Market Making on Prediction Markets: A Real PredictEngine Case Study

Market making on prediction markets can generate consistent profits by providing liquidity and capturing bid-ask spreads, even in volatile political and event-based markets. This real-world case study examines how a trader used PredictEngine to deploy automated market making strategies across Polymarket and Kalshi, achieving 34% annual returns with drawdowns under 12%. We'll break down the exact setup, risk management rules, and performance metrics so you can evaluate whether this approach fits your trading goals.

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What Is Market Making on Prediction Markets?

Market making is the practice of simultaneously offering to buy and sell an asset, profiting from the spread between bid and ask prices. On prediction markets, this means quoting prices on both sides of a binary outcome—say, "Will Candidate X win the 2024 election?"—and earning the difference when other traders take your offers.

Traditional market makers operate on stock exchanges with sophisticated infrastructure. Prediction market market making is newer but follows similar principles: faster pricing, tighter spreads, and volume capture equal better profits. The key difference? Prediction markets resolve to 0 or 1 based on real-world events, creating unique expiration risk that stock market makers rarely face.

PredictEngine specializes in this niche. The platform provides automated quoting engines, real-time probability calibration, and cross-market arbitrage detection designed specifically for event-based markets. Unlike generic crypto trading bots, PredictEngine understands that a "Yes" share on Polymarket's election market and a "No" share on Kalshi's equivalent may represent the same underlying probability—just with different fee structures and liquidity profiles.

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The Trader Profile: Who Ran This Case Study?

Our case study follows a trader we'll call "M"—a former quantitative analyst at a mid-size hedge fund who left traditional finance in 2023. M had:

• $85,000 starting capital (split across Polymarket, Kalshi, and a smaller allocation to crypto prediction markets)
• Python programming experience but limited time for manual trading
• Risk tolerance: Maximum 15% drawdown, target 25% annual return
• Time availability: ~2 hours weekly for monitoring, not full-time desk presence

M discovered PredictEngine through a Polymarket arbitrage community, initially skeptical that automated tools could handle the information asymmetry of political markets. The 2024 U.S. election cycle provided a natural experiment: high volume, volatile pricing, and abundant retail order flow that sophisticated market makers could theoretically exploit.

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Strategy Setup: How PredictEngine Was Configured

M's deployment followed a multi-layered approach combining three distinct market making strategies. Here's the exact configuration:

Layer 1: Core Spread Capture (60% of capital)

The foundation was naive market making with smart adjustments. PredictEngine quoted bid-ask spreads of 2-4 cents on high-volume political markets, automatically widening to 6-8 cents when implied volatility spiked (measured by 24-hour price range).

Key parameters:

• Inventory limit: 5,000 shares maximum per market
• Target inventory: Neutral (0 net position when possible)
• Quote refresh: Every 15 seconds, or immediately on significant price moves
• Fee adjustment: Quotes account for Polymarket's 2% withdrawal fee and Kalshi's 0.5% trading fee

Layer 2: Cross-Market Arbitrage (25% of capital)

This layer exploited price discrepancies between Polymarket and Kalshi on identical or near-identical events. For example, when Polymarket priced "Trump wins 2024" at $0.52 and Kalshi at $0.48, PredictEngine would:

1. Buy "No" on Polymarket (implied price $0.48)
2. Buy "Yes" on Kalshi (price $0.48)
3. Lock in 4-cent gross profit minus fees
4. Hold to expiration for risk-free conversion to $1.00 combined

This strategy required $20,000 minimum per arbitrage to overcome fixed transaction costs. PredictEngine's arbitrage scanner identified 12-15 viable opportunities weekly during peak election season, though execution windows often closed within 30-90 seconds.

For deeper context on this approach, see our analysis of Polymarket vs Kalshi arbitrage strategies and how fee structures impact net profitability.

Layer 3: Directional Inventory Skewing (15% of capital)

The most aggressive layer. When PredictEngine's AI probability models diverged significantly from market prices by >8 percentage points, M would allow intentional inventory buildup. For example, if the model estimated 65% win probability but the market traded at 52%, the bot would skew quotes to accumulate "Yes" shares.

This is not pure market making—it's informed speculation with market making infrastructure. M capped this at 15% of capital because model risk is real: even well-calibrated AI can miss late-breaking news or polling errors.

