Strategy Methodology

Monte Carlo Simulation

A computational method that runs thousands of randomized scenarios to estimate the probability distribution of possible outcomes.

Monte Carlo simulation answers the question: given what we know about the inputs, what is the range of possible outcomes? Instead of calculating a single expected value, it runs thousands of trials with randomized inputs drawn from observed distributions, then maps the full range of results.

The botwir3 Monte Carlo module uses simulation for position sizing and risk estimation. Given a proposed trade, the module generates outcome distributions based on historical volatility and the configured parameters, then evaluates whether the proposed size keeps the portfolio within the tolerance band across a specified confidence interval.

Monte Carlo does not predict the future. It maps what is plausible given the inputs. If the inputs are wrong — volatility is understated, correlations shift, regime changes — the simulation output is wrong. The module is a sizing tool, not an oracle. The user configures the number of trials, confidence level, and input distributions.

Sources

Metropolis, N. & Ulam, S. (1949). “The Monte Carlo Method.” Journal of the American Statistical Association, 44(247), 335–341.The original paper introducing the Monte Carlo method at Los Alamos.
Glasserman, P. (2003). “Monte Carlo Methods in Financial Engineering.” Springer, Applications of Mathematics Vol. 53.The standard reference for Monte Carlo applications in finance.

Used in

◎Monte Carlo Simulation module — builder

See this in action

→ polymarket→ kalshi→ betfair→ coinbase

Related

→ Position Sizing→ Kelly Criterion→ Drawdown→ Volatility
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