Front-Running

#Definition

Front-running is the practice of executing trades based on advance knowledge of pending orders that will move the market. In prediction markets, front-runners exploit information about incoming transactions to trade ahead of other participants, profiting at their expense.

On blockchain-based platforms like Polymarket, front-running takes a specific form: bots monitor the public mempool (pending transactions) and insert their own trades before large orders execute, capturing value that would otherwise go to the original trader.

#The Scale of the Problem

MEV extraction represents a massive, often invisible tax on crypto traders:

Key trend: Private routing (transactions avoiding public mempools) grew from 31.8% to 50.1% of all transactions between November 2024 and February 2025—showing traders increasingly seek protection.

Polymarket-specific: Polymarket's hybrid model (off-chain matching, on-chain settlement) significantly reduces traditional mempool-based MEV attacks compared to pure AMM DEXs.

#Why It Matters in Prediction Markets

Front-running directly impacts trading costs and market fairness. Understanding this practice helps traders:

Recognize hidden costs: The price you see when submitting an order may not be the price you receive. Front-runners can move the market against you between submission and execution.

Explain unexpected slippage: When trades execute at worse prices than expected despite adequate depth of market, front-running may be the cause.

Protect capital: Large orders are particularly vulnerable. Without protective measures, significant positions can lose substantial value to front-runners before even being established.

Understand blockchain mechanics: On decentralized prediction markets, front-running connects to broader concepts like MEV (maximal extractable value) that affect all DeFi participants.

Front-running is generally considered unethical and is illegal in traditional securities markets. In crypto prediction markets, it exists in a regulatory gray area but remains adversarial to regular traders.

#How It Works

#Traditional Front-Running

In traditional markets, front-running typically involves brokers or insiders:

1. A broker receives a large client order to buy
2. Before executing the client's order, the broker buys for their own account
3. The client's large order moves the price up
4. The broker sells their position at the higher price
5. The client receives a worse execution; the broker profits

This is illegal in regulated markets and violates fiduciary duties.

#Blockchain Front-Running (MEV)

On blockchain prediction markets, front-running operates differently:

1. Transaction visibility: When you submit a trade on Polymarket, it enters the public mempool before being included in a block
2. Bot monitoring: Automated bots continuously scan the mempool for profitable opportunities
3. Gas price manipulation: Bots submit competing transactions with higher gas fees to ensure their trades execute first
4. Sandwich attacks: The most common form—bots place orders both before AND after your transaction

Sandwich Attack Visualization

#Python: Estimating MEV Cost

Calculate how much "slippage tolerance" actually costs you if exploited.

def estimate_mev_risk(trade_size, slippage_tolerance):
    """
    Estimates potential loss if a bot exploits your full slippage tolerance.
    """
    max_price_impact = trade_size * slippage_tolerance
    
    print(f"Trade Size: ${trade_size}")
    print(f"Slippage Tolerance: {slippage_tolerance*100}%")
    print(f"Potential MEV Extraction: ${max_price_impact:.2f}")
    
    if max_price_impact > 20: # Arbitrary gas threshold
        print("⚠️ Warning: Profitable target for sandwich bots.")
    else:
        print("✅ Likely safe (unprofitable for bots).")

# Example: $5000 trade with 2% slippage
estimate_mev_risk(5000, 0.02)

#Quantifying the Impact

Front-Running Cost = (Execution Price - Expected Price) × Position Size

Example:

• Expected price: $0.55
• Actual execution: $0.57
• Position size: 5,000 shares
• Front-running cost: $0.02 × 5,000 =$100

This cost is invisible in standard fee calculations but directly reduces trading returns.

#Where Front-Running Occurs

#High-Risk Environments

• Public mempools: Any blockchain transaction visible before confirmation
• Large market orders: Size attracts front-runner attention
• Illiquid markets: Less depth means easier price manipulation
• Volatile periods: Fast-moving markets during news events
• Predictable trading patterns: Regular rebalancing or systematic strategies

#Lower-Risk Environments

• Polymarket's Hybrid Model: Polymarket matches orders off-chain (preventing mempool monitoring) and only settles on-chain. This structural design eliminates classic mempool-based sandwich attacks, though other forms of arbitrage still exist.
• Private transaction submission: Some platforms offer MEV protection
• Limit orders: Less vulnerable than market orders (though still targetable)
• High-liquidity markets: Harder to move prices profitably
• Regulated centralized platforms: Kalshi and similar platforms have internal controls
• Small orders: May not be profitable targets after gas costs

#Platform Differences

#Examples

Election market sandwich: A trader submits a $20,000 market buy on a presidential election outcome. A MEV bot detects the transaction, buys$5,000 worth of shares first, lets the large order push the price up 3%, then sells into the elevated price. The original trader pays an effective 2% premium; the bot captures most of that value.

