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Market and Historical data points for analysing market trends and human movements through time.

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VIBE - Visual Interactive Bloomberg Experience

VIBE is a modern, interactive financial data visualization platform that combines market data with news sentiment analysis to provide insights into market trends and human sentiment over time.

Project Overview

VIBE provides a comprehensive view of financial markets by integrating:

  • Interactive timeline visualization
  • Market data (OHLCV) charts
  • News article analysis with sentiment scoring
  • Correlation between market movements and news sentiment

Core Modules

1. Frontend (React)

  • Timeline component using D3.js/Recharts
  • Market charts (candlestick + volume)
  • News bubble overlays (clickable)
  • Sentiment line overlay
  • Drill-down modal with news list and AI summaries

2. Backend (FastAPI)

  • Time-based queries
  • Data endpoints for market data, news, and sentiment
  • News article summaries
  • Sentiment scoring pipeline

3. Data Layer

  • Market Data: IEX Cloud / Quando
  • News Data: GDELT CSVs
  • Databases:
    • PostgreSQL for structured data
    • TimescaleDB for time-series data

4. Processing Scripts

  • GDELT CSV processing
  • Sentiment scoring (VADER or BERT)
  • News-to-ticker matching
  • Database storage utilities

Getting Started

Prerequisites

  • Docker and Docker Compose
  • Git

Installation

  1. Clone the repository:
git clone https://github.com/MetaBundleAutomation/Bloomberg-Terminal.git
cd Bloomberg-Terminal
  1. Start the development environment:
docker-compose up
  1. Access the application:

Development

Frontend Development

cd frontend
npm install
npm start

Backend Development

cd backend
pip install -r requirements.txt
uvicorn app.main:app --reload

Data Processing

cd data_processing
pip install -r requirements.txt
python process_gdelt.py

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Market and Historical data points for analysing market trends and human movements through time.

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