Add 6 Polymarket trading skills with paper trading engine
Composable Agent Skills (SKILL.md format) for Polymarket prediction market trading. Includes scanner, analyzer, monitor, paper trader, strategy advisor, and live executor. All tested against live Polymarket APIs. Security audited with all HIGH/MEDIUM findings resolved. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Polymarket Skills Builder
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Claude Opus 4.6
Claude Opus 4.6
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# Polymarket Fee Model
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## Overview
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Most Polymarket markets are **fee-free**. Dynamic taker fees apply only to
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short-duration crypto markets (5-minute and 15-minute expiry).
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## Fee-Free Markets
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The vast majority of markets on Polymarket -- political, sports, entertainment,
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weather, and long-duration crypto markets -- charge **zero fees** for both makers
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and takers. This makes arbitrage significantly more viable than on traditional
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exchanges.
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## Dynamic Taker Fees (Crypto Short-Duration Only)
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For 5-minute and 15-minute crypto prediction markets, a dynamic taker fee applies:
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```
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feeQuote = baseRate * min(price, 1 - price) * size
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```
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Where:
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- `baseRate` is set per market (typically 0.063 or 6.3%)
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- `price` is the execution price (0 to 1)
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- `size` is the number of shares
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### Effective Fee Rate by Price
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| Price | min(p, 1-p) | Effective Rate (baseRate=0.063) |
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|-------|-------------|-------------------------------|
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| 0.05 | 0.05 | 0.315% (0.063 * 0.05) |
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| 0.10 | 0.10 | 0.630% |
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| 0.20 | 0.20 | 1.260% |
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| 0.30 | 0.30 | 1.890% |
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| 0.40 | 0.40 | 2.520% |
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| 0.50 | 0.50 | 3.150% (maximum) |
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| 0.60 | 0.40 | 2.520% |
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| 0.70 | 0.30 | 1.890% |
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| 0.80 | 0.20 | 1.260% |
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| 0.90 | 0.10 | 0.630% |
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| 0.95 | 0.05 | 0.315% |
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The fee is **parabolic**, peaking at p=0.50 and dropping sharply near the extremes.
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This was explicitly designed to kill latency arbitrage on these fast markets.
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### Fee Calculator
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```python
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def calculate_fee(price: float, size: float, base_rate: float = 0.063) -> dict:
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"""Calculate dynamic taker fee for crypto short-duration markets."""
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fee_rate = base_rate * min(price, 1 - price)
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fee_amount = fee_rate * size
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cost_basis = price * size
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total_cost = cost_basis + fee_amount
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effective_rate = fee_amount / cost_basis if cost_basis > 0 else 0
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return {
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"fee_rate": fee_rate,
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"fee_amount": fee_amount,
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"cost_basis": cost_basis,
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"total_cost": total_cost,
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"effective_rate_pct": effective_rate * 100,
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}
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```
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### Breakeven Analysis for Arbitrage
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For an arbitrage trade buying both YES and NO:
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```python
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def arbitrage_breakeven(yes_price, no_price, base_rate=0.063):
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"""Calculate if arb is profitable after fees on fee-bearing markets."""
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raw_sum = yes_price + no_price
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raw_edge = 1.0 - raw_sum # Positive = underpriced
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yes_fee = base_rate * min(yes_price, 1 - yes_price)
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no_fee = base_rate * min(no_price, 1 - no_price)
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total_fee_rate = yes_fee + no_fee
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net_profit_per_share = raw_edge - total_fee_rate
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return {
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"raw_edge": raw_edge,
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"total_fee_rate": total_fee_rate,
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"net_profit_per_share": net_profit_per_share,
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"profitable": net_profit_per_share > 0,
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}
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```
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## Maker Rebates
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Post-only limit orders (introduced January 2026) receive maker rebates on
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qualifying markets. This creates a structural advantage for market-making
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strategies that provide liquidity.
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## Practical Implications
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1. **Fee-free markets**: Arbitrage edges as small as $0.01 are worth capturing
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2. **Fee-bearing markets**: Need at least 3-6% raw edge at mid-prices to break even
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3. **Extreme prices** (< 0.10 or > 0.90): Fees are minimal even on fee-bearing markets
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4. **Market making**: Maker rebates make spread-capture profitable on thin books
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# Viable Polymarket Trading Strategies (2026)
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On-chain analysis of 95 million transactions shows only 0.51% of Polymarket wallets
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have profits exceeding $1,000. Four strategies remain viable for bot builders.
