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In this work, we adapt a Monte Carlo algorithm introduced by Broadie andGlasserman (1997) to price a $\pi$-option. This method is based on thesimulated price tree that comes from discretization and replication of possibletrajectories of the underlying asset's price. As a result this algorithmproduces the lower and the upper bounds that converge to the true price withthe increasing depth of the tree. Under specific parametrization, this$\pi$-option is related to relative maximum drawdown and can be used in thereal-market environment to protect a portfolio against volatile and unexpectedprice drops. We also provide some numerical analysis.

Note on simulation pricing of $\pi$-options