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31 articles for “Algorithmic trading(algo-trading)”
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Algorithmic Trading Using Artificial Intelligence and Machine Learning Algorithms
Abstract: Algorithmic trading conducts trades quickly and effectively using algorithms that follow a trend and predetermined set of instructions. In addition, algorithmic trading reduces the influence of human emotions on trading, leading to increased market liquidity and more precise and accurate trading. Automated trading for day-to-day trading, depending on varied market situations, the bot will automatically trade user strategies in addition to its own algorithms, providing the best trade turnover, reducing …
Published in Current Trends in Information Technology · Vol. 13, Issue 2, 2023 · pp. 23–28 Read article
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Using Elliot Wave Theory and Fibonacci Retracement and in Algorithmic Trading
Abstract: The analysis technique used in predicting stock prices consists of Fundamental analysis and Technical analysis. These analyses are complex in nature and usually unreliable. The algorithmic trading systems offered today consists of primitive technical analysis techniques such as, simple moving averages, exponential moving average, moving averages convergence and diversions and volume weighted average price. The primitive nature of this techniques makes them very unreliable and has a low success rate. …
Published in Trends in Machine design · Vol. 7, Issue 3, 2020 · pp. 22–29 Read article
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Exploring the Efficiency of Leading and Lagging Indicators in Algorithmic Trading
Abstract: This paper details a comparison of the overall performance of leading and lagging technical indicators used in algorithmic trading over an extended period. While much of the prior research focuses on index price forecasting and some on statistical arbitrage derived from these predictive techniques, there is a scarcity of studies that assess and evaluate trading strategies. The strategies considered for the study were tested on historical data of the 50 …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 2, 2024 · pp. 8–18 Read article
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Algorithmic Strategies for Complex Data Handling: Optimizing Data Structures for Enhanced Computational Performance
Abstract: We live in an age of big data and processing very large often complicated datasets can be crucial to efficient algorithmic performance. This paper discusses different algorithmic techniques when working with difficult data and how to arrange your information structures correctly for better functionality in large-scale methods. It checks the impact of different algorithms like sorting, searching, and hashing in boosting its processing speed as well as memory use. This …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 2, 2024 · pp. 1–10 Read article
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Data-Driven Predictive Analytics and Decision- Making in FinTech Using MongoDB and High-Throughput Data Pipelines
Abstract: This paper examines the implementation of MongoDB and high-throughput data pipelines within the financial technology (FinTech) sector to drive data-informed predictive analytics and decision-making. The study focuses on the architectural components, scalability, and challenges of integrating NoSQL databases into real-time data ingestion and analytics pipelines. The transformative potential of these technologies in modern financial systems is highlighted through practical use cases such as fraud detection, credit scoring, and personalized financial …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 1, 2025 · pp. 1–15 Read article
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Emotions and Artificial Intelligence in Finance: Exploring the Relationship
Abstract: The integration of Artificial Intelligence (AI) into financial systems has profoundly transformed the industry, providing unprecedented efficiency, accuracy, and speed in decision-making processes. These technological advancements have streamlined operations, reduced human errors, and enabled more informed decision-making based on vast datasets analyzed in real-time. However, the role of emotions in finance remains a critical factor that cannot be ignored. Human emotions, such as fear, greed, and optimism, frequently drive market …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 15, Issue 1, 2025 · pp. 11–17 Read article
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Implementation of K-Means clustering algorithm with technical indicators to identify Profitable stocks
Abstract: In the ever-changing world of stock market trading, accurately predicting price movements is key to maximizing profits. Technical analysis, which looks at past price data to predict future trends, provides valuable insights for investors. This paper delves into using machine learning methods, particularly the K-Means clustering algorithm, along with moving average data, to categorize daily trading patterns. By breaking down the market into clusters and examining the main patterns within …
Published in Current Trends in Signal Processing · Vol. 13, Issue 3, 2023 · pp. 8–14 Read article
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Comparison of Models of Machine Learning and Hyperparameter Optimization Methods on Various Datasets
Abstract: The most likely phase in achieving powerful and robust machine learning models is probably the hyperparameter tuning step. The traditional exhaustive methods of search (grid search and others) ensure that the search space is covered, but are computationally inexpensive; random search is less expensive and can still miss good regions; and lastly, the modern model-based and population-based methods (Bayesian optimization, tree-structured Parzen estimator (TPE), genetic algorithms) are thought to provide …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 · pp. 35–42 Read article
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AI-Driven Multi-Objective Optimization of Conductive Polymer Composites for High-Performance Flexible Electronics
Abstract: The development of conductive polymer composites (CPCs) is critical for advancing flexible and wearable electronic technologies. However, the conventional trial-and-error approach to material formulation is time-consuming and often inefficient due to the high-dimensional nature of the design space. This study introduces a novel AI-driven framework that integrates machine learning (ML) with multi-objective optimization to accelerate the discovery of high-performance CPCs. A dataset of 1,000 experimentally reported formulations was compiled, capturing …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 734–745 Read article
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Multi-Objective Optimization of Polymer-Based Functionally Graded Composites for Lightweight Structures
Abstract: Functionally graded composites (FGCs) improve lightweight structural performance by allowing material properties to change smoothly across a component. Polymer-based FGCs (P-FGCs), in particular, are gaining prominence in aerospace, automotive, and biomedical industries due to their excellent strength-to-weight ratio, tunability, and ease of processing. However, optimizing these materials for lightweight structural applications requires addressing conflicting design objectives, such as maximizing stiffness while minimizing weight or enhancing thermal resistance while maintaining manufacturability. …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 961–973 Read article
