International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Financial markets generate large volumes of fast- moving data and require decisions to be taken in very short intervals. Human traders alone struggle to monitor all signals and react consistently without emotional bias. To address this challenge, this work presents an AI driven trading bot that combines Machine Learning (ML) for price forecasting with Reinforcement Learning (RL) for action selection. The system uses technical indicators, a Long Short-Term Memory (LSTM) network for short-term prediction and a Deep Q-Network (DQN) agent to learn profitable buy, sell and hold policies. The complete solution is deployed as a web application that provides real- time charts, portfolio analytics, sentiment summaries and AI- generated trading signals. Experimental evaluation indicates promising accuracy, low-latency inference and improved profit consistency when compared with simple rule-based strategies.

Keywords: Stock Market, Machine Learning, Reinforcement Learning, LSTM, DQN, Trading Bot, Financial Analytics.

Downloads: | DOI: 10.17148/IJARCCE.2025.1412132