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>
This commit is contained in:
Polymarket Skills Builder
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
6a03bbe2e5
commit
068b2adc75
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---
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name: polymarket-analyzer
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description: >
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Use this skill whenever the user wants to find trading opportunities, detect arbitrage,
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analyze a market, perform edge detection, find mispricing, do probability analysis,
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evaluate orderbook depth, find momentum signals, or assess Polymarket market quality.
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Triggers: "find opportunities", "detect arbitrage", "analyze market", "edge detection",
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"mispricing", "probability analysis", "orderbook analysis", "momentum scanner",
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"market inefficiency", "price gap", "volume surge", "trading edge", "market analysis".
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---
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# Polymarket Analyzer Skill
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Detect trading edges and opportunities across Polymarket prediction markets using
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real-time data from the Gamma and CLOB APIs. Zero authentication required -- all
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analysis is read-only.
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## Available Scripts
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### 1. Find Arbitrage Edges (`scripts/find_edges.py`)
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Scans all active markets for pricing inefficiencies:
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- **Underpriced**: YES + NO < $1.00 (guaranteed profit if you buy both sides)
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- **Overpriced**: YES + NO > $1.02 (sell opportunity)
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- Calculates profit after fees for each opportunity
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- Outputs market name, prices, sum, potential profit, and fee impact
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```bash
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python scripts/find_edges.py
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python scripts/find_edges.py --min-edge 0.02 --limit 500
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```
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### 2. Analyze Order Book (`scripts/analyze_orderbook.py`)
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Deep analysis of a single market's order book:
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- Spread, mid-price, bid/ask depth (top N levels)
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- Bid-ask imbalance ratio (signals directional pressure)
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- Thin vs thick book classification
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- Liquidity concentration analysis
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```bash
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python scripts/analyze_orderbook.py --token-id <TOKEN_ID>
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python scripts/analyze_orderbook.py --token-id <TOKEN_ID> --depth 10
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```
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### 3. Momentum Scanner (`scripts/momentum_scanner.py`)
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Detect markets with unusual activity:
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- **Volume surges**: 24h volume significantly exceeds 7-day average
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- **Price momentum**: recent price moves in one direction
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- **Liquidity changes**: markets gaining or losing depth
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- Ranked output by signal strength
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```bash
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python scripts/momentum_scanner.py
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python scripts/momentum_scanner.py --min-volume 10000 --limit 300
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```
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## Workflow
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1. Run `find_edges.py` to scan for arbitrage across all active markets
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2. For interesting markets, run `analyze_orderbook.py` to check if the edge is executable
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3. Run `momentum_scanner.py` to find markets with directional momentum
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4. Combine findings to identify the best opportunities
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## Fee Awareness
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Most Polymarket markets are fee-free. Crypto 5-min/15-min markets have dynamic taker
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fees: `fee = baseRate * min(price, 1 - price) * size`. See `references/fee-model.md`
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for the full fee calculator and breakeven analysis.
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## Strategy Reference
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See `references/viable-strategies.md` for the four strategies that still work in 2026
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with win rates, expected returns, and risk profiles.
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## Important Disclaimers
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- This skill performs read-only analysis only -- no trades are executed
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- Past patterns do not guarantee future results
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- Always verify opportunities manually before trading
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- Not financial advice
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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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+244
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#!/usr/bin/env python3
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"""Analyze a Polymarket order book for a given token ID.
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Calculates spread, depth, bid-ask imbalance, and classifies book thickness.
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Requires: py-clob-client (pip install py-clob-client)
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"""
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import argparse
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import json
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import sys
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from py_clob_client.client import ClobClient
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CLOB_HOST = "https://clob.polymarket.com"
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def fetch_orderbook(token_id: str) -> object:
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"""Fetch order book from CLOB API."""
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client = ClobClient(CLOB_HOST)
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return client.get_order_book(token_id)
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def analyze(book, depth: int = 5) -> dict:
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"""Analyze an order book and return metrics."""
