AKCodez

AKCodez / prediction-market-alpha-playbook

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Comprehensive reference for building trading bots on prediction markets (Polymarket, Kalshi, cross-venue). Edges, APIs, architecture patterns, antipatterns, and methodology — for AI agents and engineers.

12
1
100% credibility
Found Apr 26, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A detailed guide sharing proven strategies, system designs, pitfalls, and lessons for creating automated traders on prediction markets like Polymarket and Kalshi.

How It Works

1
🔍 Discover the Playbook

You find this handy guide while searching for ways to get ahead in prediction markets like Polymarket or Kalshi.

2
📖 Explore the Guide

You read through the organized sections on strategies, system ideas, helpful connections, and common traps to avoid.

3
🎯 Pick a Winning Edge

You select one simple, proven trading opportunity that matches your goals, feeling excited about its potential.

4
🛡️ Build Smart and Safe

You learn reliable patterns for your trading setup and steer clear of pitfalls that could lose money.

5
🧪 Test Without Risk

You practice your strategy in a pretend environment to confirm it works before using real money.

6
🚀 Launch Your Trader

You put your automated trader into action, watching it make smart moves across markets.

💰 Enjoy the Wins

Your trader spots opportunities and grows your returns steadily, thanks to the guide's wisdom.

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AI-Generated Review

What is prediction-market-alpha-playbook?

This repo is a comprehensive reference book for building trading bots on prediction markets like Polymarket, Kalshi, and cross-venue setups. It hands you battle-tested edges, API details with rate limits and gotchas, architecture patterns for scalable systems, antipatterns to dodge capital-burning mistakes, and a methodology for validating strategies. Think of it as a playbook that gets AI agents and engineers from zero to paper trading without the usual pitfalls—pure instructional gold, no code drop-ins.

Why is it gaining traction?

It stands out by distilling real production lessons into honest, categorized edges with evidence levels, unlike scattered forum posts or unproven GitHub bots. Developers hook on the quick orientation for agents, API minimal examples, and warnings like journal PnL fiction or side/token misalignment that save weeks of debugging. In a space full of hype, this delivers pragmatic alpha without fluff.

Who should use this?

AI engineers bootstrapping prediction-market bots need its architecture skeletons and API endpoints to avoid reinventing wheels. Quantitative researchers hunting validated edges will value the cohort search methods and Wilson bounds for honest stats. Bot operators scaling cross-venue trades should hit antipatterns first to sidestep thin-book capital locks and fee-bound arbs.

Verdict

Grab it as a free starting map if you're dipping into prediction markets—antipatterns and APIs alone justify the read. With just 12 stars and 1.0% credibility, it's immature but docs are sharp; fork and contribute to evolve it. (178 words)

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