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