Stock Sage

Abstract

Stock market has been constant fascinating topic but since last few years’ stockholders desire to hardback return on day-to-day basis got glorified, then with the support of machine learning stockholders initiate stress-free approaches to get squint of forthcoming market trends. This Project benevolences Reinforcement Learning grounded methodology to forecast the characteristics of a specific stock, our method put greater concentration on sets of hundred days moving averages. Hundred days moving average is a technical method followed by the stock market professionals to forecast the forthcoming trends of market, which represents the current as well as the characteristics which is stock going to show in upcoming days. As we are scraping data with the API and creating our own dataset hence our method is comfortable with ambiguous data besides it provides output with high accuracy. The results indicate that our method attained superior upshot than other approaches.

Introduction

The financial market enables trading of currencies, stocks, and derivatives via virtual platforms, offering investors opportunities to grow wealth with relatively lower risk than starting a business. Stock markets are influenced by many unpredictable factors, leading to high volatility. Automated Trading Systems (ATS), powered by machine learning and complex algorithms including Reinforcement Learning (RL), Convolutional Neural Networks (CNN), and Gated Recurrent Units (GRU), can execute trades faster and more efficiently than humans, though human oversight remains essential for risk management.

Stock price prediction is a key research area, involving traditional statistical models (like ARIMA), machine learning methods (such as Random Forest, SVM), and deep learning models (LSTM, CNN). Sentiment analysis of social media and news has been integrated to enhance predictive power. Despite advances, challenges remain, including data noise, overfitting, model interpretability, and translating results into live trading contexts.

This project uses Reinforcement Learning to predict stock prices by collecting historical stock data, preprocessing it with sliding windows, and training an RL model optimized via hyperparameter tuning. The model is integrated into a chatbot interface that provides real-time stock price forecasts and interactive user queries, enhancing accessibility and usability.

The system employs linear regression and machine learning algorithms within a two-layer architecture: backend data processing and user interface layers. It offers users daily predictions of stock trends, visualizations, and personalized interactions via the chatbot named "Stock Sage."

Results show the chatbot provides a modern, user-friendly experience with 92% prediction accuracy, outperforming traditional static interfaces (85%). The inclusion of LSTM, sentiment analysis, and real-time updates improves prediction reliability and user engagement, suggesting strong potential for AI-driven stock market applications.

Conclusion

The Stock Sage project successfully developed a stock prediction model using ReinforcementLearning(RL) techniques. By leveraging algorithms like Q-learning and DeepQ-Networks(DQN), the model learned optimal trading strategies through simulation, providing a dynamic approach to predicting stock price movements. While promising, the model\'s performance can be further improved by incorporating additional factors like market sentiment and external data sources. Overall, StockSage demonstrates the potential of RL in financial forecasting and lays the foundation for future advancements in stock market prediction.

References

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Copyright

Copyright © 2025 Renuka Deshpande , Abhishek Mahadik , Mahesh Wagh, Avin Walunj. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.