresses one of the most pressing problems for a trading enterprise, namely the problem of routing logistics flows for efficient enterprise management. To optimize itineraries, we use state-of-the-art electronic navigation and mapping systems that can be implemented in modern computer technology, from online services available on any computer to mobile devices. Navigation and mapping systems are distributed software complexes with an array of information stored in database management systems. They involve millions of static objects and huge amounts of dynamic data around the world

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Li, Yeti, Catherine Burns, and Rui Hu. "Representing Stages and Levels of Automation on a Decision Ladder." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (2016): 328–32. http://dx.doi.org/10.1177/1541931213601074.

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We propose that representing stages and levels of automation on a decision ladder (DL) could help to identify information requirements for designing automation interfaces. We look at automated financial trading systems, a domain with variable degrees of automation (DOA). We give examples of modelling a financial trading task for two DOAs: basket trading (a low DOA) and trend following trading (a high DOA). On the resulting DLs, both human and automated information-processing activities are presented. The steps and states of knowledge allocated to automation are first categorized by the commonl

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Shukla, Pradip K., and Sashan Umashankar. "The Role of Advanced Technologies in Automated Trading Systems and Its Influence on Investor Attitudes." European Journal of Business and Management Research 10, no. 1 (2025): 8–16. https://doi.org/10.24018/ejbmr.2025.10.1.2542.

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This paper provides an overview of algorithmic trading (robo-trading), which replaces human stockbroker recommendations to investors for buying/selling/holding stocks in their investment portfolio, and the integration of technologies like artificial intelligence and quantum computing within automated systems. The paper covers the impacts of algorithmic trading, the specific implementations of advanced technologies, and the acceptance of these technologies among college students. The cited articles show that robo-trading has existed since the 1970s. Algorithmic trading has increased stock marke

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Zhou, Yingya, Chin Hao Chong, Weidou Ni, Zheng Li, Xiaoyong Zhou, and Linwei Ma. "Data Modeling and Synchronization Method to Align Power Trading Rules for Integrated Energy Management Systems." Sustainability 16, no. 20 (2024): 9073. http://dx.doi.org/10.3390/su16209073.

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Integrated energy systems (IESs) couple multiple energy sources to promote clean energy and reduce emissions. IESs need to participate in business activities, such as power trading, aided by automated data-driven systems to achieve optimal and economical operation. However, challenges arise due to the lack of unified data model standards and the semantic ambiguity of rules, on top of the difficulty of synchronizing data across heterogeneous subsystems of integrated energy management systems (IEMSs). Previous research on power trading data models was limited to certain application scenarios and

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Plotnikov, Arkadiy P., and Roman A. Shishlov. "Algorithm for combining automated trading systems to jointly achieve the target result." Vestnik of Samara University. Economics and Management 15, no. 1 (2024): 113–28. http://dx.doi.org/10.18287/2542-0461-2024-15-1-113-128.

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Automated trading systems (trading robots) used to make transactions in financial markets do not always provide stable results. The article proposes a universal algorithm that provides a high ratio of profitability and risk in the automatic management of an investment portfolio. The algorithm involves dividing the investment portfolio into several parts, each of which is controlled by an individual trading robot. Each robot gets to manage a part of the investment portfolio, the size of which is calculated according to the chosen method. The rapid automatic adjustment of the size of these parts

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Vezeris, Dimitrios, Themistoklis Kyrgos, Ioannis Karkanis, and Vasiliki Bizergianidou. "Automated trading systems’ evaluation using d-Backtest PS method and WM ranking in financial markets." Investment Management and Financial Innovations 17, no. 2 (2020): 198–215. http://dx.doi.org/10.21511/imfi.17(2).2020.16.

