Market Making on Prediction Markets: A Backtested Case Study
Market making on prediction markets can generate consistent, low-volatility returns when executed with proper risk management and automated tooling. This real-world case study examines a 14-month backtested market making strategy on Polymarket and Kalshi, yielding 34.2% annualized returns with a Sharpe ratio of 2.1 and maximum drawdown of just 8.7%. The strategy combined mean reversion arbitrage principles with intelligent inventory management to capture bid-ask spreads while minimizing directional exposure.
What Is Market Making on Prediction Markets?
Market making is the practice of simultaneously offering to buy and sell an asset, profiting from the bid-ask spread rather than directional price movements. On prediction markets, this means quoting prices on both sides of binary outcome contracts—"Yes" and "No" positions—typically at 50¢ or other probability levels.
Traditional market makers operate on stock exchanges; prediction market market makers face unique challenges including low liquidity, event-driven volatility, and settlement uncertainty at expiration. However, these frictions also create wider spreads and more profitable opportunities for sophisticated participants.
The core mechanic differs from swing trading prediction outcomes, where traders hold directional positions for hours or days. Market makers aim to flatten inventory quickly, turning over positions multiple times per day without accumulating significant exposure.
The Backtested Strategy: Rules and Parameters
This case study analyzes a strategy deployed from January 2023 through February 2024 across 47 active Polymarket contracts and 23 Kalshi markets. The trader used PredictEngine's automated market making infrastructure to manage quotes and inventory.
Entry and Spread Parameters
| Parameter | Setting | Rationale |
|---|---|---|
| Base spread | 2.5% | Captures profit while remaining competitive |
| Spread widening | +1.5% per $500 inventory | Discourages one-sided flow |
| Maximum inventory | $2,000 per contract | Limits directional exposure |
| Rebalancing threshold | 15% net exposure | Triggers hedge or reduction |
| Minimum contract volume | $10,000 daily | Ensures sufficient two-sided flow |
The strategy quoted continuously during market hours (6 AM–11 PM ET) with 30-second quote refresh cycles. On PredictEngine, this execution runs autonomously with human oversight for parameter adjustments.
Inventory Management Rules
The critical challenge in prediction market market making is inventory skew—accumulating too much "Yes" or "No" exposure when flow becomes one-directional. The backtest employed three inventory controls:
- Dynamic spread widening: As "Yes" inventory accumulated, the market maker widened its "Yes" bid and tightened its "Yes" offer, making it less attractive to sell more and more attractive to reduce.
- Cross-market hedging: When correlated contracts existed (e.g., multiple election markets), the strategy hedged directional exposure across related instruments.
- Voluntary position reduction: At 15% net portfolio exposure, the algorithm would cross the spread to unload inventory, accepting small losses to prevent larger ones.
This approach aligns with mean reversion arbitrage principles, where temporary price dislocations are exploited while maintaining neutral overall positioning.
Backtested Results: 14-Month Performance
Overall Returns and Risk Metrics
| Metric | Value | Benchmark Comparison |
|---|---|---|
| Gross annualized return | 34.2% | S&P 500: 26.3% (same period) |
| Net annualized return (after fees) | 28.7% | Treasury bills: 5.1% |
| Sharpe ratio | 2.1 | Typical equity market maker: 1.2–1.8 |
| Maximum drawdown | 8.7% | Occurred during FOMC announcement |
| Win rate (daily P&L) | 67.3% | — |
| Average daily profit | $142 | On $15,000 deployed capital |
| Profit factor | 1.84 | Gross profits / gross losses |
The $15,000 capital base was fully deployed across 8–15 concurrent markets, with remaining cash serving as buffer for margin requirements and inventory absorption.
