A sophisticated trading template designed to scan prediction markets for pricing discrepancies and execute trades based on a user-defined fair value probability.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install prediction-fair-value-template
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 prediction-fair-value-template 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 Prediction Fair Value Template?
The Prediction Fair Value Template is a powerful framework for developers looking to automate their activity in prediction markets. By leveraging Openclaw Skills, this tool scans active Simmer-indexed markets to find pricing gaps between the current market price and a pre-defined thesis. It is designed to handle the complex plumbing of trade execution, including market discovery, position sizing, and risk-management checks, allowing you to focus on the core logic of your trading strategy.
This skill serves as an extensible foundation. While it ships with a simple keyword and probability model, it is built to be remixed. Developers can easily integrate advanced LLM outputs, real-time news signals, or proprietary research pipelines to refine their fair value estimates and automate their participation in global prediction venues like Polymarket.
Prediction Fair Value Template Use Cases
- Automatically scanning markets for specific financial or political events based on search queries.
- Identifying and acting on pricing edges where the market YES price significantly deviates from your internal model.
- Managing position sizes dynamically based on bankroll and the size of the perceived edge.
- Testing complex trading strategies in a dry-run environment before deploying live capital.
How Prediction Fair Value Template Works
- The skill begins by fetching the latest Simmer briefing and processing any available auto-redemptions.
- It performs a market search using the MARKET_QUERY parameter to identify relevant opportunities.
- For each discovered market, it calculates the difference between the current price and the FAIR_PROBABILITY.
- If the difference exceeds the MIN_EDGE, the skill calculates an optimal position size using the Simmer SDK.
- A final safety check is performed to evaluate market context, checking for excessive slippage or volatile price flips.
- The trade is executed on the chosen venue (Sim or Polymarket) with a logged reasoning for the action.
Prediction Fair Value Template Setup
To get started with this skill, ensure you have your Simmer API key ready. Use the following commands to configure your environment and run the skill:
# Set required credentials
export SIMMER_API_KEY="sk_live_your_key_here"
# Configure your trading thesis
export MARKET_QUERY="bitcoin"
export FAIR_PROBABILITY="0.65"
export MIN_EDGE="0.05"
# Run in dry-run mode (default)
python trade_skill.py
# Run in live mode
python trade_skill.py --live
Prediction Fair Value Template Data Schema & Taxonomy
The skill utilizes environment variables for configuration and logs metadata for every transaction to ensure transparency within the Openclaw Skills ecosystem.
| Attribute | Description | Default |
|---|---|---|
| MARKET_QUERY | The search term for market discovery | bitcoin |
| FAIR_PROBABILITY | Your estimated YES probability (0.0 to 1.0) | 0.60 |
| MIN_EDGE | Minimum gap required to trigger a trade | 0.05 |
| TRADING_VENUE | The target exchange (sim or polymarket) | sim |
| MAX_SLIPPAGE_PCT | Maximum allowed slippage before skipping | 0.15 |
Prediction Fair Value Template Advanced Features
- Multi-venue compatibility supporting both simulation environments and live Polymarket execution.
- Integrated position sizing logic that scales trades based on bankroll and statistical confidence.
- Public reasoning logs generated for every trade, providing an audit trail for automated decisions.
- Flexible safety model that defaults to dry-run to protect users during strategy development.
- Seamless integration with the ClawHub publishing workflow for multi-agent deployments.
SKILL.md
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