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1.0.0

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Release v1.0.0 - Euclid: Predictive Analytics Platform

Release Date: February 20, 2025

Overview

We are excited to announce the initial release of Euclid: Predictive Analytics Platform! This release establishes a robust foundation for financial forecasting, interactive analytics, and a seamless REST API integration. Euclid leverages advanced statistical models, real-time data acquisition, and an intuitive dashboard to empower users with actionable financial insights.

New Features

  • Advanced Time Series Forecasting:

    • Integration of ARIMA, SARIMA, and Exponential Smoothing models for accurate financial predictions.

  • Real-Time Financial Data:

    • Fetches up-to-date market data from Yahoo Finance using yfinance.

  • Sentiment Analysis:

    • Utilizes TextBlob to analyze market sentiment and provide contextual insights.

  • Interactive Dashboard:

    • Built with Streamlit and Plotly, offering dynamic visualizations and an intuitive user interface.

  • REST API Integration:

    • Flask-based backend provides endpoints for data processing, prediction generation, and sentiment analysis.

  • Robust Error Handling:

    • Incorporates Tenacity for automatic retry logic and extensive logging for improved system stability.

  • Modular Architecture:

    • Clear separation between backend and frontend components for enhanced maintainability and future scalability.

Improvements

  • Performance Enhancements:

    • Optimized data retrieval and processing for faster response times.

  • User Experience:

    • Enhanced UI elements in the dashboard for better usability and clearer data visualization.

  • Documentation:

    • Comprehensive README and project documentation have been added to facilitate easy setup and customization.

Bug Fixes

  • Resolved initial integration issues with data fetching from Yahoo Finance.
  • Fixed minor bugs in API endpoints to ensure consistent data retrieval.
  • Improved exception handling across both backend and frontend modules.

Known Issues

  • High-load scenarios require further testing to ensure optimal performance.
  • Some forecasting parameters may need fine-tuning for specific edge cases.
  • Continuous improvements for sentiment analysis accuracy are in progress.

Installation & Setup

For complete installation and setup instructions, please refer to the README.md. Highlights include:

  • Cloning the Repository: 
    git clone https://github.com/your-username/Euclid-Computer.git
cd Euclid-Computer
  • Installing Dependencies: 
    pip install -r requirements.txt
  • Running the Backend: 
    cd Backend
python Euclids_backend.py
  • Running the Frontend: 
    cd ../FrontEnd
streamlit run EuclidFrontEnd.py

Future Roadmap

  • Enhanced Machine Learning Models:

    • Integration of deep learning models for improved forecasting accuracy.

  • Expanded Data Sources:

    • Support for additional financial data providers and extended market data.

  • User Authentication:

    • Implementation of secure user login for personalized dashboards.

  • Mobile Application Support:

    • Development of a mobile-friendly version for on-the-go analytics.

  • Automated Reporting:

    • Automation of daily market summaries and report generation.

Acknowledgments

A heartfelt thanks to our contributors, the open-source community, and all users who provided feedback during the development phase. Your support and collaboration have been invaluable in bringing Euclid to life.

For more detailed information, please refer to our [README.md](README.md). If you have any questions or feedback, feel free to open an issue on our [GitHub repository](https://github.com/your-username/Euclid-Computer).

Thank you for using Euclid!

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