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bids = [(float(b.price), float(b.size)) for b in (book.bids or [])]
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asks = [(float(a.price), float(a.size)) for a in (book.asks or [])]
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# Sort: bids descending by price, asks ascending by price
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bids.sort(key=lambda x: x[0], reverse=True)
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asks.sort(key=lambda x: x[0])
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result = {
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"token_id": book.asset_id,
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"total_bid_levels": len(bids),
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"total_ask_levels": len(asks),
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}
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if not bids and not asks:
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result["status"] = "EMPTY_BOOK"
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return result
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# Best bid / best ask
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best_bid = bids[0][0] if bids else 0.0
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best_ask = asks[0][0] if asks else 1.0
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spread = best_ask - best_bid
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mid_price = (best_bid + best_ask) / 2.0 if (bids and asks) else None
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result["best_bid"] = best_bid
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result["best_ask"] = best_ask
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result["spread"] = round(spread, 6)
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result["spread_pct"] = round((spread / mid_price * 100) if mid_price else 0, 4)
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result["mid_price"] = round(mid_price, 6) if mid_price else None
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# Depth at top N levels
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top_bids = bids[:depth]
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top_asks = asks[:depth]
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bid_depth = sum(size for _, size in top_bids)
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ask_depth = sum(size for _, size in top_asks)
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total_depth = bid_depth + ask_depth
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result["bid_depth"] = round(bid_depth, 2)
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result["ask_depth"] = round(ask_depth, 2)
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result["total_depth"] = round(total_depth, 2)
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result["depth_levels_used"] = depth
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# Bid-ask imbalance ratio: positive = more bids (buying pressure)
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if total_depth > 0:
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imbalance = (bid_depth - ask_depth) / total_depth
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else:
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imbalance = 0.0
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result["imbalance_ratio"] = round(imbalance, 4)
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# Classify the imbalance
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if imbalance > 0.3:
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result["imbalance_signal"] = "STRONG_BUY_PRESSURE"
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elif imbalance > 0.1:
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result["imbalance_signal"] = "MODERATE_BUY_PRESSURE"
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elif imbalance < -0.3:
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result["imbalance_signal"] = "STRONG_SELL_PRESSURE"
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elif imbalance < -0.1:
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result["imbalance_signal"] = "MODERATE_SELL_PRESSURE"
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else:
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result["imbalance_signal"] = "BALANCED"
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# Book thickness classification
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if total_depth < 500:
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result["book_class"] = "THIN"
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result["book_note"] = "Easy to move price; high slippage risk"
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elif total_depth < 5000:
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result["book_class"] = "MODERATE"
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result["book_note"] = "Normal depth; moderate slippage on large orders"
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else:
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result["book_class"] = "THICK"
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result["book_note"] = "Stable book; low slippage for most order sizes"
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# Bid levels detail
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result["bid_levels"] = [
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{"price": p, "size": round(s, 2), "cumulative": round(sum(sz for _, sz in top_bids[:i+1]), 2)}
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for i, (p, s) in enumerate(top_bids)
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]
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result["ask_levels"] = [
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{"price": p, "size": round(s, 2), "cumulative": round(sum(sz for _, sz in top_asks[:i+1]), 2)}
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for i, (p, s) in enumerate(top_asks)
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]
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# Slippage estimate: cost to buy/sell $100 worth
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slippage_size = 100.0
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result["buy_slippage"] = _estimate_slippage(asks, slippage_size)
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result["sell_slippage"] = _estimate_slippage(
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[(p, s) for p, s in bids], slippage_size, selling=True
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)
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return result
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def _estimate_slippage(
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levels: list[tuple[float, float]], target_size: float, selling: bool = False
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) -> dict | None:
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"""Estimate average fill price and slippage for a target size."""
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if not levels:
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return None
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filled = 0.0
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cost = 0.0
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for price, size in levels:
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remaining = target_size - filled
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fill_qty = min(size, remaining)
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cost += fill_qty * price
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filled += fill_qty
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if filled >= target_size:
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break
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if filled == 0:
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return None
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avg_price = cost / filled
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best_price = levels[0][0]
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slippage = abs(avg_price - best_price)
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return {
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"target_size": target_size,
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"filled": round(filled, 2),
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"avg_price": round(avg_price, 6),
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"best_price": best_price,
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"slippage": round(slippage, 6),
|
||||
"slippage_pct": round(slippage / best_price * 100 if best_price else 0, 4),
|
||||
"fully_filled": filled >= target_size,
|
||||
}
|
||||
|
||||
|
||||
def format_output(result: dict) -> str:
|
||||
"""Format analysis result for display."""
|
||||
lines = []
|
||||
lines.append(f"Order Book Analysis for {result['token_id'][:30]}...")
|
||||
lines.append("=" * 70)
|
||||
|
||||
if result.get("status") == "EMPTY_BOOK":
|
||||
lines.append("Order book is empty -- no bids or asks.")
