Prevayo Blog

Dynamic position sizing is the single most controllable variable in your prediction market edge. This complete guide walks through the exact frameworks — from fixed fractional to volatility-adjusted sizing — that separate consistently profitable traders from those who blow up on a single bad month.

Risk management is the single skill that separates profitable prediction market traders from those who blow up their bankroll. This complete guide covers every framework you need — from bankroll allocation rules to stop-loss logic — with real numbers and step-by-step execution.

Kalshi is the most regulated prediction market in the U.S., but knowing how to navigate it strategically separates profitable traders from gamblers. This guide covers everything from category selection and timing windows to position sizing and portfolio construction — with real numbers and actionable frameworks.

Mean reversion is one of the most statistically reliable edges in prediction markets — and one of the most misunderstood. This complete guide walks you through the math, the market mechanics, and a repeatable step-by-step framework for identifying and trading overreacted markets in 2026.

The Kelly Criterion is the mathematically optimal formula for sizing positions in prediction markets — and most traders either ignore it or apply it wrong. This complete guide walks you from the basic formula through advanced fractional Kelly techniques, with real worked examples on Kalshi and Polymarket.

Arbitrage in prediction markets is one of the few genuinely low-risk profit strategies available to retail traders — but finding and executing these opportunities requires more precision than most guides admit. This complete guide walks through every type of prediction market arbitrage, with real examples, execution frameworks, and the risk factors that kill most attempts before they start.

Cross-market arbitrage in prediction markets lets you lock in near-guaranteed profits when the same event is priced differently across platforms. This complete guide walks you through how to find mispricings, calculate your edge, and execute trades before the gap closes.

Most prediction market traders focus obsessively on picking winners — but the traders who actually grow their bankroll over time focus on portfolio construction. This complete guide covers how to allocate capital across multiple markets, manage correlation risk, and build a prediction market portfolio that survives losing streaks and capitalizes on edge.

Position sizing is the single biggest lever most prediction market traders never pull correctly. This complete guide breaks down dynamic sizing frameworks — from flat betting to volatility-adjusted scaling — with real numbers and step-by-step execution for Kalshi and Polymarket traders.

Most prediction market traders lose not because they pick wrong outcomes — they lose because they size positions incorrectly and let one bad trade crater their bankroll. This complete guide covers every risk management framework you need, from basic bankroll rules to multi-market portfolio construction.

The Kelly Criterion is the mathematically optimal formula for sizing positions in prediction markets. This guide breaks down the formula f* = (bp − q) / b with real Kalshi examples, explains why professionals use Half Kelly over Full Kelly, and covers the most common sizing mistakes traders make on Polymarket and Kalshi.

Mean reversion is one of the most statistically robust edges available in prediction markets — and most traders completely overlook it. This complete guide walks you through the math, the setup, and the execution, from first principles to advanced applications on Kalshi and Polymarket.

Most prediction market traders update their positions on instinct. Bayesian updating gives you a mathematically rigorous framework to revise probabilities as new evidence arrives — and to size positions accordingly. Here's how to actually implement it, with formulas and real market examples.

Most prediction market traders obsess over entries but ignore exits — and that's where real edge is lost. This guide walks through quantitative take-profit frameworks, including expected value decay curves, trailing exit mechanics, and multi-bracket scaling, so you can lock in gains systematically rather than emotionally.

Statistical arbitrage in prediction markets isn't just about finding price discrepancies — it's about executing them profitably before they close. This guide breaks down the exact formulas, entry triggers, and position-sizing rules you need to capture real edge.

Entry gets the headlines, but exit mechanics determine your actual P&L in prediction markets. This guide breaks down trailing stop and take-profit frameworks with precise thresholds, execution logic, and real market examples — so you stop leaving edge on the table.

Most prediction market traders obsess over win rate. The sophisticated ones optimize for risk-adjusted returns. This guide walks through Sharpe ratio calculation, portfolio-level optimization, and a practical implementation framework you can deploy today across Kalshi and Polymarket.

Advanced Kelly Criterion goes beyond the standard formula by applying fractional multipliers (0.25–0.5×) and solving for correlated multi-market exposure. This guide covers the exact formulas, when to reduce your fraction further, and how to measure whether your sizing is actually working.

Learn how to construct diversified prediction market portfolios using correlation matrices and systematic risk management. This mathematical framework helps traders optimize exposure across multiple markets while managing systematic risk through quantitative position sizing.

Standard financial volatility models fail in prediction markets because prices are bounded between 0 and 1 — creating systematic bias near market extremes. This guide covers Logistic GARCH, Markov regime-switching frameworks, and a practical 5-step implementation architecture for boundary-aware volatility modeling that improves forecast accuracy by 30%+.

Mean reversion trading exploits price overreactions in prediction markets using statistical signals. Learn how to identify oversold opportunities and time entries for consistent edge.

While basic Kelly Criterion works for single bets, sophisticated prediction market traders need dynamic position sizing strategies that account for portfolio correlations, changing market conditions, and multi-outcome scenarios.

Options-style spread trading on Kalshi involves simultaneously buying and selling related contracts to create positions with defined risk-reward profiles. This advanced strategy can generate consistent profits while limiting downside exposure.

Arbitrage opportunities in prediction markets can generate consistent, risk-free profits when executed properly. This guide reveals advanced techniques for identifying and capitalizing on price discrepancies across platforms.

Effective prediction market risk management involves limiting individual trade risk to 2-5% of your bankroll and diversifying across market categories. Most traders fail due to poor risk management, not prediction accuracy.

Advanced Kalshi betting strategies go beyond fixed-stake trading to use confidence-based position sizing, niche market targeting, and strategic entry timing. Experienced traders applying these systematic techniques typically see 15–25% better returns per observed prediction market performance data. This guide covers every pillar of a winning Kalshi strategy system.

Sports prediction markets are delivering consistent profits while other prediction market categories struggle in 2026. Learn why data-driven sports markets offer better opportunities than political and crypto prediction markets.

The Kelly Criterion is a mathematical formula that calculates the optimal fraction of your bankroll to bet on each prediction market contract based on your estimated edge. This guide covers the formula, a step-by-step Kalshi example, fractional Kelly, and the most common mistakes traders make when sizing positions.