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Given the popularity and propagation of automated trading systems in financial markets among institutional and individual traders in recent decades, this work attempts to compare and evaluate such ten systems based on different popular technical indicators in combination – for the first time – with the d-Backtest PS method for parameter selection. The systems use the technical indicators of Moving Averages (MA), Average Directional Index (ADX), Ichimoku Kinko Hyo, Moving Average Convergence/Divergence (MACD), Parabolic Stop and Reverse (SAR), Pivot, Turtle and Bollinger Bands (BB), and are enh

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Păuna, Cristian. "Reliable Signals Based on Fisher Transform for Algorithmic Trading." Timisoara Journal of Economics and Business 11, no. 1 (2018): 87–102. http://dx.doi.org/10.2478/tjeb-2018-0006.

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Abstract Trading and investment on financial markets are common activities today. A very high number of investors, companies, public or private funds are buying and selling every day with a single purpose: the profit. The common questions for any market participant are: when to buy, when to sell and when is better to stay away from the market risk. In order to answer all these questions, many trading strategies are used to establish the best moments to entry or to exit the trades. Due to the large price volatility, a significant part of the trades is set up automatically today by computers usi

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Saepudin, Deni, and Khalifatur Rauf. "Application of Deep Reinforcement Learning for Stock Trading on The Indonesia Stock Exchange." Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 14, no. 1 (2025): 144–57. https://doi.org/10.23887/janapati.v14i1.83775.

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In the last couple of years, stock trading has gained so much popularity because of its promising returns. However, most investors do not pay attention to the risks of trading without analysis, which can lead to a big loss. Some to reduce these risks, try their luck with automated and pre-programmed trading systems, which are called Expert Advisors. The current study examines the application of DRL for automated assistance in trading with an emphasis on decision-making enhancement, particularly the use of DRL in order to realize high asset returns with a low risk of exposure. Concretely, the t

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Cervesato, Iliano, Sharjeel Khan, Giselle Reis, and Dragiša Žunić. "Formalization of Automated Trading Systems in a Concurrent Linear Framework." Electronic Proceedings in Theoretical Computer Science 292 (April 15, 2019): 1–14. http://dx.doi.org/10.4204/eptcs.292.1.

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Rajesh Dey. "Machine Learning-Based Automated Trading Strategies for the Indian Stock Market." Journal of Information Systems Engineering and Management 10, no. 26s (2025): 782–93. https://doi.org/10.52783/jisem.v10i26s.4285.

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The Indian stock market is highly volatile and influenced by various macroeconomic factors, making it challenging for traders to consistently achieve profitable trades using traditional methods. Machine learning (ML)-based automated trading systems offer an intelligent, data-driven approach to analyzing historical market trends, detecting patterns, and executing trades with minimal human intervention. This study explores the application of ML techniques in developing automated trading strategies tailored for the Indian stock market. The research focuses on supervised learning methods, such as

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Rundo, Trenta, di Stallo, and Battiato. "Grid Trading System Robot (GTSbot): A Novel Mathematical Algorithm for trading FX Market." Applied Sciences 9, no. 9 (2019): 1796. http://dx.doi.org/10.3390/app9091796.

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Grid algorithmic trading has become quite popular among traders because it shows several advantages with respect to similar approaches. Basically, a grid trading strategy is a method that seeks to make profit on the market movements of the underlying financial instrument by positioning buy and sell orders properly time-spaced (grid distance). The main advantage of the grid trading strategy is the financial sustainability of the algorithm because it provides a robust way to mediate losses in financial transactions even though this also means very complicated trades management algorithm. For the

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Singla, Ashutosh. "Exploring Liquidity Pooling and Automated Trading with COREDAOVIP Token in Decentralized Exchanges." Scientific Journal of Metaverse and Blockchain Technologies 2, no. 2 (2024): 1–12. http://dx.doi.org/10.36676/sjmbt.v2.i2.26.

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The emergence of decentralized finance (DeFi) has transformed traditional financial systems by leveraging blockchain technology to offer decentralized solutions for trading and liquidity provision. Within the CORE Chain ecosystem, the COREDAO VIP token plays a pivotal role in facilitating liquidity pooling and automated trading across various COREDAO-based tokens. This research investigates the impact of COREDAO VIP token within decentralized exchanges (DEX) such as ICECREAMSWAP, LFGSWAP, SHADOWSWAP, and ARCHERSWAP. By analyzing its integration into these platforms, the study explores how CORE

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Nakayama, Makoto. "E-Commerce and Firm Bargaining Power Shift in Grocery Marketing Channels: A Case of Wholesalers’ Structured Document Exchanges." Journal of Information Technology 15, no. 3 (2000): 195–210. http://dx.doi.org/10.1177/026839620001500303.