|
||||
return "\n".join(lines)
|
||||
|
||||
lines.append(f" Best Bid: ${result['best_bid']:.4f}")
|
||||
lines.append(f" Best Ask: ${result['best_ask']:.4f}")
|
||||
lines.append(f" Mid Price: ${result['mid_price']:.4f}" if result['mid_price'] else " Mid Price: N/A")
|
||||
lines.append(f" Spread: ${result['spread']:.4f} ({result['spread_pct']:.2f}%)")
|
||||
lines.append("")
|
||||
|
||||
lines.append(f"Depth (top {result['depth_levels_used']} levels):")
|
||||
lines.append(f" Bid Depth: {result['bid_depth']:,.2f} shares")
|
||||
lines.append(f" Ask Depth: {result['ask_depth']:,.2f} shares")
|
||||
lines.append(f" Total: {result['total_depth']:,.2f} shares")
|
||||
lines.append(f" Imbalance: {result['imbalance_ratio']:+.4f} ({result['imbalance_signal']})")
|
||||
lines.append(f" Book Class: {result['book_class']} -- {result['book_note']}")
|
||||
lines.append("")
|
||||
|
||||
lines.append("Bid Levels:")
|
||||
lines.append(f" {'Price':>8} {'Size':>10} {'Cumulative':>12}")
|
||||
for lvl in result.get("bid_levels", []):
|
||||
lines.append(f" ${lvl['price']:<7.4f} {lvl['size']:>10,.2f} {lvl['cumulative']:>12,.2f}")
|
||||
|
||||
lines.append("")
|
||||
lines.append("Ask Levels:")
|
||||
lines.append(f" {'Price':>8} {'Size':>10} {'Cumulative':>12}")
|
||||
for lvl in result.get("ask_levels", []):
|
||||
lines.append(f" ${lvl['price']:<7.4f} {lvl['size']:>10,.2f} {lvl['cumulative']:>12,.2f}")
|
||||
|
||||
for label, key in [("Buy", "buy_slippage"), ("Sell", "sell_slippage")]:
|
||||
slip = result.get(key)
|
||||
lines.append("")
|
||||
if slip:
|
||||
status = "YES" if slip["fully_filled"] else "PARTIAL"
|
||||
lines.append(
|
||||
f"{label} Slippage ({slip['target_size']:.0f} shares): "
|
||||
f"avg ${slip['avg_price']:.4f}, "
|
||||
f"slippage ${slip['slippage']:.4f} ({slip['slippage_pct']:.2f}%), "
|
||||
f"filled: {status}"
|
||||
)
|
||||
else:
|
||||
lines.append(f"{label} Slippage: No liquidity on this side")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Analyze Polymarket order book"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--token-id",
|
||||
required=True,
|
||||
help="CLOB token ID to analyze",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--depth",
|
||||
type=int,
|
||||
default=5,
|
||||
help="Number of price levels to analyze (default: 5)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--json",
|
||||
action="store_true",
|
||||
help="Output results as JSON",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
try:
|
||||
book = fetch_orderbook(args.token_id)
|
||||
except Exception as e:
|
||||
print(f"Error fetching order book: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
result = analyze(book, depth=args.depth)
|
||||
|
||||
if args.json:
|
||||
print(json.dumps(result, indent=2))
|
||||
else:
|
||||
print(format_output(result))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Executable
+341
@@ -0,0 +1,341 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Scan active Polymarket markets for arbitrage edges.
|
||||
|
||||
Detects:
|
||||
- Underpriced markets: best-ask YES + best-ask NO < $1.00 (buy both for profit)
|
||||
- Overpriced markets: best-bid YES + best-bid NO > $1.00 (sell both for profit)
|
||||
- Wide spreads: markets where bid-ask spread creates opportunity
|
||||
|
||||
Uses Gamma API for market discovery and CLOB API for real order book prices.
|
||||
Gamma mid-prices always sum to $1.00 by construction, so order book prices are
|
||||
needed to find real executable edges.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
import time
|
||||
|
||||
import requests
|
||||
from py_clob_client.client import ClobClient
|
||||
|
||||
|
||||
GAMMA_API = "https://gamma-api.polymarket.com"
|
||||
CLOB_HOST = "https://clob.polymarket.com"
|
||||
|
||||
|
||||
def fetch_markets(limit: int = 100, offset: int = 0) -> list[dict]:
|
||||
"""Fetch active markets from Gamma API."""
