International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
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INTELLISENSE STOCK STRATEGIST TAVVA T N V S S R MANIKNATA1, Dr. M. KAVITHA2, JI-HAN JIANG3 1UG Student, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute
of Science and Technology, Chennai, Tamil Nadu, India
2 Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute
of Science and Technology, Chennai, Tamil Nadu, India
3Associated Professor, Department of Computer Science and Information Engineering, National Formosa
University, Huwei Township, Yunlin County 632, Taiwan ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The IntelliSense Stock Strategist is a cutting-edge
framework capable of navigating the complexities of financial markets with amazing precision and agility. The system also incorporates results from Rather's LSTM-based Deep Learning Model for Stock Prediction [2], which employs recurrent neural networks to detect temporal correlations and trends in stock market data.
platform that aims to reinvent stock market forecasting. Our system adjusts to changing market conditions in real time, using advanced deep learning algorithms such as Deep QNetworks (DQN), to provide traders with timely insights and recommendations. The IntelliSense Stock Strategist creates a strong predictive model capable of capturing a wide range of market variables by merging numerous deep learning agents via ensemble modelling. Adaptive learning mechanisms, comprehensive risk management tactics, and real-time insights for informed decision-making are among its key features. In this study, we provide a full description of the framework's design, algorithmic components, and experimental validation results. Our findings illustrate the efficacy and practicality of the IntelliSense Stock Strategist in boosting stock market forecasting accuracy and equipping traders to achieve consistent success in their investment Strategies
The IntelliSense Stock Strategist forecasts market patterns and swings with remarkable accuracy, allowing investors to make informed decisions in real time. Based on Gandhmal and Kumar's thorough examination of stock market prediction approaches [3][4], the IntelliSense Stock Strategist takes a comprehensive, data-driven approach to forecasting. The framework discovers the most effective approaches for predicting stock prices by synthesizing empirical studies and literature reviews and incorporating them into its predictive models. This rigorous study assures that the IntelliSense Stock Strategist remains at the forefront of financial forecasting, always evolving and reacting to the most recent innovations in the sector.
Key Words: Machine learning, Strategic decisions, Buysell-hold, User-friendly design, Financial markets
Furthermore, the system makes use of transfer learning concepts, as outlined in Wu et al.'s research on modelling transfer learning of industrial chain information and deep learning for stock prediction [5]. By leveraging pre-trained models and integrating knowledge from relevant fields, the IntelliSense Stock Strategist may effectively incorporate industrial chain information into its predictive models, enhancing prediction skills and resilience. Furthermore, findings from Semiromi et al.'s study on forecasting foreign currency prices based on news events [6] help the system adjust to real-time market dynamics. By integrating news sentiment analysis and event-based forecasting techniques, the IntelliSense Stock Strategist can capture market sentiment and respond swiftly to emerging trends and changes, ensuring that investors receive the most relevant and up-to-date information.
1.INTRODUCTION The IntelliSense Stock Strategist is at the forefront of financial innovation, poised to revolutionize stock market forecasting by incorporating cutting-edge deep learning technology. Based on modern computational methodologies, this framework represents a paradigm shift in how investors approach decision-making in the volatile and often unexpected world of finance. At its core, the IntelliSense Stock Strategist is inspired by a wide range of research and techniques, providing a synthesis of insights garnered from important publications in financial forecasting. The application of deep reinforcement learning techniques is critical to its methodology, as illustrated by Aboussalah and Lee's work on continuous control utilizing Stacked Deep Dynamic Recurrent Reinforcement Learning [1]. The IntelliSense Stock Strategist uses the adaptability and
The IntelliSense Stock Strategist, which uses deep learning algorithms pioneered by Ansari and Khan [7] and Fu et al. [12], provides investors with actionable insights derived from extensive stock market data research. By combining innovative methodologies such as tree-based classifiers [10] and deep learning applications [11], the platform provides
Self-learning capabilities inherent in deep reinforcement learning architectures to deliver a dynamic and adaptive
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