惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Stack Overflow Blog
Stack Overflow Blog
量子位
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
美团技术团队
小众软件
小众软件
aimingoo的专栏
aimingoo的专栏
Recent Announcements
Recent Announcements
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Microsoft Security Blog
Microsoft Security Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
酷 壳 – CoolShell
酷 壳 – CoolShell
J
Java Code Geeks
V
V2EX
大猫的无限游戏
大猫的无限游戏
D
DataBreaches.Net
博客园 - Franky
爱范儿
爱范儿
T
Tailwind CSS Blog
A
About on SuperTechFans
Google DeepMind News
Google DeepMind News
博客园_首页
B
Blog RSS Feed
博客园 - 司徒正美
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
The exact math that made $40,000,000 out of Polymarket (F...
NinE X · 2026-06-16 · via DEV Community

While you're manually checking if YES + NO = 1, quantitative systems are solving massive constraint satisfaction problems across thousands of correlated markets in milliseconds.

The Hidden Reality of Prediction Market Arbitrage

You see a market where YES is trading at $0.62 and NO at $0.33. You think: There's $0.05 of arbitrage here. You're right.

What you don't see is that by the time you place both orders, professional systems have already:

  • Scanned 17,000+ conditions
  • Detected dozens of correlated mispricings
  • Calculated optimal position sizes (with fees & slippage)
  • Executed everything in parallel
  • Moved on to the next opportunity

Between April 2024 and April 2025, quantitative traders extracted $39,688,585 in guaranteed arbitrage profits from Polymarket.

The top individual wallet made $2,009,631.76 across 4,049 trades — an average of $496 guaranteed profit per trade.

This wasn't gambling. This was mathematics.

Why Simple "YES + NO = 1" Checks Fail

Most retail traders stop at basic price sum checks. That's not enough.

Markets are logically dependent. Example:

  • "Will Trump win Pennsylvania?" → YES: $0.48
  • "Will Republicans win Pennsylvania by 5+ points?" → YES: $0.32

If the second outcome happens, the first must be true. These dependencies create arbitrage opportunities that simple addition cannot detect.

This is known as the marginal polytope problem — projecting prices onto the set of arbitrage-free probability distributions.

The Scale of the Computational Challenge

For any event with n binary conditions, there are 2ⁿ possible outcome combinations.

  • 2024 U.S. elections: 305 markets → tens of thousands of pairs
  • 2010 NCAA tournament: 63 games → 2⁶³ ≈ 9.2 quintillion combinations

Brute force is impossible. Smart systems use constraints instead.

Real example: Duke vs Cornell basketball market

7 possible win counts per team → 14 conditions.

Instead of checking 16,384 combinations, 3 linear constraints were enough.

Research found that 41% of 17,218 conditions showed single-market arbitrage, with median mispricing of $0.60 (~40% error).

The Core Math: Bregman Projection + Frank-Wolfe

To find the optimal trade, you project the current market prices onto the nearest arbitrage-free probability distribution using Bregman divergence (logarithmic distance that respects probability structure).

Direct projection is intractable, so the Frank-Wolfe algorithm is used:

  • Start with a small active set of valid outcomes
  • Iteratively solve linear programs
  • Add one new vertex per iteration
  • Converges in 50–150 iterations instead of exploring 2ⁿ space

As more outcomes are resolved (e.g., games completed), the feasible set shrinks dramatically and solves become faster — from 10–30 seconds early in an event to under 5 seconds near the end.

The Full Production System Architecture

A real arbitrage system includes:

1. Real-time Data Pipeline

  • WebSocket feed from Polymarket CLOB
  • Alchemy node for Polygon OrderFilled events
  • Sub-5ms latency

2. Dependency Detection

  • Fine-tuned LLM (DeepSeek-R1-Distill-Qwen-32B) classifying market relationships with 81%+ accuracy on complex electoral markets

3. 3-Layer Optimization Engine

  • Layer 1: Linear programming relaxations (milliseconds)
  • Layer 2: Frank-Wolfe + Gurobi integer programming (1–30s)
  • Layer 3: Live order book validation before execution

4. Risk & Position Sizing

  • Modified Kelly criterion accounting for execution risk
  • Never exceed 50% of available book depth

Where Most Strategies Die: Execution

Even perfect math fails if you can't execute.

Polymarket is a Central Limit Order Book. Your beautiful two-leg arbitrage can easily become a one-leg disaster due to slippage.

The real edge is in the 30-second detection-to-submission window before everyone else reacts.

Copy-trading visible wallets usually means you're buying the exit liquidity at worse prices.

The Winners (Public On-Chain)

Here are 15 verified wallets that extracted massive profits using systematic approaches:

  • kch123$12M (latency arb)
  • RN1$7.4M (market making)
  • Swisstony$5.9M (oracle arb)
  • DrPufferfish$3.4M (combinatorial)
  • ...and 11 more (total > $51 million)

Full breakdown and names in the original research.

Key Papers

Getting Started on Polymarket Today

  1. Connect MetaMask/Coinbase Wallet (Polygon network)
  2. Deposit USDC
  3. Start with small positions ($10–50) to qualify for rewards
  4. Monitor the Rewards tab — active campaigns pay out based on volume

Final Note

The $40 million wasn't extracted by luck or better predictions.

It was extracted by people who treated prediction markets as a mathematical optimization problem instead of a betting platform.

The algorithms are public. The infrastructure is buildable.

The only question left is execution.