|
||||
url = (
|
||||
f"{GAMMA_API}/markets"
|
||||
f"?limit={limit}&offset={offset}&active=true&closed=false"
|
||||
)
|
||||
resp = requests.get(url, timeout=15)
|
||||
resp.raise_for_status()
|
||||
return resp.json()
|
||||
|
||||
|
||||
def parse_token_ids(market: dict) -> tuple[str, str] | None:
|
||||
"""Extract YES and NO token IDs from a market dict."""
|
||||
raw = market.get("clobTokenIds")
|
||||
if not raw:
|
||||
return None
|
||||
try:
|
||||
ids = json.loads(raw)
|
||||
if len(ids) < 2:
|
||||
return None
|
||||
return ids[0], ids[1]
|
||||
except (json.JSONDecodeError, ValueError, IndexError):
|
||||
return None
|
||||
|
||||
|
||||
def parse_mid_prices(market: dict) -> tuple[float, float] | None:
|
||||
"""Extract mid-prices from Gamma API (for display context)."""
|
||||
raw = market.get("outcomePrices")
|
||||
if not raw:
|
||||
return None
|
||||
try:
|
||||
prices = json.loads(raw)
|
||||
if len(prices) < 2:
|
||||
return None
|
||||
return float(prices[0]), float(prices[1])
|
||||
except (json.JSONDecodeError, ValueError, IndexError):
|
||||
return None
|
||||
|
||||
|
||||
def calculate_fee(price: float, base_rate: float = 0.063) -> float:
|
||||
"""Calculate dynamic taker fee rate for fee-bearing markets."""
|
||||
return base_rate * min(price, 1.0 - price)
|
||||
|
||||
|
||||
def get_book_prices(client: ClobClient, token_id: str) -> tuple[float, float] | None:
|
||||
"""Get best bid and best ask for a token. Returns (best_bid, best_ask) or None."""
|
||||
try:
|
||||
book = client.get_order_book(token_id)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
bids = [(float(b.price), float(b.size)) for b in (book.bids or [])]
|
||||
asks = [(float(a.price), float(a.size)) for a in (book.asks or [])]
|
||||
|
||||
bids.sort(key=lambda x: x[0], reverse=True)
|
||||
asks.sort(key=lambda x: x[0])
|
||||
|
||||
best_bid = bids[0][0] if bids else None
|
||||
best_ask = asks[0][0] if asks else None
|
||||
|
||||
if best_bid is None or best_ask is None:
|
||||
return None
|
||||
return best_bid, best_ask
|
||||
|
||||
|
||||
def scan_edges(
|
||||
max_markets: int = 200,
|
||||
min_edge: float = 0.005,
|
||||
check_orderbooks: bool = True,
|
||||
) -> list[dict]:
|
||||
"""Scan markets for pricing edges.
|
||||
|
||||
Two modes:
|
||||
1. Fast scan (check_orderbooks=False): Uses Gamma mid-prices (always sum to 1.0,
|
||||
so only finds spread-based opportunities via CLOB spot check)
|
||||
2. Deep scan (check_orderbooks=True): Fetches actual order book for each market
|
||||
to find real executable edges (slower, rate-limited)
|
||||
"""
|
||||
client = ClobClient(CLOB_HOST) if check_orderbooks else None
|
||||
edges = []
|
||||
offset = 0
|
||||
batch_size = 100
|
||||
fetched = 0
|
||||
checked_books = 0
|
||||
|
||||
while fetched < max_markets:
|
||||
batch = fetch_markets(limit=batch_size, offset=offset)
|
||||
if not batch:
|
||||
break
|
||||
|
||||
for market in batch:
|
||||
token_ids = parse_token_ids(market)
|
||||
mid_prices = parse_mid_prices(market)
|
||||
|
||||
if token_ids is None or mid_prices is None:
|
||||
continue
|
||||
|
||||
yes_token_id, no_token_id = token_ids
|
||||
yes_mid, no_mid = mid_prices
|
||||
|
||||
# Skip very low-liquidity markets
|
||||
liquidity = float(market.get("liquidityNum", 0) or 0)
|
||||
if liquidity < 100:
|
||||
continue
|
||||
|
||||
if not check_orderbooks:
|
||||
continue
|
||||
|
||||
# Fetch real order book prices
|
||||
