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Random Walk Price Simulator

Price Dynamics & Uncertainty

Overview

This simulator models a discrete-time random walk for an asset price. Each step applies a drift, producing many possible price paths that illustrate how uncertainty compounds over time. By simulating hundreds of trajectories, the model visualises the probabilistic spread inherent in stochastic systems.

Python Monte Carlo Stochastic Modelling
Random Walk Example

Snapshot

Process Random Walk

Each step adds a drift and range of volatility.

Paths Hundreds

Inputed from user, can alter amount paths and total time in days, along with volatility.

Random Walk Simulation Frame

Representative simulation frame.

Methodology

1. Initialise

Define number of paths, steps, starting value and volatility.

2. Generate Shocks

Apply Gaussian noise at each timestep.

3. Analyse Spread

Evaluate the distribution and behaviour of resulting price paths.