TY - GEN
T1 - Leveraging Machine Learning Techniques for Enhanced Algorithmic Trading Strategies
AU - Nayak, Aditi
AU - Chetia, Barish Priyam
AU - Bhadrike, Ishaan
AU - Kapadia, Jil
AU - Parashar, Deepak
AU - Mandal, Gouranga
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - People have always been at the heart of financial markets, bringing in their analytical talent, intuition and decision-making talents to devise through their extremely complex, volatile and dynamic nature. Nonetheless, as the field of artificial intelligence (AI) develops extremely fast, companies and investors are starting to investigate new opportunities of automated and data-driven trading. AI-based trading bots are now actively involved in financial markets using machine learning (ML) algorithms and natural language processing (NLP) methods to scan data in large amounts, detect patterns, and trade at speed and accuracy. The paper discusses the benefits of using AI-based trading bots, explains the main goals of the project, and discusses the development of algorithmic trading in detail as it becomes less manual and automated than intelligent.
AB - People have always been at the heart of financial markets, bringing in their analytical talent, intuition and decision-making talents to devise through their extremely complex, volatile and dynamic nature. Nonetheless, as the field of artificial intelligence (AI) develops extremely fast, companies and investors are starting to investigate new opportunities of automated and data-driven trading. AI-based trading bots are now actively involved in financial markets using machine learning (ML) algorithms and natural language processing (NLP) methods to scan data in large amounts, detect patterns, and trade at speed and accuracy. The paper discusses the benefits of using AI-based trading bots, explains the main goals of the project, and discusses the development of algorithmic trading in detail as it becomes less manual and automated than intelligent.
UR - https://www.scopus.com/pages/publications/105035746894
UR - https://www.scopus.com/pages/publications/105035746894#tab=citedBy
U2 - 10.1109/AISP68263.2025.11396194
DO - 10.1109/AISP68263.2025.11396194
M3 - Conference contribution
AN - SCOPUS:105035746894
T3 - 2025 5th International Conference on Artificial Intelligence and Signal Processing, AISP 2025
BT - 2025 5th International Conference on Artificial Intelligence and Signal Processing, AISP 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Artificial Intelligence and Signal Processing, AISP 2025
Y2 - 22 November 2025 through 24 November 2025
ER -