yes_book = get_book_prices(client, yes_token_id)
|
||||
no_book = get_book_prices(client, no_token_id)
|
||||
checked_books += 1
|
||||
|
||||
if yes_book is None or no_book is None:
|
||||
continue
|
||||
|
||||
yes_bid, yes_ask = yes_book
|
||||
no_bid, no_ask = no_book
|
||||
|
||||
# Check underpriced: buy YES at ask + buy NO at ask < $1.00
|
||||
buy_both_cost = yes_ask + no_ask
|
||||
if buy_both_cost < (1.0 - min_edge):
|
||||
raw_edge = 1.0 - buy_both_cost
|
||||
yes_fee = calculate_fee(yes_ask)
|
||||
no_fee = calculate_fee(no_ask)
|
||||
total_fee = yes_fee + no_fee
|
||||
net = raw_edge - total_fee
|
||||
|
||||
edges.append({
|
||||
"question": market.get("question", "Unknown"),
|
||||
"slug": market.get("slug", ""),
|
||||
"type": "UNDERPRICED",
|
||||
"yes_ask": yes_ask,
|
||||
"no_ask": no_ask,
|
||||
"cost_sum": round(buy_both_cost, 6),
|
||||
"raw_edge": round(raw_edge, 6),
|
||||
"fee_impact": round(total_fee, 6),
|
||||
"net_profit_per_share": round(net, 6),
|
||||
"profitable_after_fees": net > 0,
|
||||
"yes_mid": yes_mid,
|
||||
"no_mid": no_mid,
|
||||
"volume_24h": market.get("volume24hr", 0) or 0,
|
||||
"liquidity": liquidity,
|
||||
})
|
||||
|
||||
# Check overpriced: sell YES at bid + sell NO at bid > $1.00
|
||||
sell_both_value = yes_bid + no_bid
|
||||
if sell_both_value > (1.0 + max(min_edge, 0.005)):
|
||||
raw_edge = sell_both_value - 1.0
|
||||
yes_fee = calculate_fee(yes_bid)
|
||||
no_fee = calculate_fee(no_bid)
|
||||
total_fee = yes_fee + no_fee
|
||||
net = raw_edge - total_fee
|
||||
|
||||
edges.append({
|
||||
"question": market.get("question", "Unknown"),
|
||||
"slug": market.get("slug", ""),
|
||||
"type": "OVERPRICED",
|
||||
"yes_bid": yes_bid,
|
||||
"no_bid": no_bid,
|
||||
"cost_sum": round(sell_both_value, 6),
|
||||
"raw_edge": round(raw_edge, 6),
|
||||
"fee_impact": round(total_fee, 6),
|
||||
"net_profit_per_share": round(net, 6),
|
||||
"profitable_after_fees": net > 0,
|
||||
"yes_mid": yes_mid,
|
||||
"no_mid": no_mid,
|
||||
"volume_24h": market.get("volume24hr", 0) or 0,
|
||||
"liquidity": liquidity,
|
||||
})
|
||||
|
||||
# Also report wide spreads (opportunity for market making)
|
||||
yes_spread = yes_ask - yes_bid
|
||||
no_spread = no_ask - no_bid
|
||||
max_spread = max(yes_spread, no_spread)
|
||||
if max_spread >= 0.03: # 3 cent spread or wider
|
||||
edges.append({
|
||||
"question": market.get("question", "Unknown"),
|
||||
"slug": market.get("slug", ""),
|
||||
"type": "WIDE_SPREAD",
|
||||
"yes_bid": yes_bid,
|
||||
"yes_ask": yes_ask,
|
||||
"yes_spread": round(yes_spread, 6),
|
||||
"no_bid": no_bid,
|
||||
"no_ask": no_ask,
|
||||
"no_spread": round(no_spread, 6),
|
||||
"max_spread": round(max_spread, 6),
|
||||
"raw_edge": round(max_spread, 6),
|
||||
"fee_impact": 0.0,
|
||||
"net_profit_per_share": round(max_spread, 6),
|
||||
"profitable_after_fees": True,
|
||||
"yes_mid": yes_mid,
|
||||
"no_mid": no_mid,
|
||||
"volume_24h": market.get("volume24hr", 0) or 0,
|
||||
"liquidity": liquidity,
|
||||
})
|
||||
|
||||
# Rate limit: avoid hammering the CLOB API
|
||||
if checked_books % 5 == 0:
|
||||
time.sleep(0.2)
|
||||
|
||||
fetched += len(batch)
|
||||
offset += batch_size
|
||||
|
||||
if len(batch) < batch_size:
|
||||
break
|
||||
|
||||
# Sort by raw edge descending
|
||||
edges.sort(key=lambda x: x["raw_edge"], reverse=True)
|
||||
return edges
|
||||
|
||||
|
||||
def format_output(edges: list[dict]) -> str:
|
||||
"""Format edges for display."""
