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Automatic parameter optimizer for polymarket-executor. Reads performance_metrics.json every 6 hours, analyzes win rates and P&L per strategy, adjusts learned_config.json to improve future performance. Also builds paper trade metrics and assesses live trading readiness. Part of the Wesley Agent Ecosystem — mirrors crypto-executor-optimizer pattern.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install georges91560/polymarket-optimizerInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
Reads trading performance from polymarket-executor, analyzes what works, and adjusts parameters automatically.
Runs every 6 hours via OpenClaw cron job. Zero manual intervention required.
Input files (written by executor):
performance_metrics.json — per-strategy win rates, P&L, trade countspaper_trades.json — all simulated trade recordsportfolio.json — capital, positions, daily P&LOutput files (read by executor at next startup):
learned_config.json — updated thresholds, Kelly fraction, allocationsoptimizer_log.jsonl — full history of every optimization runcd /data/.openclaw/workspace/skills/polymarket-optimizer
python3 polymarket_optimizer.py
Expected output:
============================================================
POLYMARKET OPTIMIZER v1.0.0
Run time: 2026-03-05 14:00:00 UTC
============================================================
[CONFIG] Loaded. Optimization #1
[PORTFOLIO] Health: HEALTHY
[PORTFOLIO] Capital: $102.34 | Return: 2.3%
[ANALYSIS] parity_arbitrage: EXCELLENT | WR=100.0% | Trades=8 | P&L=+0.523
[ANALYSIS] tail_end: GOOD | WR=75.0% | Trades=12 | P&L=+0.312
[OPTIMIZER] 2 adjustments made:
🚀 parity_arb excellent → min_profit 0.020 → 0.015
📈 parity_arb allocation 30% → 40%
[READINESS] Resolved trades: 8/30
[READINESS] Win rate: 84.6% (need 55%+)
[TELEGRAM] Report sent.
============================================================
OPTIMIZER COMPLETE
============================================================
| If strategy is... | Action |
|---|---|
| Poor (WR < 50%) | Raise threshold → trade only best opportunities |
| Underperforming (WR 50–65%) | Slight threshold increase |
| Average (WR 65–80%) | No change |
| Good (WR 65–80%, positive P&L) | No change |
| Excellent (WR > 80%) | Lower threshold → capture more volume |
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
Automatic parameter tuner for Polymarket Executor
Part of the Wesley Agent Ecosystem — mirrors crypto-executor-optimizer pattern.
Runs every 6 hours. Reads performance data from polymarket-executor, figures out what's working, and adjusts trading parameters automatically.
No manual configuration needed after setup.
polymarket-executor trades
↓
Writes performance_metrics.json + paper_trades.json + portfolio.json
↓
polymarket-optimizer runs (every 6h)
↓
Analyzes win rates, P&L, drawdowns per strategy
↓
Adjusts learned_config.json:
- Thresholds (tighter if poor, looser if excellent)
- Capital allocation (more to winners)
- Kelly fraction (conservative if drawdown)
- Scan frequency (faster if performing well)
↓
polymarket-executor reads new config at next startup
polymarket-optimizer/
├── SKILL.md ← OpenClaw integration
├── README.md ← This file
├── CONFIGURATION.md ← Setup guide
├── SYSTEMD_SETUP.md ← Run as system service
└── polymarket_optimizer.py ← Main optimizer script
Runtime files (auto-created in WORKSPACE):
learned_config.json ← Output (read by executor)
optimizer_log.jsonl ← Full optimization history
python3 polymarket_optimizer.py
Runs once and exits. Schedule via cron or systemd for automatic operation.
