Arbitraider

Imagine you could spot price differences between prediction markets before anyone else. We use AI to analyze markets across platforms like Kalshi and Polymarket.

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Inspiration

In the growing world of prediction markets like Kalshi and Polymarket, we noticed a unique opportunity: markets that should be correlated often trade at different prices. This market inefficiency creates opportunities for savvy traders who can spot these relationships, but identifying them manually is nearly impossible. We wanted to automate this process using AI.

What it does

Arbitrage Alpha analyzes prediction markets across different platforms, using AI to identify correlated events and highlight potential arbitrage opportunities. Our platform provides real-time market data visualization, similarity analysis between markets, and a user-friendly interface for traders to discover and act on these opportunities.

How we built it

We built a full-stack application using Next.js for the frontend and Flask for the backend. The frontend leverages shadcn's component library for a polished UI, while our backend integrates with Kalshi's API for market data. We implemented AWS services, including S3 for data storage and SageMaker for our machine learning models. The platform uses natural language processing to analyze market similarities and identify arbitrage opportunities.

Challenges we ran into

  • Learning and implementing the Kalshi API while handling rate limits and data synchronization
  • Building a responsive and efficient UI with shadcn components in Next.js
  • Setting up AWS infrastructure, particularly configuring SageMaker for our ML models
  • Managing real-time data updates without overwhelming the API or the frontend
  • Ensuring accurate market correlation detection while minimizing false positives

Accomplishments that we're proud of

  • Successfully integrated multiple complex technologies (Next.js, Flask, AWS) into a cohesive platform
  • Built a scalable system that can handle real-time market data analysis
  • Created an intuitive interface for visualizing complex market relationships
  • Implemented efficient data processing pipelines using AWS services
  • Developed a working prototype that can identify genuine arbitrage opportunities

What we learned

  • Deep understanding of prediction market mechanics and arbitrage opportunities
  • Practical experience with AWS services, particularly S3 and SageMaker
  • Advanced Next.js development techniques and state management
  • Real-world API integration and rate limit handling
  • UI/UX design principles using shadcn components
  • The complexities of market correlation analysis and arbitrage detection

What's next for Arbitraider

  • Enhance our machine learning models for more accurate market correlation detection
  • Expand to additional prediction market platforms
  • Implement automated trading capabilities
  • Add real-time alerts for new arbitrage opportunities
  • Develop more sophisticated market analysis tools
  • Build a community feature for traders to share insights
  • Integrate additional data sources for better market analysis

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