|
||||
if not edges:
|
||||
return (
|
||||
"No arbitrage edges found in current markets.\n"
|
||||
"This is normal -- Polymarket is well-arbitraged, with most\n"
|
||||
"opportunities lasting only ~2.7 seconds (median) in 2026."
|
||||
)
|
||||
|
||||
lines = []
|
||||
|
||||
# Group by type
|
||||
underpriced = [e for e in edges if e["type"] == "UNDERPRICED"]
|
||||
overpriced = [e for e in edges if e["type"] == "OVERPRICED"]
|
||||
wide_spread = [e for e in edges if e["type"] == "WIDE_SPREAD"]
|
||||
|
||||
if underpriced:
|
||||
lines.append(f"\n=== UNDERPRICED ({len(underpriced)}) - Buy both sides for guaranteed profit ===\n")
|
||||
lines.append(f" {'YES ask':>8} {'NO ask':>8} {'Sum':>8} {'Edge':>7} {'Net':>7} {'Vol24h':>10} Question")
|
||||
lines.append(" " + "-" * 100)
|
||||
for e in underpriced:
|
||||
marker = " *" if e["profitable_after_fees"] else ""
|
||||
lines.append(
|
||||
f" ${e['yes_ask']:<7.4f} ${e['no_ask']:<7.4f} "
|
||||
f"${e['cost_sum']:<7.4f} ${e['raw_edge']:<6.4f} "
|
||||
f"${e['net_profit_per_share']:<+6.4f}{marker} "
|
||||
f"${e['volume_24h']:>9,.0f} {e['question'][:55]}"
|
||||
)
|
||||
|
||||
if overpriced:
|
||||
lines.append(f"\n=== OVERPRICED ({len(overpriced)}) - Sell both sides ===\n")
|
||||
lines.append(f" {'YES bid':>8} {'NO bid':>8} {'Sum':>8} {'Edge':>7} {'Net':>7} {'Vol24h':>10} Question")
|
||||
lines.append(" " + "-" * 100)
|
||||
for e in overpriced:
|
||||
marker = " *" if e["profitable_after_fees"] else ""
|
||||
lines.append(
|
||||
f" ${e['yes_bid']:<7.4f} ${e['no_bid']:<7.4f} "
|
||||
f"${e['cost_sum']:<7.4f} ${e['raw_edge']:<6.4f} "
|
||||
f"${e['net_profit_per_share']:<+6.4f}{marker} "
|
||||
f"${e['volume_24h']:>9,.0f} {e['question'][:55]}"
|
||||
)
|
||||
|
||||
if wide_spread:
|
||||
lines.append(f"\n=== WIDE SPREADS ({len(wide_spread)}) - Market-making opportunities ===\n")
|
||||
lines.append(f" {'Y Spread':>8} {'N Spread':>8} {'Max':>7} {'Vol24h':>10} {'Liq':>10} Question")
|
||||
lines.append(" " + "-" * 100)
|
||||
for e in wide_spread:
|
||||
lines.append(
|
||||
f" ${e.get('yes_spread', 0):<7.4f} ${e.get('no_spread', 0):<7.4f} "
|
||||
f"${e.get('max_spread', 0):<6.4f} "
|
||||
f"${e['volume_24h']:>9,.0f} "
|
||||
f"${e['liquidity']:>9,.0f} "
|
||||
f"{e['question'][:55]}"
|
||||
)
|
||||
|
||||
lines.append("")
|
||||
lines.append("* = profitable even on fee-bearing markets (most markets are fee-free)")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Scan Polymarket for arbitrage edges using real order book data"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--min-edge",
|
||||
type=float,
|
||||
default=0.005,
|
||||
help="Minimum edge to report (default: 0.005 = $0.005/share)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--limit",
|
||||
type=int,
|
||||
default=200,
|
||||
help="Maximum markets to scan (default: 200, each requires 2 API calls)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--json",
|
||||
action="store_true",
|
||||
help="Output results as JSON",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
print(f"Scanning up to {args.limit} markets (2 order book lookups each)...",
|
||||
file=sys.stderr)
|
||||
|
||||
try:
|
||||
edges = scan_edges(
|
||||
max_markets=args.limit,
|
||||
min_edge=args.min_edge,
|
||||
)
|
||||
except requests.RequestException as e:
|
||||
print(f"Error fetching data: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
if args.json:
|
||||
print(json.dumps(edges, indent=2))
|
||||
else:
|
||||
print(format_output(edges))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
+229
@@ -0,0 +1,229 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Scan Polymarket for momentum signals: volume surges and price trends.
