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Cross-Exchange Arbitrage: Kalshi, Polymarket, and PredictIt

6 min read
  • arbitrage
  • kalshi
  • trading

Cross-Exchange Arbitrage: Kalshi, Polymarket, and PredictIt

Blog illustration

Cross-exchange arbitrage presents an exciting opportunity for traders leveraging the price discrepancies of similar contracts on different prediction markets. In this article, we will delve into how arbitrage strategies can be effectively executed across platforms like Kalshi, Polymarket, and PredictIt. By examining market structure, trading strategies, and practical examples involving Python, we will provide a comprehensive guide for traders looking to enhance their skill set in this niche.

Understanding Prediction Markets

What are Prediction Markets?

Prediction markets are platforms that enable users to trade contracts based on the outcome of future events. Typically, these platforms allow participants to buy and sell contracts that will pay off based on a specified event occurring or not. Prices in these markets can be interpreted as the probability of an event's occurrence, which enables traders to speculate or hedge based on their expectations.

Overview of Key Platforms

  • Kalshi: A regulated market that provides a wide array of event contracts, such as economic indicators and political events. Kalshi stands out by being one of the first regulated exchanges solely dedicated to trading event contracts.

  • Polymarket: An unregulated platform that leverages blockchain technology to host various betting markets. It allows users to trade on concepts typically associated with traditional prediction markets.

  • PredictIt: Known for its focus on political events, PredictIt offers a limited yet diverse range of trading contracts. The platform, backed by a unique regulatory framework circumventing traditional gaming laws, allows a relatively straightforward trading experience.

Arbitrage Opportunities

Arbitrage involves taking advantage of price discrepancies across different exchanges. Traders can enhance profitability by executing trades in such a way that they lock in a profit regardless of the outcome of the event. Below are some examples and practical insights into cross-exchange arbitrage opportunities in the context of Kalshi, Polymarket, and PredictIt.

Example 1: Betting on a Political Event

Let’s assume that the upcoming U.S. presidential election is the event in focus. You notice the following prices across the platforms:

  • Kalshi: A contract indicating a candidate winning for $0.45 (implying a 45% probability).
  • Polymarket: Similar contract prices the outcome at $0.55.
  • PredictIt: The same contract trades at $0.40.

In this scenario, you can initiate an arbitrage trade.

# Pseudo-code for an arbitrage strategy
kalshi_price = 0.45
polymarket_price = 0.55
predictit_price = 0.40

# Buy from the cheapest
if kalshi_price < polymarket_price and kalshi_price < predictit_price:
    buy_kalshi = True  # Buy from Kalshi
    sell_polymarket = True  # Sell on Polymarket
elif polymarket_price < kalshi_price and polymarket_price < predictit_price:
    buy_polymarket = True  # Buy from Polymarket
    sell_predictit = True  # Sell on PredictIt
else:
    buy_predictit = True  # Buy from PredictIt
    sell_kalshi = True  # Sell on Kalshi

In this case, you would buy the contract at Kalshi for $0.45 and simultaneously sell it on Polymarket for $0.55. This would lock in a guaranteed profit of $0.10 per contract, assuming you are able to execute the trades quickly enough to avoid price movement.

Example 2: Economic Indicators

Let’s take a more macroeconomic approach. Imagine you’re assessing a new economic indicator’s expected release, and you find the following trades:

  • Kalshi: Odds at $0.60 for a job growth above expectations.
  • Polymarket: Odds at $0.70 for the same outcome.
  • PredictIt: Odds at $0.50 for the same outcome.

This situation presents another arbitrage opportunity.

