Correlation Analysis between Real Currency Fluctuations and Virtual Currency of an MMORPG

This repository contains code about a scientific initiation from National Telecommunications Institute located in Santa Rita do Sapucaí, Brazil titled "Correlation Analysis between Real Currency Fluctuations and Virtual Currency of an MMORPG". You can download the full documentation here.

Abstract

This work proposes the collection of data from the MMORPG World of Warcraft’s virtual currency and real-world exchange rates variations in order to perform a correlation analysis, to identify similarities and differences between these contexts and to point out possible explanations for such relationships.

Example

This Graphics shows us the correlation between two datasets, the China's real currency in red(Renminbi - Cny) and the World of Warcraft coin, WoW Token in blue.

Comparing and correlating each real currency with each respective realm, we got the follow correlation table. Where the columns Pearson, Kendall and Spearman show the method used for correlation.

Region	Currency	Pearson	Kendall	Spearman
Américas	BRL	0,81	0,47	0,65
Europa	Eur	0,55	0,28	0,46
China	Cny	0,74	0,36	0,56
Coreia	Krw	-0,10	-0,05	-0,09

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

First of all, we are using the Python 3.7.4. You can download this repository as a zip, and run the following command in to terminal to install the dependecies:

pip install -r requirements.txt

Running

You can run the whole project with the main.py, for a while it isn't plotting any graphics.

python main.py

Directory Structure

This project is comprised of the following directories:

• code: Contains all codes used for correlate and plot;
• files: Contains pictures and documentations archives.

Auxiliar tools

• gdrive: Upload automatically the database to my google drive.

Libraries

The following libraries were used in the project:

• Beautifulsoup4: Used for webscrapping;
• Matplotlib: Plotting 2D graphics;
• Numpy: NumPy is the fundamental package for scientific computing with Python;
• Pandas: Dataframes manipulation;
• Requests: Send organic, grass-fed HTTP/1.1 requests, without the need for manual labor;
• SciPy: Python-based ecosystem of open-source software for mathematics, science, and engineering;
• Statsmodels: Module that provides classes and functions for the estimation of many different statistical models.

Authors

Name	Role	Github
Rubens Cividati	Author	Github
Marcelo V. C. Aragão	Advisor	Github
Isabella Capistrano	Contributor	-

Presentation

Incitel XXXII 2020