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## 1. Market Making / Liquidity Provision
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**Win Rate**: 78-85%
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**Expected Monthly Return**: 1-3%
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**Minimum Bankroll**: $5,000+
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**Risk Level**: Medium
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Place limit orders on both sides of a market, earning the bid-ask spread plus
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Polymarket's liquidity reward program. Post-only orders (January 2026) and maker
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rebates create structural advantages.
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**How it works**:
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- Quote both bid and ask around a fair-value estimate
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- Earn the spread on each round-trip fill
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- Collect maker rebates on qualifying markets
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- Manage inventory risk by adjusting quotes based on position
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**Key risks**:
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- Adverse selection (informed traders pick you off)
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- Inventory accumulation on one side
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- Market resolution risk (holding when outcome becomes certain)
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**Best for**: Larger bankrolls, markets with stable prices and consistent volume.
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## 2. AI-Powered News Arbitrage
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**Win Rate**: 65-75%
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**Expected Monthly Return**: 3-8%
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**Minimum Bankroll**: $1,000+
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**Risk Level**: Medium-High
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Exploit the 30-second to 5-minute window where Polymarket prices have not adjusted
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to breaking news. One documented trade captured a 13 cent spread on a $2,000
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position ($896 profit in under 10 minutes) after Trump legal news broke.
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**How it works**:
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- Monitor news feeds (RSS, Twitter, official sources) with LLM analysis
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- Detect market-moving events before prices adjust
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- Place aggressive market orders in the direction indicated by the news
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- Exit once the market reaches new equilibrium
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**Key risks**:
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- Speed competition with sub-100ms bots
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- False signals from ambiguous news
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- Slippage on thin order books
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**Best for**: LLM-based agents with fast news processing. Natural fit for AI agents.
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## 3. Weather Market Exploitation
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**Win Rate**: 33% (but asymmetric payoff)
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**Expected Monthly Return**: Variable, potentially 10%+
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**Minimum Bankroll**: $100+
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**Risk Level**: Low-Medium
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Buy outcomes priced at 0.1-10 cents where real probability (from NOAA or weather
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models) is much higher. One bot turned $27 into $63,853 using Claude + NOAA APIs.
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Despite low win rate, the asymmetric payoff structure drives consistent profits.
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**How it works**:
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- Compare Polymarket weather prices against NOAA/NWS forecast data
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- Identify outcomes where market underestimates probability
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- Buy cheap shares on near-certain weather outcomes
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- Wait for resolution (typically 24-48 hours)
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**Key risks**:
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- Weather forecast uncertainty
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- Low liquidity on niche weather markets
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- Capital locked until resolution
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**Best for**: Small bankrolls, patient traders. Good entry point for beginners.
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## 4. Imbalance Arbitrage ("Gabagool")
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**Win Rate**: ~100% (mechanical)
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**Expected Monthly Return**: 0.5-2%
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**Minimum Bankroll**: $500+
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**Risk Level**: Very Low
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Buy YES and NO tokens at different timestamps when their combined cost dips below
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$1.00, guaranteeing profit regardless of outcome. Documented earning approximately
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$58.52 per 15-minute window through mechanical dual-side buying.
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**How it works**:
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- Monitor YES + NO price sums across active markets
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- When sum < $1.00, buy both sides
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- Guaranteed $1.00 payout on resolution minus cost
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- Profit = $1.00 - (YES cost + NO cost)
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**Key risks**:
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- Opportunities are rare and short-lived (2.7 seconds avg duration in 2026)
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- Capital efficiency is low (money locked until resolution)
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- Competition from sub-100ms bots has compressed most opportunities
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- Transaction timing: prices may shift between placing YES and NO orders
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**Best for**: Capital-rich, latency-sensitive setups. Less viable for LLM agents
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due to speed requirements.
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## Strategy Selection Guide
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| Bankroll | Recommended Strategy | Expected Return |
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|-------------|-------------------------------|-----------------|
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| < $500 | Weather exploitation | Variable |
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| $500-$2K | Weather + news arbitrage | 3-8%/month |
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| $2K-$10K | News arbitrage + market making | 2-5%/month |
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| > $10K | Market making (primary) | 1-3%/month |
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## Key Insight for AI Agents
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AI-powered news arbitrage is the natural fit for LLM-based trading agents. The
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agent's ability to rapidly process and interpret news, assess probability shifts,
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and generate trade signals creates a genuine edge. Market making and gabagool
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require sub-second execution that is better suited to traditional bot architectures.
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