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Electronic commerce is transforming interorganizational relationships in marketing channels. Reports frequently note that ‘power’ goes to those who gain relevant trading information in marketing channels. The paper then asks whether and how structured or ‘automated’ trading information exchanges (e.g. electronic data interchange (EDI) links) between suppliers and wholesalers impact on their bargaining power in grocery marketing channels. This paper starts with the view that automated information exchanges favour the suppliers because they obtain trading information and gain marketing flexibili

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Raudys, Sarunas. "Portfolio of Automated Trading Systems: Complexity and Learning Set Size Issues." IEEE Transactions on Neural Networks and Learning Systems 24, no. 3 (2013): 448–59. http://dx.doi.org/10.1109/tnnls.2012.2230405.

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Ramon de Cerqueira Silva and Carlos Alberto Rodrigues. "Optimizing Automated Trading Systems Portfolios with Reinforcement Learning for Risk Control." JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH 7, Suppl2 (2025): 31–38. https://doi.org/10.34178/jbth.v7isuppl2.449.

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This work proposes an innovative method for optimizing Automated Trading Systems (ATS) portfolios with advanced Deep Reinforcement Learning (DRL) techniques. The algorithms A2C, DDPG, PPO, SAC, and TD3 are assessed for their ability to learn and adapt in volatile market conditions. The main goal is to enhance ATS's risk control and operational efficiency using data from the Brazilian stock market. DRL models outperformed traditional benchmarks by offering better risk management and risk-adjusted returns. The findings demonstrate the potential of DRL algorithms in complex financial scenarios an

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Andriushchenko, Kateryna, Inna Riepina, Andrii Buriachenko, and Oksana Kyryliuk. "Determining the capabilities of artificial intelligence on the development of cryptotrading and blockchain technology." Technology audit and production reserves 3, no. 4(83) (2025): 42–52. https://doi.org/10.15587/2706-5448.2025.330463.

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The object of research is crypto trading and blockchain technologies, including the development of trading bots and the implementation of automated risk management systems. AI includes the development of trading bots, the implementation of automated risk management systems, and the use of predictive analytics tools that optimize trading operations. This is important, as AI technology plays a crucial role in the rapid analysis of huge amounts of data to predict market prices and trading opportunities, thereby increasing the efficiency of investments. In addition, AI provides investors with real

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Vinay Kumar Deeti. "Generative Artificial Intelligence in finance." International Journal of Science and Research Archive 12, no. 1 (2024): 3206–15. https://doi.org/10.30574/ijsra.2024.12.1.1091.

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Generative Artificial Intelligence drives substantial financial industry transformations because it helps develop and automate high-quality risk analysis models from both data-based selection and detailed automated operations. Through generative AI platforms financial institutions receive protection against fraud and achieve both automated trading solutions and custom financial systems using synthetic data platforms. Generative AI creates three core problems for financial services when used in their operations including model biases alongside privacy risks for safeguarding data along with conf

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Sungatullin, Rais G. "AUTOMATION OF FINANCIAL ANALYTICS: THE IMPACT OF BI SYSTEM IMPLEMENTATION ON MANAGERIAL DECISION-MAKING." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 5/8, no. 158 (2025): 5–14. https://doi.org/10.36871/ek.up.p.r.2025.05.08.001.

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The growing volume of trading conducted by automated systems has fundamentally changed the landscape of financial markets, making algorithmic trading one of the key drivers of structural transformation. This article aims to identify the economic implications of widespread adoption of algorithmic strategies in market infrastructure and analyze the associated ethical challenges. It explores changes in liquidity, spreads, volatility, and pricing speed due to the activity of high-frequency and program-controlled trading systems. Alongside the benefits of operational efficiency and reduced transact

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Goyal, Hemanshu, Nikhil Chandel, Abhishek Maurya, and Shailja. "AI-Powered Autonomous Trading: Enhancing Market Efficiency Through Predictive Analytics." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 377–86. http://dx.doi.org/10.22214/ijraset.2024.65001.

