Stock Sentiment Analytics

A real-time platform that tracks, classifies, and visualizes market sentiment around stocks using data from social media, news, and market feeds.
By combining real-time data collection, FinBERT-based sentiment analysis, and interactive dashboards, it reveals how public sentiment correlates with stock price movements and volatility.

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Features

• Real-Time Data Streams

  • Scrapes and aggregates stock mentions from Reddit (r/wallstreetbets, r/investing) and financial news APIs.
  • Integrates intraday stock price data (via Yahoo Finance or Alpaca).

• Sentiment Classification

  • Uses FinBERT (a transformer model fine-tuned for financial text) to classify posts and headlines as positive, negative, or neutral.
  • Aggregates scores by ticker and time window for trend tracking.

• Interactive Dashboard

  • Visualizes sentiment trends alongside stock price and volume data.
  • Displays trending tickers, sentiment heatmaps, and alerts for significant shifts in market mood.

• Extensible Architecture

  • Modular codebase for adding new data sources, backtesting sentiment strategies, or deploying trading signals.

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Tech Stack

• Python (Pandas, NumPy, asyncio for streaming)
• APIs: PRAW (Reddit), NewsAPI, yfinance/Alpaca (stock data)
• NLP: Hugging Face Transformers (FinBERT)
• Database: SQLite or PostgreSQL for storing historical data
• Visualization: Streamlit or Plotly Dash
• Deployment: Streamlit Cloud / Docker

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Architecture Overview

flowchart TD
    A[Reddit / News APIs] -->|Text Data| B[Data Ingestion - Python/asyncio]
    C[Stock Price Feeds] -->|Price Data| B
    B --> D[Sentiment Classification - FinBERT]
    D --> E[Aggregation & Storage - SQLite/Postgres]
    E --> F[Interactive Dashboard - Streamlit]

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Project Structure

market-sentiment-analyzer/
├── dashboard.py              # Streamlit dashboard frontend
├── stream_sentiment.py       # Backend data collection and sentiment analysis
├── utils.py                  # Utility functions and helpers
├── config.py                 # Configuration settings
├── run.py                    # Startup script for easy execution
├── requirements.txt          # Python dependencies
├── env_template.txt          # Environment variables template
├── README.md                # This file
└── .gitignore               # Git ignore file

Getting Started

Follow these steps to set up and run the project locally.

1. Clone the Repository

git clone https://github.com/yourusername/stock-sentiment-analytics.git
cd stock-sentiment-analytics

2. Install Dependencies

pip install -r requirements.txt

3. Configure API Keys

Create a .env file in the project root and add your credentials:

NEWS_API_KEY=your_newsapi_key

4. Quick Start

Use the provided startup script to run the application:

# Run dashboard only (with sample data)
python run.py dashboard

# Run data collection stream only
python run.py stream

# Run both dashboard and data stream
python run.py both

5. Manual Start (Alternative)

If you prefer to run components manually:

Start Data Collection:

python stream_sentiment.py

Launch Dashboard:

streamlit run dashboard.py

The dashboard will be available at http://localhost:8501.