# Pseudo-code for a different arbitrage scenario
kalshi_job_growth = 0.60
polymarket_job_growth = 0.70
predictit_job_growth = 0.50

# Execute the arbitrage logic
if kalshi_job_growth < polymarket_job_growth and kalshi_job_growth < predictit_job_growth:
    buy_kalshi_job_growth = True
    sell_polymarket_job_growth = True
elif polymarket_job_growth < kalshi_job_growth and polymarket_job_growth < predictit_job_growth:
    buy_polymarket_job_growth = True
    sell_predictit_job_growth = True
else:
    buy_predictit_job_growth = True
    sell_kalshi_job_growth = True

Here, the strategy would involve buying the contract on PredictIt at $0.50 while simultaneously selling on Kalshi at $0.60, guaranteeing another profit of $0.10 per contract.

Market Structure: Understanding Liquidity and Timing

Cross-exchange arbitrage isn't just about finding discrepancies in prices; it’s essential to understand the market dynamics. Each platform has its liquidity profiles, and this can significantly affect how effectively and quickly you can execute an arbitrage strategy.

Liquidity Considerations

  • Kalshi: Being regulated often means that these markets have deeper liquidity, which translates to smaller bid-ask spreads. However, regulatory constraints can sometimes limit the number of trades.

  • Polymarket: Offers less regulatory oversight, which can lead to greater price fluctuation, but it may also have significantly less liquidity, making it challenging to execute larger trades without substantial slippage.

  • PredictIt: Generally has lower liquidity compared to Kalshi but is still effective for political event contracts. It often has price inefficiencies that can be exploited.

Timing and Execution

The effectiveness of arbitrage trading relies heavily on timing. The differences in contract pricing may only last seconds or minutes, depending on market activity and volume. Utilizing tools for monitoring real-time prices across platforms can be invaluable.

import requests
import time

def monitor_prices():
    while True:
        kalshi_price = get_kalshi_price()
        polymarket_price = get_polymarket_price()
        predictit_price = get_predictit_price()
        
        if kalshi_price < polymarket_price and kalshi_price < predictit_price:
            execute_arbitrage('Kalshi', 'Polymarket', 'PredictIt', kalshi_price, polymarket_price)
        elif polymarket_price < kalshi_price and polymarket_price < predictit_price:
            execute_arbitrage('Polymarket', 'Kalshi', 'PredictIt', polymarket_price, kalshi_price)
        else:
            # Handle PredictIt
            handle_predictit_arbitrage()
        
        time.sleep(1)  # Check every second

![Article illustration](https://sgpqsfrnctisvvexbaxi.supabase.co/storage/v1/object/public/blog-images/cross-exchange-arbitrage-kalshi-polymarket-predictit-body.png)

def get_kalshi_price():
    # Fetch the price from Kalshi API endpoint
    return requests.get('KALSHI_API_ENDPOINT').json()['price']

def get_polymarket_price():
    # Fetch the price from Polymarket API endpoint
    return requests.get('POLYMARKET_API_ENDPOINT').json()['price']

def get_predictit_price():
    # Fetch the price from PredictIt API endpoint
    return requests.get('PREDICTIT_API_ENDPOINT').json()['price']

# This is a simplified example; ensure robust error handling and authentication handling are implemented.

This script showcases a basic methodology for monitoring prices. An efficient arbitrage bot will likely require more advanced features such as error handling, rate limiting, and API connection management.

Risks in Cross-Exchange Arbitrage

Arbitrage strategies are not without risks. Some primary considerations include:

  1. Execution Risks: Slippage can reduce profits if prices change between executing buy and sell orders.
  2. Regulatory Risks: Especially with platforms like Kalshi, ensuring compliance with trade limits and regulations is crucial.
  3. Market Risks: Rapid market changes can result in losses rather than profits if the market moves unfavorably.

Conclusion

Cross-exchange arbitrage in prediction markets like Kalshi, Polymarket, and PredictIt provides traders with a systematic approach to utilize price discrepancies for profit. By leveraging Python scripts for monitoring prices, understanding market structures, and recognizing potential risks, traders can approach these opportunities with greater confidence. The balance between execution speed and market analysis is key for successfully capitalizing on these arbitrage opportunities. As always, thorough testing and an understanding of each platform's idiosyncrasies will enhance the chances of success in this competitive field.