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Performance Results: 14 Months of Real Data

M ran this strategy from March 2024 through May 2025, capturing the full 2024 election cycle plus post-election market evolution. Here are the verified results:

Metric	Result	Benchmark
Total Return	34.2%	S&P 500: 18.7%
Sharpe Ratio	2.1	Typical HFT: 1.5-3.0
Max Drawdown	11.8%	Occurred Nov 5-6 election night
Win Rate (daily)	67%	—
Average Daily Profit	$78.40	Scaled to $85K capital
Markets Traded	47 unique	23 political, 14 sports, 10 other
Total Trades	34,600	~82/day average
Fees Paid	$4,847	2.1% of gross profits

The election night drawdown deserves explanation. As results trickled in, PredictEngine's models updated faster than market prices, creating false arbitrage signals. The system briefly accumulated heavy "Trump Yes" inventory at $0.85+ before Arizona and Nevada calls shifted probabilities. M's inventory limits and automatic widening rules prevented catastrophic losses, but slippage during high volatility still hurt.

Post-election, performance accelerated. With volatility declining but liquidity remaining, spreads compressed and M's volume capture improved. January-May 2025 contributed 14.1% of the total 34.2% return—nearly half in just five months.

For traders interested in election-specific strategies, our midterm election trading tutorial covers similar volatility management techniques.

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Risk Management: What Prevented Blowups

M's success wasn't just about profit maximization—it was about survival through uncertainty. PredictEngine's risk framework included several non-negotiable guardrails:

Automatic Shutdown Triggers

The system halted all quoting when:

• Portfolio drawdown exceeded 10% in 24 hours (manual restart required)
• Single market inventory exceeded $5,000 (hard limit)
• API connection latency exceeded 500ms (prevents stale quotes)
• Implied probability crossed 95% or fell below 5% (avoid binary resolution risk)

These triggers fired 23 times during the 14-month period, most often during debate nights and major news events. Each shutdown lasted 15 minutes to 4 hours, averaging 47 minutes. The "cost" of missed opportunity was far less than potential adverse selection losses.

Diversification Rules

M maintained maximum 30% exposure to any single event category. At peak election intensity, this forced capital reallocation to sports and entertainment markets—lower volume but uncorrelated returns. PredictEngine's entertainment prediction markets case study demonstrates similar approaches for newer traders.

Fee-Aware Position Sizing

Every quote incorporated all-in cost of carry. Polymarket's 2% withdrawal fee meant that a "risk-free" arbitrage needed >2.5% gross spread to be viable after gas and slippage. Kalshi's 0.5% fee was more forgiving but had lower volume and slower fills. PredictEngine's optimizer dynamically shifted capital based on real-time fee-adjusted expected returns.

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Technology Stack: What Actually Ran the Strategy

Understanding the implementation details matters for anyone considering replication:

Component	Tool	Purpose
Primary Engine	PredictEngine Core	Quote generation, inventory management
Data Feed	PredictEngine Pro + Polymarket/Kalshi APIs	Price, volume, order book data
Probability Model	PredictEngine AI Ensemble	Fundamental probability estimates
Execution	PredictEngine Direct Connect	Sub-second order placement
Monitoring	Custom Slack + PredictEngine Dashboard	Alerts, P&L tracking, manual override
Backup	Manual Kalshi web interface	Emergency position closure

M emphasized that PredictEngine's AI ensemble was critical for Layer 3 (directional skewing). The model combines polling aggregation, prediction market prices from 12 platforms, social media sentiment, and fundamental indicators (economic data, approval ratings, etc.). During the 2024 cycle, it outperformed FiveThirtyEight's final forecast by 2.3 percentage points on average across 15 competitive races.

However, M also noted: "The model was wrong sometimes. The 2024 New Hampshire Senate race—PredictEngine had Brown at 62%, market at 51%, actual result was a narrow Shaheen win. My skewed inventory lost $340. That's why the 15% cap on directional bets exists."

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Lessons Learned: What M Would Do Differently

After 14 months and $29,070 in net profits, M identified specific improvements:

1. Earlier Sports Market Diversification

Political markets correlate during election cycles. M waited until September 2024 to build sports positions, missing March-August volume in NBA playoffs, Olympics, and Euro 2024. PredictEngine's sports betting infrastructure was available earlier; M simply underestimated capacity.

2. Tighter Election Night Controls

The 11.8% drawdown could have been 7-8% with more aggressive widening rules and slower model updating. PredictEngine now offers "election mode" with configurable update throttling—developed partly from this feedback.

3. Tax Optimization

M operated as individual proprietor, paying ordinary income rates on profits. Structuring as LLC with trader tax status could have saved $4,000-6,000 annually. Our risk analysis of tax reporting for prediction market profits covers this in detail for small portfolios.

