StockForesight: A Dash-Based System for Visualization and Forecasting of Stock Prices

Abstract

This study aims to provide a comprehensive solution for the analysis of stocks in response to the growing dynamism and complexity of financial markets. Through the incorporation of cutting-edge machine learning algorithms with the flexible features of the yfinance library, the project seeks to provide users with robust prediction capabilities. By using a model based on Support Vector Regression (SVR), the system improves the precision of stockpredictions and offers insightful information on the dynamics of market sentiment. In addition, putting an interactive Dash application live on Heroku guarantees users easy accessibility and real-time, well-informed decision-making. With an emphasis on user-friendly design and advanced analytical features, this project represents a significant advancement in stock market prediction tools, catering to the evolving needs of financial analysts and investors in navigating the ever-changing market landscape.

Introduction

This project introduces an interactive, web-based dashboard—built using Dash and deployed on Heroku—to forecast stock prices with the help of machine learning techniques. The solution integrates real-time financial data (via the yfinance library), Support Vector Regression (SVR) for stock prediction, and Exponential Moving Averages (EMA) for trend analysis. The system aims to make financial analytics accessible, accurate, and user-friendly for global users.

Core Objectives:

• Develop an interactive stock forecasting system with machine learning.

• Use SVR for accurate regression-based stock price predictions.

• Enhance visualization and usability using Dash and Plotly.

• Ensure cloud-based access by deploying the application on Heroku.

Key Features:

• Real-time stock data collection using yfinance.

• Dynamic user input for stock symbols and date ranges.

• Interactive visualizations using Plotly in the Dash interface.

• Forecasting via SVR, optimized through hyperparameter tuning (C, gamma).

• EMA applied to show smoothed trends in price history.

• System tested and debugged for robustness and accuracy.

• Deployed globally using Heroku for universal accessibility.

Literature Survey Insights:

The literature review examined various modern approaches to stock price prediction, including:

1. LSTM with sentiment analysis.

2. Feature selection and machine learning for Chinese markets.

3. Decision fusion strategies.

4. Data fusion techniques using quantized time series.

5. Image-based prediction via Deep Q-Networks.

6. Hybrid Red Deer-Grey optimization for ensemble models.

7. ARIMA for time-series volatility forecasting.

8. Graph Attention Networks (ML-GAT) for relational learning.

Most studies demonstrate effective prediction capabilities but suffer from limitations like data dependency, market specificity, or lack of real-world deployment.

Methodology Overview:

• Data Collection: Using yfinance to fetch historical prices and metadata.

• Prediction Model: SVR trained with past stock prices; kernel functions and parameters tuned for best results.

• EMA Calculation: Highlights price trends using exponential smoothing.

• Visualization: Dash and Plotly used to build a user-friendly interface.

• User Interaction: Accepts live user input to customize forecasts.

• Testing & Debugging: Comprehensive evaluation to ensure system stability.

• Deployment: Hosted on Heroku to allow real-time access across devices.

Results & User Experience:

• "Stock Dash App" provides a seamless and engaging interface for stock data visualization and forecasting.

• Users can:

  • Input stock symbols.

  • Set date ranges.

  • View EMA indicators.

  • Receive predictive analytics instantly.

• The system demonstrates reliability and performance through testing and user interaction.

Conclusion

Our project, the “Visualizing and Forecasting of Stocks using Dash”, successfully integrates data visualization and predictive modeling into a single, user-friendly platform. This application serves as a comprehensive tool for stock market analysis, providing users with both historical data and future predictions. The application’s interactive interface allows users to customize their viewing experience, enabling them to gain detailed insights into the historical performance of any selected stock. The integration of a Support Vector Regression (SVR) model enhances the application’s capabilities by providing accurate forecasts of future stock prices. We intend to add more sophisticated features and predictive models in the future to improve the user experience overall and the accuracy of our projections. With its existing features and upcoming improvements, we think our program will remain a useful resource for anybody interested in stock market analysis.

References

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Copyright

Copyright © 2025 Dr. Chayadevi M L, B T Mayur, Aryan Raj. 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.