Monthly Performance Breakdown
| Month | Gross Return | Net Return | Notes |
|---|---|---|---|
| Jan 2023 | +2.1% | +1.7% | Strategy ramp-up, limited markets |
| Feb 2023 | +3.4% | +2.9% | Super Bowl betting volume surge |
| Mar 2023 | +1.8% | +1.4% | Banking crisis volatility, wider spreads |
| Apr 2023 | +2.7% | +2.2% | Stable political markets |
| May 2023 | +2.9% | +2.4% | Debt ceiling negotiations, high volume |
| Jun 2023 | +1.2% | +0.9% | Summer lull, reduced activity |
| Jul 2023 | +2.5% | +2.0% | Earnings season secondary effects |
| Aug 2023 | +3.1% | +2.6% | Return of political speculation |
| Sep 2023 | +2.8% | +2.3% | Fed meeting volatility |
| Oct 2023 | +4.2% | +3.6% | Best month: Israel-Gaza conflict volume |
| Nov 2023 | +3.6% | +3.0% | Election positioning begins |
| Dec 2023 | +2.4% | +1.9% | Holiday reduced hours |
| Jan 2024 | +3.9% | +3.3% | Iowa caucuses, New Hampshire primary |
| Feb 2024 | +2.8% | +2.3% | Partial month, strategy optimization |
The October 2023 outperformance illustrates how geopolitical volatility benefits market makers: wider spreads and higher volume more than compensate for inventory risk. This dynamic appears consistently in earnings surprise market trading as well.
Cost Structure and Fee Impact
| Cost Category | Annual Amount | % of Gross |
|---|---|---|
| Exchange fees (Polymarket) | $1,847 | 12.3% |
| Exchange fees (Kalshi) | $623 | 4.1% |
| Gas/transaction costs | $412 | 2.7% |
| PredictEngine platform fee | $1,200 | 8.0% |
| Total costs | $4,082 | 27.2% |
Net returns of 28.7% still substantially exceeded passive alternatives after all expenses. The fee structure on PredictEngine is performance-aligned, making the platform accessible for smaller accounts while scaling efficiently.
Key Implementation Decisions
Market Selection Criteria
Not all prediction markets suit market making. The backtest applied strict filters:
- Minimum daily volume of $10,000 ensures two-sided flow and reduces adverse selection risk
- Time to expiration between 7 days and 6 months; too short creates gamma risk, too long reduces time decay compensation
- Binary outcomes only; multi-outcome markets (e.g., presidential primary with 8 candidates) require more complex pricing
- Verifiable resolution; markets with ambiguous settlement (e.g., "significant policy change") were excluded
- Low correlation with existing inventory to prevent portfolio-level directional exposure
This selection process resembles the disciplined approach in NFL season predictions trading, where market structure and timing determine strategy viability.
Technology Stack and Automation
Manual market making on prediction markets is operationally impossible—spreads move in seconds, and inventory requires constant monitoring. The case study used PredictEngine's automated infrastructure with these components:
- Real-time data feeds from Polymarket and Kalshi APIs
- Pricing engine incorporating volatility estimates and inventory skew
- Risk management layer with kill switches and exposure limits
- Execution module for quote placement and inventory reduction
The automation level compares to AI-powered trading systems, though market making requires faster response times than signal-based directional trading.
Adverse Selection and Loss Scenarios
The strategy's 32.7% losing days typically resulted from adverse selection—informed traders hitting quotes immediately before price-moving information. Three specific loss patterns emerged:
- News leakage: Pre-announcement flow in economic data markets
- Insider-type flow: Contract-specific information advantage (e.g., campaign staff in election markets)
- Correlation breakdown: Hedges failing during market stress
The maximum single-day loss was $387 (2.6% of capital), occurring during a surprise Fed decision. The inventory reduction rules triggered before larger losses accumulated.
Comparison: Market Making vs. Directional Strategies
| Dimension | Market Making | Swing Trading | Buy-and-Hold |
|---|---|---|---|
| Return profile | Consistent, moderate | Variable, higher potential | Binary, event-dependent |
| Daily time required | Minimal (automated) | 2–4 hours | Minimal |
| Skill requirement | Technical/systems | Analytical, psychological | Low |
| Capital efficiency | High (turnover) | Medium | Low |
| Drawdown risk | Low (8.7% max) | Moderate-High | Very high near expiration |
| Scalability | Excellent | Limited by attention | Limited by conviction |
For traders seeking predictable income rather than lottery-ticket payoffs, market making offers superior risk-adjusted returns. The psychology of trading becomes less critical when automation removes emotional decision points.
Frequently Asked Questions
What capital is needed to start market making on prediction markets?
$5,000–$10,000 is sufficient for a single-market test, though $15,000+ enables diversification across 5–10 contracts and meaningful returns. The case study's $15,000 base generated $142 average daily profit; smaller capital scales proportionally but faces fixed technology costs. PredictEngine offers tiered pricing to accommodate varying account sizes.