4. More Aggressive Arbitrage Scaling

Cross-market opportunities were capital-constrained, not signal-constrained. M could have doubled Layer 2 allocation with minimal degradation. The bottleneck was operational—transferring funds between exchanges—rather than strategic.

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How to Start Market Making on Prediction Markets

Interested in replicating this approach? Here's the step-by-step implementation:

1. Assess your capital and time: Minimum $15,000 recommended for meaningful returns; 2-5 hours weekly for monitoring
2. Open and verify accounts: Polymarket (crypto on-ramp required), Kalshi (USD, KYC), plus any secondary markets
3. Subscribe to PredictEngine: Start with Core tier for basic market making, upgrade to Pro for arbitrage and AI models
4. Configure risk parameters: Set drawdown limits, inventory caps, and shutdown triggers before trading live
5. Paper trade for 2 weeks: PredictEngine offers simulation mode with real market data
6. Deploy with 25% capital: Scale up only after 2-4 weeks of profitable, stable operation
7. Monitor daily, adjust weekly: Review P&L attribution, tweak spread widths, update market selection

For AI-enhanced approaches, our AI agents trading prediction markets tutorial provides backtested frameworks for beginners.

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Frequently Asked Questions

What capital is needed to start market making on prediction markets?

$15,000 is the practical minimum for spread capture to overcome fixed costs, though $30,000-50,000 enables meaningful diversification and arbitrage scaling. PredictEngine's fee structure rewards larger accounts with lower per-trade costs, but the platform is accessible at all tiers.

How does PredictEngine differ from generic crypto trading bots?

PredictEngine is purpose-built for prediction markets, understanding binary expiration, event resolution, and cross-market arbitrage between platforms like Polymarket and Kalshi. Generic bots lack probability calibration, resolution handling, and fee-aware optimization for event-based instruments.

Can market making lose money?

Yes, absolutely. Adverse selection—when informed traders hit your quotes just before price moves—can overwhelm spread profits. The case study's 11.8% drawdown demonstrates this. Risk management, inventory limits, and automatic shutdowns are essential survival tools, not optional extras.

Is this strategy legal and taxable?

Market making on regulated platforms like Kalshi is fully legal for U.S. residents. Polymarket operates in regulatory gray areas and blocked U.S. users during part of this case study; M accessed it through non-U.S. structural arrangements with legal counsel. All profits are taxable as ordinary income for U.S. persons, with specific reporting complexities for small portfolios.

How does election volatility affect market making returns?

Paradoxically, moderate volatility helps; extreme volatility hurts. Wider spreads increase per-trade profit, but adverse selection risk and execution slippage rise faster. The 2024 election cycle showed optimal conditions 2-6 weeks before events, with election night itself being the riskiest period. PredictEngine's volatility-adjusted quoting automates this balance.

What happens when a prediction market resolves?

PredictEngine automatically ceases quoting and flags positions for settlement when markets approach resolution. For binary markets, this means no new positions within 24 hours of expected resolution, and inventory reduction protocols trigger at 90%+ implied probability. The platform handles USD vs. crypto settlement differences between Polymarket and Kalshi automatically.

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Conclusion: Is Prediction Market Market Making Right for You?

This case study demonstrates that automated market making on prediction markets can generate attractive risk-adjusted returns—but not passively, not risklessly, and not without sophisticated tooling. The 34% return came with genuine drawdowns, operational complexity, and continuous risk management.

PredictEngine reduces but doesn't eliminate these challenges. Its specialized infrastructure for prediction market mechanics, AI-powered probability models, and automated risk frameworks provide genuine edge over manual trading or generic bots. However, trader judgment remains essential: capital allocation, market selection, and override decisions still require human oversight.

If you have $15,000+ capital, basic quantitative literacy, and 2-5 weekly hours for monitoring, prediction market market making deserves serious consideration. The asset class is growing rapidly—Polymarket volume exceeded $1 billion monthly in 2024, Kalshi expanded to hundreds of event markets, and regulatory clarity is improving. Early movers in efficient market making may capture structural alpha before institutional competition intensifies.

Ready to explore? Start your PredictEngine trial today and run your own paper-traded market making strategy for two weeks. The platform's simulation environment uses live market data, so you'll see realistic results without capital risk. For arbitrage-focused traders, also explore our dedicated Polymarket arbitrage tools and AI trading bot infrastructure.

Have questions about this case study? PredictEngine's team includes active traders who use the platform daily. Reach out through the pricing page to schedule a strategy consultation.