Simulate potential returns by following whale consensus signals at different tiers
Run Monte Carlo simulations based on historical signal accuracy by tier. Understand expected value, optimal position sizing with the Kelly criterion, and projected P&L before risking real capital.
This is a Monte Carlo simulation based on historical signal accuracy. Past performance does not guarantee future results.
Set your parameters and hit Simulate to see projected returns.
Smart position sizing is the difference between profitable trading and blowing up your bankroll. Here are the key concepts behind the calculator.
Expected value measures the average profit per dollar bet over many trades. A positive EV means the bet is profitable long-term. Our Tier A signals have a historically positive EV because the 78% accuracy at average entry prices of 62c yields more wins than losses after fees.
The Kelly criterion calculates the mathematically optimal bet size to maximize long-term growth while minimizing risk of ruin. It balances between betting too much (high variance) and too little (leaving money on the table). Most practitioners use half-Kelly or quarter-Kelly for extra safety.
Never risk more than you can afford to lose on a single trade. A common rule is 1-5% of your bankroll per position. Higher tier signals (A and B) may warrant larger sizes due to higher accuracy, while lower tiers should use smaller allocations. The calculator shows Kelly-optimal sizing for each tier.
Polymarket charges approximately 2% on net profits from winning trades. This fee is deducted before payout and is already factored into all calculator results. No fee is charged on losing trades. Always factor fees into your expected returns to avoid overestimating profitability.
Our 4-tier system (A through D) reflects confidence level based on expert count, average score, and consensus strength. Tier A signals come from the highest-scoring experts with the strongest agreement. Higher tiers have higher accuracy but fewer signals per week.
This calculator uses Monte Carlo simulation to model outcomes. Each run generates random trade results based on historical accuracy rates. Run multiple simulations to see the range of possible outcomes. The variance between runs shows real-world uncertainty even with a positive expected value edge.
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