Aviator Strategy Backtester

🔬 Monte Carlo Strategy Backtester

Stress-test Martingale, reverse-Martingale & flat-betting strategies against a 1,000,000-round scientific crash dataset. Full client-side execution.





📊 Methodology & Key Metrics

Metric Calculation Method Interpretation
Win Rate Proportion of rounds where crash point exceeds target multiplier Higher target multiplier = lower win probability
Martingale Escalation Bet multiplied by Martingale factor after each loss; resets to base on win Aggressive recovery can exhaust bankroll during extended loss streaks
Bankruptcy Risk Simulation ends when balance drops below current bet requirement Higher base bet relative to balance increases ruin probability
Peak Valuation Highest balance reached during the 1M-round simulation Indicates strategy upside potential and volatility exposure

⚠️ Disclaimer: All simulations are based on historical crash data and mathematical models. Past performance does not guarantee future results. This tool is for educational and research purposes only.

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❓ Frequently Asked Questions

How many crash points are in the dataset?

The dataset contains exactly 1,000,000 verified crash points collected from live game rounds, making it one of the largest publicly available samples for Monte Carlo simulation.

Is the simulation performed on my device or on a server?

Everything runs locally in your browser using JavaScript. The 4.8MB crash dataset is loaded once and cached, and the 1M-round simulation completes within 8-15 milliseconds on modern hardware.

What strategy parameters can I customize?

You can adjust initial balance, base bet amount, target cashout multiplier, and Martingale factor. The simulation then iterates through all 1M rounds to determine whether the strategy survives or goes bankrupt.

Does the backtester account for the house edge?

Yes. The crash dataset reflects real game outcomes, which inherently include the house edge. The simulation does not apply any additional edge reduction.