codexbt/poly-claw-bot — explained in plain English
Analysis updated 2026-05-18
Run an automated momentum trading strategy on Polymarket.
Scan sports markets for trading opportunities without manual monitoring.
Validate trade signals with an LLM confidence score before executing.
Test strategies in paper trading mode before risking real funds.
| codexbt/poly-claw-bot | 2arons/llm-cli | adzza/guardium-dns | |
|---|---|---|---|
| Stars | 11 | 11 | 11 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | easy | moderate |
| Complexity | 3/5 | 2/5 | 3/5 |
| Audience | developer | developer | general |
Figures from each repo's GitHub metadata at analysis time.
Requires API keys and a private key for live trading, stored in a .env file, paper mode needs no real funds.
Poly Claw Bot is an AI-powered automated trading suite written in Python, designed for use on prediction markets and sports betting markets. The description mentions Polymarket specifically, which is a platform where users trade on the outcome of real-world events. Instead of placing trades manually, this bot monitors markets, applies predefined strategies, and executes trades on your behalf. The suite includes several distinct bots with different approaches. One runs a momentum strategy on Polymarket, scanning for price moves and volume triggers every five minutes. Another focuses on sports markets, scanning for trading opportunities in sports outcomes. A third handles arbitrage, looking for price differences to exploit across positions. Each bot runs as a separate Python script, and they can run in parallel so multiple strategies operate simultaneously. What makes it "AI-powered" is the integration with an LLM (large language model) through an external API. The LLM is used to score the confidence of trade signals before the bot acts on them, adding a validation layer on top of the rule-based strategy logic. Configuration is handled entirely through environment variables stored in a .env file, keeping credentials like private keys and API keys out of the code. The README emphasizes a paper trading mode, which lets you simulate trades without real money, recommended for testing before going live. You would use this if you wanted to automate trading on Polymarket or sports markets, particularly combining momentum strategies with AI-assisted signal validation. It is built with Python 3.10 or above and licensed under MIT.
An automated Python trading suite for Polymarket and sports markets, using an LLM to score trade confidence before executing.
Mainly Python. The stack also includes Python, OpenRouter, dotenv.
Use freely for any purpose, including commercial use, as long as you keep the copyright notice.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.