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Abstract: In the realm of financial markets, AI-powered autonomous trading systems are revolutionizing the way trades are executed and decisions are made. This paper presents a formal approach to developing an AI-powered trading system aimed at enhancing market efficiency through predictive analytics. The proposed system integrates machine learning algorithms, including deep learning and reinforcement learning, to analyze vast amounts of historical and real-time market data. Emphasis is placed on improving decision-making speed, accuracy, and risk management through techniques such as algorith

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Fiemotongha, Joyce Efekpogua, Abbey Ngochindo Igwe, Chikezie Paul Mikki Ewim, and Ekene Cynthia Onukwulu. "Mitigating market volatility: Advanced techniques for enhancing stability and profitability in energy commodities trading." International Journal of Management and Organizational Research 3, no. 1 (2024): 131–48. https://doi.org/10.54660/ijmor.2024.3.131-148.

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The energy commodities market is inherently volatile, driven by geopolitical events, supply-demand imbalances, regulatory changes, and macroeconomic factors. This volatility poses significant challenges for traders, investors, and policymakers, necessitating advanced methodologies to stabilize trading outcomes and improve profitability. This study explores cutting-edge techniques for mitigating market fluctuations in energy commodities trading, focusing on algorithmic trading strategies, predictive analytics, risk management frameworks, and portfolio optimization models. One of the primary tec

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Currie, Wendy L., and Jonathan J. M. Seddon. "The Regulatory, Technology and Market ‘Dark Arts Trilogy’ of High Frequency Trading: A Research Agenda." Journal of Information Technology 32, no. 2 (2017): 111–26. http://dx.doi.org/10.1057/s41265-016-0025-3.

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Computerization has transformed financial markets with high frequency trading displacing human activity with proprietary algorithms to lower latency, reduce intermediary costs, enhance liquidity and increase transaction speed. Following the “Flash Crash” of 2010 which saw the Dow Jones Industrial Average plunge 1000 points within minutes, high frequency trading has come under the radar of multi-jurisdictional regulators. Combining a review of the extant literature on high frequency trading with empirical data from interviews with financial traders, computer experts and regulators, we develop c

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Ismayilov, M., and Z. Maharramov. "ANALYSIS OF THE EFFICIENCY OF AUTOMATION OF THE PROCESSES OF TRADING ENTERPRISES DEPENDING ON THE MARKET AND THE SIZE OF THE TRADING ENTERPRISE." Sciences of Europe, no. 118 (June 9, 2023): 34–38. https://doi.org/10.5281/zenodo.8019813.

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This article examines the automation of trade turnover in the oil and gas sector. The study is aimed at studying the advantages, disadvantages and difficulties of automation taking into account the type of activity of companies in this field, as well as to identify factors affecting the adoption and implementation of automated systems.

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Locatelli, Jerry, and Guang Wei Liu. "Automated Metal Siphoning and Cast House Energy Consumption." Materials Science Forum 630 (October 2009): 61–69. http://dx.doi.org/10.4028/www.scientific.net/msf.630.61.

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The transfer of molten aluminium from the potline crucible to the holding furnace is receiving renewed attention as pressure to reduce energy wastage in the form of dross is heightened by the prospect of Emissions Trading. Siphoning has long been recognised as the most loss efficient method of metal transfer and now with the availability of fully engineered, automated systems which are safe and reliable in addition to reducing melt loss by 75%; this method is set to become the industry standard. This paper presents some examples of automated siphon transfer systems and demonstrates the benefit

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Cohen, Gil. "Polynomial Moving Regression Band Stocks Trading System." Risks 12, no. 10 (2024): 166. http://dx.doi.org/10.3390/risks12100166.

