dr-manhattan

The codebase is type safe with full type hints throughout, helping developers catch errors early and enjoy superior IDE autocomplete. This enhances code reliability and developer productivity, reducing bugs and streamlining the development process for prediction market applications.

Order management is standardized, enabling developers to create, cancel, and track orders with consistent error handling across all supported exchanges. This uniformity simplifies trading operations and improves error resilience, making it easier to manage transactions programmatically.

Overall, dr-manhattan works by providing a common abstraction layer over multiple prediction market APIs, allowing developers to interact with various platforms using a single set of methods. Its unique approach mirrors the CCXT model for cryptocurrencies, applying it to prediction markets to unify a fragmented landscape. The methodology involves defining exchange-agnostic functions and data structures that map to platform-specific implementations, ensuring compatibility and ease of use.

Benefits for users include reduced development overhead, as they can write and maintain one codebase for multiple exchanges instead of several. Developers gain scalability, as the unified interface supports building applications that can easily adapt to new platforms. The open-source nature fosters community collaboration and transparency, while the type safety and strategy framework enhance code quality and trading efficiency.

Use cases include building trading bots that execute strategies across multiple prediction markets simultaneously, leveraging arbitrage opportunities or hedging positions. Developers can create analytics dashboards that aggregate data from different platforms, providing comprehensive insights into market trends and event probabilities. Market makers can use the API to manage liquidity and orders programmatically across exchanges, optimizing their operations. Researchers can automate data collection and analysis from various prediction markets for academic or commercial studies.

Target users are developers, traders, and researchers working with prediction markets, particularly those who need to interact with multiple platforms programmatically. Integrations include supported exchanges like Polymarket, Kalshi, Opinion, Limitless, and Predict.fun. The tech stack is Python-based, with installation via tools like uv, and the project is hosted on GitHub for open-source collaboration. Pricing and plan details are not explicitly stated, as it is an open-source tool.

In summary, dr-manhattan delivers a unified API that simplifies and standardizes interaction with prediction markets, empowering developers to build scalable applications with reduced complexity and enhanced efficiency across multiple platforms.

Updated 2026-02-28