|
||||
|
||||
Detects:
|
||||
- Volume surges: 24h volume significantly exceeds 7-day daily average
|
||||
- Price momentum: markets with strong directional price movement
|
||||
- Liquidity anomalies: unusually high or low liquidity relative to volume
|
||||
|
||||
Uses Gamma API (no auth required).
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
|
||||
import requests
|
||||
|
||||
|
||||
GAMMA_API = "https://gamma-api.polymarket.com"
|
||||
|
||||
|
||||
def fetch_markets(limit: int = 100, offset: int = 0) -> list[dict]:
|
||||
"""Fetch active markets from Gamma API."""
|
||||
url = (
|
||||
f"{GAMMA_API}/markets"
|
||||
f"?limit={limit}&offset={offset}&active=true&closed=false"
|
||||
)
|
||||
resp = requests.get(url, timeout=15)
|
||||
resp.raise_for_status()
|
||||
return resp.json()
|
||||
|
||||
|
||||
def compute_signals(market: dict) -> dict | None:
|
||||
"""Compute momentum signals for a single market."""
|
||||
vol_24h = float(market.get("volume24hr", 0) or 0)
|
||||
vol_1wk = float(market.get("volume1wk", 0) or 0)
|
||||
liquidity = float(market.get("liquidityNum", 0) or 0)
|
||||
|
||||
# Need at least some volume data
|
||||
if vol_24h <= 0 and vol_1wk <= 0:
|
||||
return None
|
||||
|
||||
# Parse prices
|
||||
raw_prices = market.get("outcomePrices")
|
||||
if not raw_prices:
|
||||
return None
|
||||
try:
|
||||
prices = json.loads(raw_prices)
|
||||
yes_price = float(prices[0])
|
||||
except (json.JSONDecodeError, ValueError, IndexError):
|
||||
return None
|
||||
|
||||
# Volume surge: compare 24h volume to 7-day daily average
|
||||
daily_avg_7d = vol_1wk / 7.0 if vol_1wk > 0 else 0
|
||||
if daily_avg_7d > 0:
|
||||
volume_ratio = vol_24h / daily_avg_7d
|
||||
else:
|
||||
volume_ratio = 0.0
|
||||
|
||||
# Price extremity: how far from 0.50 (max uncertainty)
|
||||
# Prices near 0 or 1 suggest strong directional conviction
|
||||
price_extremity = abs(yes_price - 0.5) * 2.0 # 0 at 0.50, 1 at 0 or 1
|
||||
|
||||
# Volume-to-liquidity ratio: high ratio suggests heavy activity relative to depth
|
||||
vol_liq_ratio = vol_24h / liquidity if liquidity > 0 else 0
|
||||
|
||||
# Composite momentum score
|
||||
# volume_ratio contributes most -- a surge is the primary signal
|
||||
score = 0.0
|
||||
if volume_ratio > 1.0:
|
||||
score += min((volume_ratio - 1.0) * 0.4, 2.0) # Cap contribution at 2.0
|
||||
if vol_liq_ratio > 1.0:
|
||||
score += min((vol_liq_ratio - 1.0) * 0.3, 1.5)
|
||||
# Extreme prices amplify the signal (market is moving toward resolution)
|
||||
if price_extremity > 0.6:
|
||||
score += (price_extremity - 0.6) * 0.3
|
||||
|
||||
if score <= 0:
|
||||
return None
|
||||
|
||||
# Classify the signal
|
||||
if volume_ratio >= 3.0:
|
||||
volume_signal = "VOLUME_SURGE"
|
||||
elif volume_ratio >= 1.5:
|
||||
volume_signal = "ELEVATED_VOLUME"
|
||||
else:
|
||||
volume_signal = "NORMAL_VOLUME"
|
||||
|
||||
if yes_price >= 0.85:
|
||||
direction = "STRONG_YES"
|
||||
elif yes_price >= 0.65:
|
||||
direction = "LEANING_YES"
|
||||
elif yes_price <= 0.15:
|
||||
direction = "STRONG_NO"
|
||||
elif yes_price <= 0.35:
|
||||
direction = "LEANING_NO"
|
||||
else:
|
||||
direction = "NEUTRAL"
|
||||
|
||||
return {
|
||||
"question": market.get("question", "Unknown"),
|
||||
"slug": market.get("slug", ""),
|
||||
"yes_price": yes_price,
|
||||
"direction": direction,
|
||||
"volume_24h": round(vol_24h, 2),
|
||||
"daily_avg_7d": round(daily_avg_7d, 2),
|
||||
"volume_ratio": round(volume_ratio, 2),
|
||||
"volume_signal": volume_signal,
|
||||
"liquidity": round(liquidity, 2),
|
||||
"vol_liq_ratio": round(vol_liq_ratio, 2),
|
||||
"momentum_score": round(score, 4),
|
||||
}
|
||||
|
||||
|
||||
def scan_momentum(
|
||||
max_markets: int = 300,
|
||||
min_volume: float = 1000.0,
|
||||
min_score: float = 0.1,
|
||||
) -> list[dict]:
|
||||
"""Scan markets and rank by momentum score."""
