Multi-Sport Betting System

A production-grade, automated sports betting system powered by Airflow, PostgreSQL, and Kelly Criterion portfolio management.

🚀 Getting Started

1. Installation

# Clone the repository
git clone https://github.com/your-org/nhlstats.git
cd nhlstats

# Install dependencies
pip install -r requirements.txt

2. Run the Dashboard

Visualize model performance and lift/gain analysis.

./run_dashboard.sh

See Dashboard Guide for details.

3. Run the Automation

The system uses Airflow for daily data ingestion and betting.

docker compose up -d

See System Overview for architecture.

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📚 Documentation

Core Guides

• Betting Strategy: The math behind the money (Elo, Kelly, Sharp Confirmation).
• Dashboard Guide: How to use the Streamlit interface.
• System Overview: Architecture, DAGs, and Data Flow.
• Troubleshooting: Fix common issues.

Integration Guides

• Kalshi Betting
• BetMGM Integration
• Portfolio Management

Developer Skills & SOPs

Located in .github/skills/

• Adding a New Sport
• Airflow DAG Patterns
• Database Operations

Data Flow & Integration (NEW)

• DAG Task Data Flow: Complete task documentation with data sources/destinations
• Data Flow Quick Reference: Quick guide for monitoring and troubleshooting
• Integration Fixes 2026-01-29: Summary of critical data consistency fixes
• Database Schema: Updated with current data flow information
• Testing Patterns

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🏗️ Repository Structure

├── dags/                  # Airflow workflows (DAGs)
├── plugins/               # Core logic (Elo, Betting, API)
├── scripts/               # Utility scripts (Backfills, Analysis)
├── tests/                 # Unit and Integration tests
├── docs/                  # Detailed documentation
├── reports/               # Historical analysis and logs
├── data/                  # Local data storage
└── dashboard_app.py       # Streamlit entry point

🧪 Testing

Run the full test suite (including documentation integrity):

pytest

See Testing Patterns for more.