Book Image

Hands-On Simulation Modeling with Python

By : Giuseppe Ciaburro
Book Image

Hands-On Simulation Modeling with Python

By: Giuseppe Ciaburro

Overview of this book

Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.
Table of Contents (16 chapters)
Section 1: Getting Started with Numerical Simulation
Section 2: Simulation Modeling Algorithms and Techniques
Section 3: Real-World Applications


In this chapter, we applied the concepts of simulation based on Monte Carlo methods and, more generally, on the generation of random numbers to real cases related to the world of financial engineering. We started by defining the model based on Brownian motion, which describes the uninterrupted and irregular movement of small particles when immersed in a fluid. We learned how to describe the mathematical model, and then we derived a practical application that simulates a random walk as a Wiener process.

Afterward, we dealt with another practical case of considerable interest, that is, how to use Monte Carlo methods to predict the stock prices of the famous Amazon company. We started to explore the trend of Amazon sharess in the last 10 years, and we performed simple statistics to extract preliminary information on any trends that we confirmed through visual analysis. Subsequently, we learned to treat the trend of stock prices as a time series, calculating the daily return...