brodyautomates

brodyautomates / polymarket-pipeline

Public

Event-driven AI pipeline that monitors breaking news in real time, classifies market impact with Claude, and trades niche Polymarket markets automatically.

ai-trading algorithmic-trading automation claude polymarket
16
2
89% credibility
Found Apr 04, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

An automated tool that monitors real-time news streams, matches headlines to low-volume prediction markets, uses AI to classify bullish or bearish impact, detects trading edges, and executes bets with safety limits.

How It Works

1
🔍 Discover the News Trader

You find this handy tool on GitHub that watches breaking news and spots smart bets on prediction markets.

2
📥 Quick Download and Setup

Run one easy command to bring the whole system to your computer in minutes.

3
🔗 Link News Feeds and AI Brain

Connect live news from social channels and hook up a smart AI helper to make sense of headlines.

4
✅ Check It's All Ready

Hit verify to ensure your connections work perfectly and everything is good to go.

5
🚀 Launch Real-Time Watching

Start the watcher and feel the excitement as it scans news streams for opportunities instantly.

6
📊 Monitor Live Dashboard

Watch colorful updates showing news matches, smart analysis, and trade signals in action.

💰 Winning Trades Unlocked

Your system catches news edges on quiet markets, places safe bets, and tracks profits over time.

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Star Growth

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AI-Generated Review

What is polymarket-pipeline?

This Python project builds an event-driven pipeline that automatically monitors breaking news from Twitter, Telegram, and RSS feeds, classifies its impact on Polymarket prediction markets using Claude AI, and executes trades on niche markets under $500K volume. It solves the problem of slow, crowded trading bots by focusing on real-time streams and low-competition opportunities, delivering edge detection, quarter-Kelly sizing, and SQLite logging out of the box. Users get a CLI for commands like `watch` for live event-driven runs, `backtest` for strategy validation, and `dashboard` for monitoring—all with dry-run safety by default.

Why is it gaining traction?

Unlike polling-based scrapers or high-volume market bots, this stands out with its Python event-driven architecture on GitHub, hitting sub-5-second news-to-trade latency via async streams and WebSocket price feeds. Developers dig the shift to Claude's bullish/bearish classification over shaky probability estimates, plus built-in calibration tracking and niche filtering to dodge sophisticated competition. The one-command setup and rich terminal dashboard make prototyping an event-driven data pipeline feel effortless.

Who should use this?

Quant devs or crypto traders experimenting with AI-driven prediction market bots on Polymarket. Python enthusiasts building event-driven pipelines for automated trading signals. Hobbyists scanning niche events like AI breakthroughs or Fed announcements without manual monitoring.

Verdict

Solid for backtesting and dry-runs (0.90% credibility score reflects its 16 stars and early stage), with excellent docs and CLI making it dead simple to spin up. Test thoroughly on resolved markets before going live—promising edge in underserved niches, but treat as educational until more resolution data proves calibration.

(198 words)

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