|
||||
signals = []
|
||||
offset = 0
|
||||
batch_size = 100
|
||||
fetched = 0
|
||||
|
||||
while fetched < max_markets:
|
||||
batch = fetch_markets(limit=batch_size, offset=offset)
|
||||
if not batch:
|
||||
break
|
||||
|
||||
for market in batch:
|
||||
vol_24h = float(market.get("volume24hr", 0) or 0)
|
||||
if vol_24h < min_volume:
|
||||
continue
|
||||
|
||||
sig = compute_signals(market)
|
||||
if sig and sig["momentum_score"] >= min_score:
|
||||
signals.append(sig)
|
||||
|
||||
fetched += len(batch)
|
||||
offset += batch_size
|
||||
|
||||
if len(batch) < batch_size:
|
||||
break
|
||||
|
||||
# Rank by momentum score descending
|
||||
signals.sort(key=lambda x: x["momentum_score"], reverse=True)
|
||||
return signals
|
||||
|
||||
|
||||
def format_output(signals: list[dict]) -> str:
|
||||
"""Format momentum signals for display."""
|
||||
if not signals:
|
||||
return "No momentum signals found matching criteria."
|
||||
|
||||
lines = []
|
||||
lines.append(f"Found {len(signals)} market(s) with momentum signals:\n")
|
||||
lines.append(
|
||||
f"{'Score':>6} {'YES':>5} {'Direction':<12} "
|
||||
f"{'VolRatio':>8} {'Signal':<16} "
|
||||
f"{'Vol24h':>12} {'Avg7d':>10} Question"
|
||||
)
|
||||
lines.append("-" * 120)
|
||||
|
||||
for s in signals:
|
||||
lines.append(
|
||||
f"{s['momentum_score']:>6.2f} "
|
||||
f"${s['yes_price']:<4.2f} "
|
||||
f"{s['direction']:<12} "
|
||||
f"{s['volume_ratio']:>7.1f}x "
|
||||
f"{s['volume_signal']:<16} "
|
||||
f"${s['volume_24h']:>11,.0f} "
|
||||
f"${s['daily_avg_7d']:>9,.0f} "
|
||||
f"{s['question'][:55]}"
|
||||
)
|
||||
|
||||
lines.append("")
|
||||
lines.append("Score = composite of volume surge, vol/liquidity ratio, and price extremity.")
|
||||
lines.append("Volume Ratio = 24h volume / 7-day daily average (>3x = VOLUME_SURGE).")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Scan Polymarket for momentum signals"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--min-volume",
|
||||
type=float,
|
||||
default=1000,
|
||||
help="Minimum 24h volume to consider (default: $1,000)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--min-score",
|
||||
type=float,
|
||||
default=0.1,
|
||||
help="Minimum momentum score to report (default: 0.1)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--limit",
|
||||
type=int,
|
||||
default=300,
|
||||
help="Maximum number of markets to scan (default: 300)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--json",
|
||||
action="store_true",
|
||||
help="Output results as JSON",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
try:
|
||||
signals = scan_momentum(
|
||||
max_markets=args.limit,
|
||||
min_volume=args.min_volume,
|
||||
min_score=args.min_score,
|
||||
)
|
||||
except requests.RequestException as e:
|
||||
print(f"Error fetching data from Gamma API: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
if args.json:
|
||||
print(json.dumps(signals, indent=2))
|
||||
else:
|
||||
print(format_output(signals))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user