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A Hybrid Algorithm for Processor Scheduling Using Game Theory Variants
Abstract: This study proposes a novel hybrid algorithm for processor scheduling in modern operating systems, integrating the strengths of traditional scheduling methods with game theory variants. Traditional schedulers often struggle to adapt to dynamic workload changes, leading to suboptimal performance. Our hybrid approach addresses this by treating processes as "players" in a game, where the "payoff" is CPU time. A base scheduler (e.g., Weighted Fair Queuing, Earliest Deadline First) provides a …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 1, 2025 · pp. 48–56 Read article
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Artificial Intelligence in Trigonometry: Innovations, Applications, and Future Prospects
Abstract: Artificial Intelligence (AI) has transformed numerous scientific fields, yet its integration with classical mathematics such as trigonometry is still emerging. This paper explores how AI enhances trigonometric problem solving, learning, and real-world applications. We analyse AI-driven tools for teaching trigonometry, AI in geometric and spatial reasoning, usage in robotics and computer vision, and future directions for research. Key challenges, methodologies, and case studies are discussed to provide a comprehensive overview …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Artificial Intelligence-Based Optimization of Mechanical and Biocompatible Properties in Polymer Composite Implants
Abstract: Artificial Intelligence (AI) has already become a ground-breaking tool of streamlining polymer composite implants to enhance both mechanical strength and biocompatibility simultaneously. This paper recommend an AI-based multi-objective optimization model, which integrates the selection of materials, structural modelling, and biological evaluation. The in vitro biocompatibility indicators, including cytotoxicity and cell adhesion, can be used to model mechanical behavior, e.g. stress-strain behavior and fatigue behavior. To arrive at an optimal material …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Approximation-Aware Computation for Graceful QoS Degradation in Modern Multiprocessor Operating Systems
Abstract: Modern multiprocessor operating systems face unprecedented challenges in maintaining Quality of Service (QoS) guarantees under dynamic workload conditions and resource constraints. Traditional approaches to resource management often result in abrupt service degradation or complete task failure when system resources become scarce. This study presents a comprehensive framework for approximation-aware computation that enables graceful QoS degradation in multiprocessor environments. We explore the integration of approximate computing paradigms with operating system schedulers, …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 3, 2025 · pp. 08–15 Read article
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Performance Evaluation of Adaptive MIMO-OFDM System Model for Wireless Networks
Abstract: Enormous growth of wireless technologies and research trends in networking has developed many useful applications. Wireless networks with various protocols and standards are huge in demand because of the expansion of internet of things (IoT). All wireless networks demand for high data rate, low energy consumption, higher throughput, more reliability, better quality of services (QoS) and quality of information (QoI). This paper presents multiple in multiple out (MIMO) orthogonal frequency …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 4, Issue 1, 2017 · pp. 17–23 Read article
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Legendry in Remote Sensing Imagery
Abstract: AbstractRemote sensing is processing platform originated devices. Air borne allocation plat sensors at airplanes while space born allocate at satellites. Since satellites are in queue in circulation morphological, therefore as a portion whole clump is constructive almost by sensors only. No whelm powered sensor or active origins ever procure power transmission systems. Same in case of high-altitude platforms (HAPs) analogized out from super jet stream liner concord consume power to …
Published in Recent Trends in Sensor Research & Technology · Vol. 6, Issue 3, 2019 · pp. 6–12 Read article
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Enhancing Energy Storage and Optimization of Distributed Energy Resources Using a Hybrid SWOA-MSNN Approach
Abstract: The fast growth of Distributed Energy Resources (DERs) like solar photovoltaics, wind power, and energy storage devices requires enhanced optimization methods to manage energy efficiently and stabilize operations in contemporary smart grids. A significant challenge is the dynamic optimization of the energy storage systems (ESS) and the distribution of the energy among DERs in conditions of uncertainty of loads and generation. The conventional control and optimization methods generally find it …
Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 2, 2026 Read article
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Data Compression for Backbone Network
Abstract: This article involves the application of data compression techniques to improve the efficiency and performance of the core infrastructure of modern digital networks. This approach focuses on reducing the size of transmitted data without compromising its quality, aiming to enhance network throughput, reduce latency, and minimize energy consumption. The study also considers practical implementation challenges and trade-offs to optimize resource utilization in backbone networks. We delve into various compression methods, …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 1, 2024 · pp. 30–40 Read article
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An Overview of Privacy-Preserving Data Encryption Techniques in Mobile Cloud Computing for Big Data
Abstract: With the introduction of mobile cloud computing (MCC), data processing, storage, and sharing have undergone a radical transformation that has greatly improved organizational effectiveness and quality of life. But there are also serious worries about data security and privacy due to the increasing usage of mobile devices and cloud computing, particularly when managing large amounts of data from many sources like sensors and cellphones. The privacy issues surrounding MCC are …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 1, 2025 · pp. 1–7 Read article
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Optimization of Robotic Path Planning Algorithms for Autonomous Material Handling Systems
Abstract: For autonomous systems for handling materials (AMHS) to operate as efficiently as possible in industrial and logistical settings, robotic route planning is essential. This study examines many robotic route planning algorithms, emphasizing their use, ways of optimization, and difficulties in material handling systems. To improve the effectiveness, precision, and computational viability of these algorithms, the study also examines a number of optimization strategies, including machine learning, parallelization, heuristic search, and …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 2, Issue 2, 2024 · pp. 15–20 Read article