polymarket-optimizer

What This Skill Does

The polymarket-optimizer is a specialized automated engine designed to maintain and improve the performance of the polymarket-executor. Operating on a 6-hour cron cycle within the OpenClaw ecosystem, it systematically ingests performance metrics, simulated trade records, and portfolio status. By analyzing win rates, total trade volume, and P&L data, the agent dynamically modifies the learned_config.json file. It optimizes critical trading parameters such as strategy thresholds, capital allocation weightings, and Kelly fractions. Furthermore, it acts as a gatekeeper for live trading readiness, validating performance against a strict four-factor criteria set before allowing real-money exposure.

Installation

To install the optimizer, use the OpenClaw CLI within your terminal. Ensure your workspace is correctly configured to allow the skill to read and write to the necessary executor data directories: clawhub install openclaw/skills/skills/georges91560/polymarket-optimizer Once installed, you can trigger a manual optimization run or verify the cron schedule by navigating to the skill directory at /data/.openclaw/workspace/skills/polymarket-optimizer and executing python3 polymarket_optimizer.py.

Use Cases

This skill is ideal for quantitative traders who are currently paper trading on Polymarket and want to automate the feedback loop between results and configuration. It is perfect for users who want to move from manual parameter tuning to a data-driven, adaptive model that self-corrects based on market volatility and strategy performance. It is specifically designed to minimize the risk of moving to live trading by providing a clear 'readiness' score.

Example Prompts

1. "OpenClaw, run a manual optimization scan for the polymarket-executor and report the results to my Telegram."
2. "Show me the last 5 adjustments made by the polymarket-optimizer and explain why the parity_arbitrage allocation was changed."
3. "Is the portfolio currently meeting the readiness criteria for live trading? If not, what specific metrics are failing?"

Tips & Limitations

• Safety First: Always review the optimizer_log.jsonl file after an automated adjustment to ensure the logic aligns with your risk tolerance.
• Dependencies: The optimizer relies entirely on valid JSON inputs from the executor; ensure your executor is correctly configured to write these logs.
• Limitations: This tool is an optimizer, not a strategy designer. It requires a foundational set of strategies within the executor to function. It will not create new alpha, but will refine the execution parameters of existing code.