• Harshal Kuldharan
• Prof. Swati Kadu
• Siddhesh Kulthe
• Atharva Kakade
• Yash Gavhane

Stock price prediction is a challenging yet rewarding task in financial markets due to the highly volatile and complex nature of stock data. Recent advancements in machine learning and deep reinforcement learning (DRL) have opened up promising avenues to address these challenges. This project presents an approach to stock price prediction using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, optimized with the Adam optimizer, which aims to increase accuracy in predicting price trends and optimizing investment decisions In the rapidly evolving field of algorithmic trading, the application of reinforcement learning (RL) algorithms has shown promising results. However, there is a lack of comprehensive analysis of RL algorithms across diverse stock markets. This study addresses the gap by investigating the effectiveness of Twin Delayed Deep Deterministic Policy Gradient (TD3) in stock trading scenarios using data from the NASDAQ

Machine learning, Twin Delayed Deep Deterministic Policy Gradient, Adam Optimizer

"Stock prediction using Twin Delayed Deep Deterministic Policy Gradient Algorithm with Adam Optimizer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.b705-b711, June-2025, Available :http://www.jetir.org/papers/JETIR2506190.pdf

"Stock prediction using Twin Delayed Deep Deterministic Policy Gradient Algorithm with Adam Optimizer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppb705-b711, June-2025, Available at : http://www.jetir.org/papers/JETIR2506190.pdf