PolyAgent
Autonomous Agent-Driven Prediction Market Trading Platform
01 / Project Overview
An autonomous trading application integrated with prediction markets. PolyAgent deploys multiple cooperative AI agents that scrape global news, calculate event probabilities, execute automated trading strategies on Polymarket, and hedge risks in real-time, yielding superior returns in sentiment-driven environments.
02 / The Challenge & Problem
Real-World Problem Statement
Prediction markets (like Polymarket) move at extreme speeds in response to real-time events. Human traders struggle to monitor hundreds of global news feeds, filter rumors from fact, calculate mathematical odds, and execute trades fast enough to capture mispriced contracts.
03 / The Engineering Solution
Implementation & Architectural Approach
Developed a swarm of specialized LangChain AI agents: a Scraper Agent (real-time news), an Analyst Agent (event probability updates), and a Trader Agent (web3 API contract execution), working in a continuous automated feedback loop.
04 / Technical Architecture Flow
News Scrapers & WebSockets
Aggregates real-time feeds from RSS, social media channels, and active prediction market orderbooks.
LangChain & CrewAI Swarm
Coordinates role-based reasoning (ReAct), sentiment classification, and probabilistic analysis.
Web3 API Interface
Executes contract swaps, handles gas routing, and interacts with Polymarket smart contracts.
05 / Key Project Features
Multi-Agent Consensus
Features role-based agents that debate event interpretations before executing capital allocation.
Instant Sentiment Engine
Converts text-based breaking news into probability shifts to catch mispriced market positions.
Decentralized Hedging
Balances exposure portfolios automatically by purchasing counter-positions during volatility spikes.
06 / Engineering Challenges & Mitigations
Large language models are vulnerable to accepting rumors or fake news as factual input.
Implemented a consensus voting protocol requiring corroboration from three independent verified sources.
Frequent transactions on decentralized networks resulted in excessive gas fee overhead.
Aggregated orders into off-chain batch queues and executed signatures conforming to EIP-712.
07 / Technical & Personal Learnings
Mastered autonomous multi-agent design patterns, tool-calling, and custom LangChain execution frameworks.
Acquired comprehensive experience in blockchain smart contract interaction and orderbook liquidity structures.