Ruin Probability Calculator

Overview

This pillar simulates thousands of future trading scenarios to calculate the probability of losing your entire bankroll. It's a critical risk management tool for ensuring long term survival in prediction markets.

What It Does

Using Monte Carlo simulations, this pillar models your future performance based on your historical win rate, average payoff, and proposed bet size. It runs thousands of randomized trading sequences to see how many of them result in total bankroll loss. The result is a clear percentage representing your 'probability of ruin'.

Why It Matters

Gut feelings about risk are unreliable. This pillar provides a concrete, statistical measure of your strategy's viability, helping you avoid overbetting and make data-driven decisions about bet sizing. It shifts the focus from winning a single market to building a sustainable, long term profitable strategy.

How It Works

First, you input your historical trading data: your win rate and average payoff ratio. Next, you define your betting strategy, specifically the percentage of your bankroll you risk on each trade. The pillar then runs a large number of simulations based on these parameters. It outputs the final percentage of simulations that ended in a zero balance.

Methodology

The pillar employs a Monte Carlo simulation. For each run, it generates a sequence of N trades (e.g., N=1000). The outcome of each trade is determined by a random number generator against the user's win rate. A winning trade increases the bankroll by (bet_size * payoff_ratio), while a losing trade decreases it by (bet_size). The final Probability of Ruin is calculated as (Number of simulations where bankroll reached zero) divided by (Total number of simulations).

Edge & Advantage

It replaces emotional, subjective risk assessment with a quantifiable probability. This provides a significant edge by enabling precise, mathematically sound bankroll management that protects against catastrophic losses.

Key Indicators

• Win Rate

  high

  The historical percentage of trades that resolve profitably. A core input for the simulation.

• Payoff Ratio

  high

  The ratio of the average win amount to the average loss amount. Crucial for determining profitability.

• Probability of Ruin (%)

  high

  The primary output; the statistical likelihood of the trading bankroll reaching zero given the inputs.

Data Sources

• User Trading History

  The user must provide their own historical performance data, including win/loss records and trade outcomes.

Example Questions This Pillar Answers

• → What is my risk of ruin if I bet 5% of my bankroll on each market?
• → How does my survival probability change if my win rate drops from 60% to 55%?
• → Is my current bet sizing strategy sustainable for long term growth?

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