polymarket-executor must have run first (to create input files)polymarket-executor ← Trades, writes metrics
polymarket-optimizer ← This skill (reads metrics, updates config)
License: MIT | Author: Georges Andronescu (Wesley Armando) GitHub: https://github.com/georges91560/polymarket-optimizer
# Polymarket Optimizer — Adaptive Parameter Tuner ## 🧠 WHAT IT DOES Reads trading performance from `polymarket-executor`, analyzes what works, and adjusts parameters automatically. Runs every **6 hours** via OpenClaw cron job. Zero manual intervention required. **Input files (written by executor):** - `performance_metrics.json` — per-strategy win rates, P&L, trade counts - `paper_trades.json` — all simulated trade records - `portfolio.json` — capital, positions, daily P&L **Output files (read by executor at next startup):** - `learned_config.json` — updated thresholds, Kelly fraction, allocations - `optimizer_log.jsonl` — full history of every optimization run --- ## ⚡ QUICK START ```bash cd /data/.openclaw/workspace/skills/polymarket-optimizer python3 polymarket_optimizer.py ``` Expected output: ``` ============================================================ POLYMARKET OPTIMIZER v1.0.0 Run time: 2026-03-05 14:00:00 UTC ============================================================ [CONFIG] Loaded. Optimization #1 [PORTFOLIO] Health: HEALTHY [PORTFOLIO] Capital: $102.34 | Return: 2.3% [ANALYSIS] parity_arbitrage: EXCELLENT | WR=100.0% | Trades=8 | P&L=+0.523 [ANALYSIS] tail_end: GOOD | WR=75.0% | Trades=12 | P&L=+0.312 [OPTIMIZER] 2 adjustments made: 🚀 parity_arb excellent → min_profit 0.020 → 0.015 📈 parity_arb allocation 30% → 40% [READINESS] Resolved trades: 8/30 [READINESS] Win rate: 84.6% (need 55%+) [TELEGRAM] Report sent. ============================================================ OPTIMIZER COMPLETE ============================================================ ``` --- ## 🔧 WHAT IT ADJUSTS ### Strategy Thresholds | If strategy is... | Action | |---|---| | Poor (WR < 50%) | Raise threshold → trade only best opportunities | | Underperforming (WR 50–65%) | Slight threshold increase | | Average (WR 65–80%) | No change | | Good (WR 65–80%, positive P&L) | No change | | Excellent (WR > 80%) | Lower threshold → capture more volume | ### Capital...
# Polymarket Optimizer 🧠
**Automatic parameter tuner for Polymarket Executor**
Part of the **Wesley Agent Ecosystem** — mirrors `crypto-executor-optimizer` pattern.
---
## What It Does
Runs every 6 hours. Reads performance data from `polymarket-executor`, figures out what's working, and adjusts trading parameters automatically.
No manual configuration needed after setup.
---
## How It Works
```
polymarket-executor trades
↓
Writes performance_metrics.json + paper_trades.json + portfolio.json
↓
polymarket-optimizer runs (every 6h)
↓
Analyzes win rates, P&L, drawdowns per strategy
↓
Adjusts learned_config.json:
- Thresholds (tighter if poor, looser if excellent)
- Capital allocation (more to winners)
- Kelly fraction (conservative if drawdown)
- Scan frequency (faster if performing well)
↓
polymarket-executor reads new config at next startup
```
---
## Files
```
polymarket-optimizer/
├── SKILL.md ← OpenClaw integration
├── README.md ← This file
├── CONFIGURATION.md ← Setup guide
├── SYSTEMD_SETUP.md ← Run as system service
└── polymarket_optimizer.py ← Main optimizer script
```
**Runtime files (auto-created in WORKSPACE):**
```
learned_config.json ← Output (read by executor)
optimizer_log.jsonl ← Full optimization history
```
---
## Quick Start
```bash
python3 polymarket_optimizer.py
```
Runs once and exits. Schedule via cron or systemd for automatic operation.
---
## Requirements
- Python 3.7+ (standard library only)
- `polymarket-executor` must have run first (to create input files)
- Telegram bot (optional — for reports)
---
## Ecosystem
```
polymarket-executor ← Trades, writes metrics
polymarket-optimizer ← This skill (reads metrics, updates config)
```
---
**License:** MIT | **Author:** Georges Andronescu (Wesley Armando)
**GitHub:** https://github.com/georges91560/polymarket-optimizer基于 Claude Code 的桌面 AI 工作力平台 — 支持飞书/QQ 远程调度、技能创建、定时任务。OpenClaw 的桌面实现,零代码养好你的 AI 🦞 Desktop AI workforce platform built on Claude Code. Feishu/QQ bot integration, skill c...
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Bots learn, human earns, curated open claw playbook list and skill list for life long learners at https://botlearn.ai