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polymarket-unified

Analyzes Polymarket data using HARA, Loopy BP, Shapley, and fictitious play.

Updated Apr 17, 2026
Search Tags:#calibration#prediction-markets#polymarket#shapley#liquidity-analysis#loopy-belief-propagation#fictitious-play
One-click install
npx skills add https://github.com/erongcao/polymarket-unified --skill polymarket-unified
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: polymarket-unified
Source: https://github.com/erongcao/polymarket-unified/tree/main
Command: npx skills add https://github.com/erongcao/polymarket-unified --skill polymarket-unified

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, tqdm, and includes scripts (resource) components.

What problem does it solve?

Polymarket Unified provides a unified, academically grounded toolkit for rigorous analysis of prediction markets by integrating core theoretical frameworks (HARA LMSR, Loopy Belief Propagation, Shapley signal aggregation, and equilibrium learning) into a single, scalable platform.

Core Features & Use Cases

  • Comprehensive Frameworks: Implementations of HARA market making, Loopy BP for combinatorial markets, Monte Carlo Shapley, and fictitious play learning to analyze market dynamics.
  • End-to-End Analysis: Ability to calibrate market efficiency, quantify trader contributions, assess performative bias, and generate reproducible reports for research or risk management.
  • Use Case: Researchers can compare liquidity across gamma values, identify key traders, and study convergence of learning dynamics on multi-outcome markets.

Quick Start

Load an event dataset into the Polymarket Unified analysis tool and run the full pipeline to generate a structured report.

Frequently Asked Questions about polymarket-unified

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze prediction market liquidity and efficiency on Polymarket?▼

You can quantify trader contributions in prediction markets by applying Monte Carlo Shapley signal aggregation, which identifies key traders and evaluates their specific impact on multi-outcome market dynamics.

Can I use fictitious play to study equilibrium learning in combinatorial prediction markets?▼

Liquidity analysis in prediction markets uses HARA market making to compare liquidity across varying gamma values, diagnosing market efficiency and assessing performative bias through numerically stable parameter validation.

How do I run a full equilibrium learning pipeline for multi-outcome prediction markets?▼

You can detect performative bias in prediction markets by applying equilibrium learning and fictitious play checks, which diagnose market efficiency and ensure numerical stability safeguards during analysis.

Do I need numpy and scipy to run Loopy Belief Propagation on combinatorial markets?▼

This toolkit diagnoses prediction market efficiency and liquidity by integrating HARA LMSR, Loopy Belief Propagation, and Shapley signal aggregation into a unified platform for rigorous academic research and risk management.

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