The Quant Journey
Published in

The Quant Journey

Monte Carlo Simulation Theory and Applications in Python

Histogram of Monte Carlo Simulation Results

The Monte Carlo Simulation is a numerical analysis technique aimed at estimating the possible outcomes of a certain random event. It is a very powerful method of evaluating integrals where there are no known solutions.

The main idea behind this simulation is that the results are computed based on repeated random sampling and statistical analysis. The technique relies…




This is a repository of information regarding everything quantitative. I am building my knowledge as I go, therefore this is a journey for both me as a contributor and you as a reader as we venture in to the world of mathematics, programming, statistics, finance and business.

Recommended from Medium

Top 10 Myths about Data Science Career

Baking Bread with Streamlit —

KMeans Clustering on RFM-T Segmentation with Python for Online Retail Data

Changing Winds in Data Analytics, Data Engineering & Machine Learning

Why Algorithms Price Differently from Humans

Pakakumi Ultimate Review — 2022

Linear Regression with All basics

Analysis on Departures for Foreign Employment with Real Wages and Paddy Harvest in Sri Lanka

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Andrea Chello

Andrea Chello

Quant | Full-Stack Blockchain Developer

More from Medium

Monte Carlo Simulation for Black-Scholes Option Pricing

Predicting Stock Prices Using Monte Carlo Methods in Python

The History of Quantitative Finance.

How to synchronize time series using cross-correlation in Python