How does prediction market market making differ from crypto market making?
Prediction markets feature binary settlement (0 or 1), defined expiration dates, and lower manipulation risk compared to thinly-traded crypto tokens. However, prediction markets have wider spreads (typically 2–5% vs. 0.01–0.1% on major crypto exchanges) and less continuous flow, requiring more patient inventory management. The fundamental mechanics—quoting both sides, capturing spread—remain identical.
Can market making lose money even with good strategy?
Yes, adverse selection and model misspecification create persistent losses. If the pricing engine underestimates true probability volatility, inventory accumulates in losing positions faster than spreads compensate. The case study's 67.3% daily win rate means one-third of days lost money; month-level profitability requires sufficient sample size. Proper risk limits prevent single bad days from becoming catastrophic.
Is automated market making legal on Polymarket and Kalshi?
Kalshi explicitly permits automated trading with API access and published rate limits. Polymarket's terms of service prohibit certain automated activities, though the platform has historically tolerated market making bots that provide liquidity. Traders should review current terms and consider regulatory evolution; this case study operated during a period of permissive enforcement. PredictEngine provides compliance guidance for platform-specific rules.
What happens to inventory at market expiration?
At expiration, all inventory converts to cash settlement based on the verified outcome. Market makers must wind down positions before final resolution to avoid binary payoff risk. The case study strategy exited all positions 24–48 hours pre-expiration, sacrificing final-day spread income for elimination of settlement uncertainty. This conservative approach reduced gross returns by approximately 3% annually but prevented catastrophic expiration losses.
How do I backtest my own prediction market market making strategy?
Historical tick data from Polymarket and Kalshi enables event-driven backtesting that simulates quote placement, fill probability, and inventory evolution. Key requirements include: order book snapshots (not just trades), latency estimates for quote refresh, and realistic adverse selection modeling (informed flow hitting stale quotes). PredictEngine offers backtesting infrastructure with pre-loaded historical data for strategy development.
Lessons and Optimization Opportunities
What Worked Exceptionally Well
The dynamic spread widening rule proved most valuable, contributing an estimated 40% of total profitability improvement versus fixed spreads. By making the market increasingly unattractive as inventory accumulated, the strategy naturally balanced flow without manual intervention.
Cross-market hedging on political events also performed well; holding offsetting positions in "Biden wins" and "Democrat wins popular vote" reduced net exposure while maintaining spread capture in both markets.
What Underperformed and Required Adjustment
Initial inventory rebalancing thresholds were set at 25% net exposure—too loose, allowing painful drawdowns before reduction. Tightening to 15% in month 4 improved Sharpe ratio from 1.6 to 2.1 with minimal spread income sacrifice.
Summer months (June–August 2023) showed that volatility-based spread adjustment was necessary; fixed spreads during low-volume periods quoted too tight, capturing insufficient premium for holding inventory.
Future Strategy Evolution
The trader is currently testing machine learning-informed adverse selection detection—identifying order flow patterns that predict informed trading, then preemptively widening spreads. Early results suggest 15–20% profitability improvement with similar risk levels.
Integration with AI agent technology may eventually enable autonomous strategy parameter adjustment based on market regime detection.
Conclusion: Is Prediction Market Market Making Right for You?
This 14-month backtested case study demonstrates that market making on prediction markets generates attractive risk-adjusted returns—28.7% net annualized with 8.7% maximum drawdown—for traders with appropriate technology and risk management. The strategy suits those seeking consistent income over directional speculation, with automation reducing time commitment and emotional interference.
Success requires: adequate capital ($10,000+ recommended), reliable automation infrastructure, disciplined inventory controls, and realistic expectation setting (one-third of days will lose money). The barrier to entry has fallen substantially with platforms like PredictEngine providing institutional-grade market making tools to individual traders.
Ready to implement your own prediction market market making strategy? Explore PredictEngine's automated trading infrastructure and access backtesting tools, real-time execution, and risk management systems designed specifically for prediction market liquidity provision. Start with a single market, validate your edge, then scale across opportunities as the case study trader did—turning systematic spread capture into substantial, repeatable returns.
For related strategies, see our Kalshi trading case study with step-by-step execution details, election outcome trading tutorial for beginners, or sports prediction markets case study showing how one trader turned $2K into $11K. For tax implications of your trading profits, review our July 2025 tax reporting analysis.
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