News-driven front-running: Breaking news hits about a political candidate. A trader quickly submits a buy order. Bots with faster infrastructure detect both the news and the pending order, executing purchases milliseconds before the trader's transaction confirms. By the time the original order executes, prices have already moved.

Liquidity withdrawal front-running: A market maker observes a large pending order and withdraws their liquidity before it executes. The order now faces worse prices due to reduced depth of market. This isn't classic front-running but achieves similar outcomes.

Cross-platform front-running: A trader places a large order on one platform. Observers (bots or humans) see the order and immediately trade on related markets on other platforms, capturing the information value before it's reflected in all venues.

#Risks and Protection Failures

Slippage tolerance exploitation: Setting high slippage tolerance to ensure execution makes you a more profitable target. Bots will extract up to your full tolerance.

Failed MEV protection: Some MEV protection services have limited coverage or fail during high-congestion periods. Don't assume protection is absolute.

Information leakage: Discussing intended trades publicly (social media, Discord) enables manual front-running even without mempool monitoring.

Repeated patterns: Trading at the same times or using predictable sizing makes you easier to target. Sophisticated front-runners profile regular traders.

False security from limit orders: While limit orders help, they can still be targeted through sophisticated strategies that manipulate prices to your limit then reverse.

Underestimating costs: Front-running costs are invisible compared to explicit fees. Traders may not realize they're losing significant value to this practice.

#Practical Tips for Protection

• Use limit orders instead of market orders whenever possible; specify the maximum price you're willing to pay rather than accepting whatever the market offers

• Set tight slippage tolerances on AMM trades; accept failed transactions over guaranteed extraction by bots

• Consider MEV protection services like Flashbots Protect or platform-specific private transaction submission when available

• Break large orders into smaller pieces executed over time; smaller orders are less profitable targets

• Avoid trading during extreme congestion when gas prices spike and MEV extraction is most aggressive

• Use centralized platforms for large positions if front-running protection is a priority; Kalshi and similar regulated platforms have internal matching that prevents mempool-based attacks

• Don't broadcast trading intentions on social media or public forums before executing

• Monitor execution quality over time; if you consistently receive worse prices than expected, front-running may be a factor

#Related Terms

• Slippage
• Order Book
• Depth of Market
• Liquidity
• Market Order
• Limit Order
• Automated Market Maker (AMM)
• Counterparty Risk

#FAQ

#What is front-running in simple terms?

Front-running is when someone sees your pending trade and jumps ahead of you to profit from the price movement your trade will cause. It's like someone cutting in line after seeing what you're about to buy, purchasing it first, and selling it back to you at a higher price.

#Is front-running illegal in prediction markets?

In traditional securities markets, front-running by brokers is illegal. In crypto-based prediction markets, the legal status is unclear—it's not explicitly regulated, but it's widely considered unethical. The blockchain makes transactions visible by design, creating opportunities that don't exist in traditional markets. Centralized prediction markets like Kalshi have internal controls that prevent this type of exploitation.

#How much can front-running cost me?

Costs vary based on order size, market liquidity, and current MEV activity. Small orders in liquid markets may lose fractions of a percent. Large orders in thin markets during volatile periods can lose 2-5% or more to front-runners. Over many trades, these costs compound significantly and can eliminate trading edge entirely.

#How do I know if I'm being front-run?

Signs include: consistently receiving worse prices than quoted when you submit orders, unusual price spikes immediately before your orders execute that reverse afterward, and execution prices at or near your slippage tolerance on most trades. Comparing expected versus actual execution over many trades reveals patterns.

#Can limit orders prevent front-running?

Limit orders reduce but don't eliminate front-running risk. They prevent sandwich attacks from extracting unlimited value (since you won't execute above your limit), but sophisticated attackers can still manipulate prices to your limit price and profit from the difference. Limit orders are still far safer than market orders in adversarial environments.