Gambler's Ruin Quant Interview Guide
Gambler's ruin quant interview guide for random walks, absorbing boundaries, win probabilities, expected time, biased walks, and mistakes.
Candidates practicing random walks, absorbing states, and hitting probabilities.
Gambler’s ruin is a bounded random walk
Gambler’s ruin asks whether a random walk hits a target before hitting zero or another lower boundary. It is a classic absorbing-state setup.
Boundary conditions come first
Set the success probability at the target to one and at ruin to zero. Then write recursion for intermediate wealth levels.
Concrete example
A player starting with 3 units and target 10 has a probability of reaching 10 before 0 that depends on whether each step is fair or biased.
Biased walks change intuition
A small edge in step probability can materially change hitting probabilities and expected duration. State whether the walk is fair before solving.
Common mistakes
Candidates often forget the lower boundary or assume fair steps. A reliable answer defines start, target, ruin state, and step probabilities.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.