Arbitrage Detector

Identifies potential arbitrage opportunities across Polymarket and Kalshi prediction markets.

Purpose

Finds discrepancies in contract prices for the same event across Polymarket and Kalshi prediction markets, calculating potential profit after fees.

Overview

Scans Polymarket and Kalshi for matching events. Fetches current prices for each event. Calculates the potential profit from buying on one platform and selling on the other, factoring in fees. Outputs a list of arbitrage opportunities ranked by potential profit.

Prerequisites

Tools: Read, Write, Bash, Glob, Grep, WebFetch

Environment: None required

Packages:

pip install requests pandas

Instructions

Step 1: Fetch Polymarket Data

Run the Polymarket data fetcher:

python {baseDir}/scripts/fetch_polymarket.py

This fetches active markets and current prices from Polymarket CLOB API, saving results to polymarket_data.json.

Step 2: Fetch Kalshi Data

Run the Kalshi data fetcher:

python {baseDir}/scripts/fetch_kalshi.py

This fetches open markets from Kalshi API, saving results to kalshi_data.json.

Step 3: Detect Arbitrage Opportunities

Run the arbitrage analyzer:

python {baseDir}/scripts/detect_arbitrage.py

This compares prices across platforms using fuzzy string matching (70% similarity threshold), calculates arbitrage strategies, and outputs opportunities sorted by profit percentage.

Step 4: Generate Report

Create the summary report:

python {baseDir}/scripts/generate_report.py

Generates a formatted markdown report with top opportunities and risk warnings.

Output

• polymarket_data.json: Raw market data from Polymarket
• kalshi_data.json: Raw market data from Kalshi
• arbitrage_opportunities.csv: All detected opportunities with profit calculations
• arbitrage_report.md: Formatted summary report

Error Handling

1. Error: Polymarket API Error Solution: Check Polymarket API status at status.polymarket.com. Retry after 30 seconds.

2. Error: Kalshi API Error Solution: Check Kalshi API status. May need API authentication for some endpoints.

3. Error: No matching events found Solution: Lower the similarity threshold (default 0.7) or manually map event names.

4. Error: Insufficient data to calculate arbitrage Solution: Ensure both platforms have YES and NO prices > 0.

Examples

Example 1: Profitable Arbitrage Found

Scenario: Same election event on both platforms

Event: "Will candidate X win the 2024 election?"
Polymarket: YES = 0.45, NO = 0.55
Kalshi: YES = 0.52, NO = 0.48

Strategy: Buy Polymarket YES + Kalshi NO
Cost: 0.45 * 1.02 + 0.48 * 1.01 = 0.459 + 0.485 = 0.944
Guaranteed Return: $1.00
Profit: $0.056 (5.9%)

Example 2: No Arbitrage

Scenario: Prices are efficiently aligned

Event: "Will it rain tomorrow in NYC?"
Polymarket: YES = 0.50, NO = 0.50
Kalshi: YES = 0.50, NO = 0.50

Cost of any strategy > $1.00 after fees
Result: No profitable arbitrage

Resources

• Polymarket data fetcher: {baseDir}/scripts/fetch_polymarket.py
• Kalshi data fetcher: {baseDir}/scripts/fetch_kalshi.py
• Arbitrage analyzer: {baseDir}/scripts/detect_arbitrage.py
• Report generator: {baseDir}/scripts/generate_report.py

Usage

Run the complete workflow:

# 1. Fetch data from both platforms
python {baseDir}/scripts/fetch_polymarket.py
python {baseDir}/scripts/fetch_kalshi.py

# 2. Detect arbitrage opportunities
python {baseDir}/scripts/detect_arbitrage.py

# 3. Generate report
python {baseDir}/scripts/generate_report.py

Or invoke this skill to generate and execute all scripts automatically.