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In this research, we attempted to fit a trading system based on polynomial moving regression bands (MRB) to Nasdaq100 stocks from 2017 till the end of March 2024. Since stocks movement does not follow a linear behavior, we used multiple degree polynomial regression models to identify the stocks’ trends and two standard deviations from the regression model to generate the trading signals. This way, the MRB was transformed into a momentum indicator designed to identify strong uptrends that can be used by a fully automated trading system. Our results indicate that the behavior of Nasdaq100 stocks

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Sai Ashish, Malla Venkata. "Algorithmic Trading Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48961.

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Abstract: The Algorithmic Trading using Machine Learning is a financial trading system that integrates technical analysis indicators and future predictions with news sentiment analysis through reinforcement learning algorithms. The system trains autonomous trading agents to make effective buy, sell, or hold decisions in complex market environments by combining historical stock data, technical indicators, and sentiment analysis from financial news. It implements and compares three state-of-the-art reinforcement learning algorithms: Proximal Policy Optimization (PPO), Deep Q-Network (DQN), and A

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Ren, Fenghui, Minjie Zhang, and Kwang Mong Sim. "Adaptive conceding strategies for automated trading agents in dynamic, open markets." Decision Support Systems 46, no. 3 (2009): 704–16. http://dx.doi.org/10.1016/j.dss.2008.11.005.

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Avdeenko, Alex, and A. S. Melnichenko. "Use of the ARCH(1) Model Peculiarities for Creation of Automated Trading Systems." SOP Transactions on Statistics and Analysis 2014, no. 1 (2014): 9–16. http://dx.doi.org/10.15764/stsa.2014.01002.

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Huang, Boming, Yuxiang Huan, Li Da Xu, Lirong Zheng, and Zhuo Zou. "Automated trading systems statistical and machine learning methods and hardware implementation: a survey." Enterprise Information Systems 13, no. 1 (2018): 132–44. http://dx.doi.org/10.1080/17517575.2018.1493145.

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Vrînceanu, Bogdan-Petru, and Florentin Șerban. "From Data to Decisions: A Trading Bot for Ethereum." International Journal of Research and Scientific Innovation XII, no. II (2025): 107–18. https://doi.org/10.51244/ijrsi.2025.12020010.

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This research focuses on the development and evaluation of an automated trading strategy for cryptocurrency markets, specifically Ethereum, using Pine Script version 5 on the Trading View platform. The strategy incorporates a variety of technical indicators, including the Relative Strength Index (RSI), Commodity Channel Index (CCI), Average True Range (ATR), Directional Movement Index (DMI), Aroon Indicator, and Exponential Moving Average (EMA), to capture diverse market conditions such as trend direction, volatility, and overbought/oversold levels. To enhance decision-making, a multiple logis

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Priya Gupta. "Trading Analysis of Law Observation System: A Comprehensive Study of Market Regulatory Frameworks in the Digital Age." Journal of Information Systems Engineering and Management 9, no. 4s (2024): 23–40. https://doi.org/10.52783/jisem.v9i4s.10989.

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The panorama of trading activities has been drastically changed by the unparalleled revolution of global financial markets brought about by technology development, thereby demanding sophisticated regulatory control systems. With special focus on the technological infrastructure allowing real-time monitoring and regulatory compliance, this thorough study explores the complex link between automated trading systems and legal observation frameworks inside modern financial markets. This paper shows how integrated legal observation systems have transformed the method of market surveillance and regul

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Vezeris, D. Th, and C. J. Schinas. "Performance Comparison of Three Automated Trading Systems (MACD, PIVOT and SMA) by Means of the d-Backtest PS Implementation." International Journal of Trade, Economics and Finance 9, no. 4 (2018): 170–73. http://dx.doi.org/10.18178/ijtef.2018.9.4.609.

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Syed Tanveer Alam. "The convergence of artificial intelligence, blockchain and fintech in energy, oil and gas trading: Increasing efficiency, transparency and automations." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 2064–73. http://dx.doi.org/10.30574/wjarr.2024.22.2.1625.

