An automated trading skill that identifies and executes trades on Polymarket by comparing user-defined probability theses against real-time market prices.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install polymarket-thesis-trader
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install polymarket-thesis-trader using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Polymarket Thesis Trader?
The Polymarket Thesis Trader is a specialized tool designed for the Openclaw Skills ecosystem that bridges the gap between manual research and automated execution. It functions as a configurable template, allowing users to input a specific market query and a personal fair-value probability. By utilizing the AION SDK, the skill scans relevant markets, calculates the mathematical edge relative to current order book prices, and triggers trades only when specific safety and edge thresholds are met.
This skill is particularly valuable for traders who want to maintain a disciplined approach to prediction markets. It handles the heavy lifting of market discovery, context verification, and transaction tagging, ensuring that every trade is backed by a quantitative thesis. Whether you are tracking economic indicators, political events, or crypto trends, this skill provides a robust framework for automated market engagement.
Polymarket Thesis Trader Use Cases
- Scanning for undervalued positions in Bitcoin, Federal Reserve, or election-related markets.
- Automating trades based on proprietary forecasting models or external news feeds.
- Managing high-frequency thematic trading strategies across multiple market queries.
- Performing safe dry-runs to validate trading hypotheses before committing real capital.
How Polymarket Thesis Trader Works
- The skill utilizes AION market discovery to find active markets matching a specific search query.
- It fetches the current YES and NO prices for the candidate markets identified.
- A mathematical edge is calculated: (fair_value - yes_price) for YES trades or ((1 - fair_value) - no_price) for NO trades.
- The system performs a market context check to detect warnings or potential flip-flop risks.
- If the calculated edge exceeds the user-defined MIN_EDGE, the skill prepares the trade execution.
- Trades are executed (if live mode is enabled) and tagged with the skill_slug for easy performance tracking.
Polymarket Thesis Trader Setup
To get started with this skill from the Openclaw Skills repository, follow these steps:
# Install the skill via clawhub
npx clawhub@latest install polymarket-thesis-trader
# Configure your credentials
export AION_API_KEY=your_api_key
# Run a dry-run to test your thesis
python trade_thesis.py --query "bitcoin" --thesis-probability 0.65
# Execute live trades when ready
python trade_thesis.py --query "bitcoin" --thesis-probability 0.65 --live --trade-amount 10
Polymarket Thesis Trader Data Schema & Taxonomy
The skill organizes its operations and tracking through a structured set of parameters and metadata tags:
| Parameter | Description | Default |
|---|---|---|
| MARKET_QUERY | The keyword used to find relevant Polymarket listings | N/A |
| THESIS_PROBABILITY | Your estimated fair probability (0.0 - 1.0) | N/A |
| MIN_EDGE | The minimum gap required between thesis and market price | 0.05 |
| TRADE_AMOUNT_USD | The dollar value for each position taken | 5 |
| RUN_LIVE | Boolean flag to enable actual transaction execution | false |
All executed trades are automatically tagged with source: aion and the skill_slug to maintain a clear audit trail.
Polymarket Thesis Trader Advanced Features
- Multi-market scanning to find the best entry point for a single thesis across different market variants.
- Dry-run safety mechanisms to prevent execution errors during strategy testing.
- Context-aware filtering that skips markets showing high volatility or conflicting signals.
- Extensible architecture allowing developers to replace static thesis inputs with dynamic research pipelines.
- Seamless integration with the broader suite of Openclaw Skills for complex multi-agent automation workflows.
SKILL.md
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