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Artificial intelligence (AI) and blockchain technology, in conjunction with Financial Technology (Fintech), are playing an increasingly significant role in shaping global Energy Trading trends. The combined use of blockchain, AI, and Fintech introduces innovative features that have the potential to improve the efficiency and performance of current systems. This study provides insights into the key features of the intersection of AI, blockchain, and Fintech, such as data security, encryption, sharing, efficiency, collective decision-making, decentralized intelligent systems, transparency, autom

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Syed, Tanveer Alam. "The convergence of artificial intelligence, blockchain and fintech in energy, oil and gas trading: Increasing efficiency, transparency and automations." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 2064–73. https://doi.org/10.5281/zenodo.14716060.

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Artificial intelligence (AI) and blockchain technology, in conjunction with Financial Technology (Fintech), are playing an increasingly significant role in shaping global Energy Trading trends. The combined use of blockchain, AI, and Fintech introduces innovative features that have the potential to improve the efficiency and performance of current systems. This study provides insights into the key features of the intersection of AI, blockchain, and Fintech, such as data security, encryption, sharing, efficiency, collective decision-making, decentralized intelligent systems, transparency, autom

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Nuij, Wijnand, Viorel Milea, Frederik Hogenboom, Flavius Frasincar, and Uzay Kaymak. "An Automated Framework for Incorporating News into Stock Trading Strategies." IEEE Transactions on Knowledge and Data Engineering 26, no. 4 (2014): 823–35. http://dx.doi.org/10.1109/tkde.2013.133.

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Kelvin Edem Bassey, Shahab Anas Rajput, and Kabir Oyewale. "Peer-to-peer energy trading: Innovations, regulatory challenges, and the future of decentralized energy systems." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 172–86. http://dx.doi.org/10.30574/wjarr.2024.24.2.3324.

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Peer-to-peer (P2P) energy trading represents a transformative approach to energy distribution, where consumers, referred to as prosumers, generate and exchange electricity directly with one another. This decentralized model promotes local generation and consumption balancing, reducing reliance on centralized grids and encouraging the use of renewable energy. The development of platforms and technologies, such as blockchain, smart contracts, and Internet of Things (IoT)-)-enabled devices, has enabled secure, transparent, and automated transactions. These innovations facilitate real-time monitor

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Kelvin, Edem Bassey, Anas Rajput Shahab, and Oyewale Kabir. "Peer-to-peer energy trading: Innovations, regulatory challenges, and the future of decentralized energy systems." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 172–86. https://doi.org/10.5281/zenodo.15074623.

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Peer-to-peer (P2P) energy trading represents a transformative approach to energy distribution, where consumers, referred to as prosumers, generate and exchange electricity directly with one another. This decentralized model promotes local generation and consumption balancing, reducing reliance on centralized grids and encouraging the use of renewable energy. The development of platforms and technologies, such as blockchain, smart contracts, and Internet of Things (IoT)-)-enabled devices, has enabled secure, transparent, and automated transactions. These innovations facilitate real-time monitor

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Фрайс, В. Э., та Е. Б. Белых. "Роль автоматизированных систем в аспекте развития рынка ценных бумаг в условиях становления биржевой политики". Industrial Economics, S1 (18 грудня 2024): 149–52. https://doi.org/10.47576/2949-1886.2024.93.28.022.

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В статье исследуются автоматизированные торговые системы (АТС) и их роль в современных финансовых рынках. Рассматриваются теоретические аспекты и основные принципы работы АТС, включая алгоритмическую и высокочастотную торговлю. Проведен анализ их влияния на структуру рынка, ликвидность и волатильность, а также обсуждаются потенциальные риски. Предлагаются меры по регулированию АТС для минимизации системных рисков и повышения стабильности финансовых систем. The article examines automated trading systems (PBX) and their role in modern financial markets. The theoretical aspects and basic principl

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Currie, Wendy L., Jonathan J. J. M. Seddon, and Ben Van Vliet. "From decision optimization to satisficing: Regulation of automated trading in the US financial markets." Information & Management 59, no. 8 (2022): 103721. http://dx.doi.org/10.1016/j.im.2022.103721.

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