Forecasting cryptocurrencies prices using social community historical time-series | Introduction
Tags: cryptocurrency, timeseriesforecasting, datascience, python
Published: 2022-03-17 21:27:34



The following article describes a **Data Science project Introduction**.
It is followed by every step, from **data gathering to final insights over cryptocurrencies price action behavior**, each with a dedicated article.

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## The Problem
**Asset price valuation** 
Predicting an asset's price action behavior has been a tempting goal in investment science for a while, and never an easy one. 

For crypto-assets, **practical intangibility** extrapolates **valuation methods subjectiveness** even further, **encouraging** the need for **new points of view**.


## Metcalfe's law solution approach
Metcalfe's law, **one of these points of view**, is not new at all: conceived in **1983** in a presentation to the 3Com sales force,  **it stated that the value of communication technologies** (specifically the Ethernet) would be proportional to the **total number of possible connections**, or **value=a*n^2** (**a** = value per connection | **n** = users). 

In 1995, impressed by the range of applications for Metcalfe's _'principia'_, the venture capitalist **George Gilder** named it **Metcalfe's Law**. If you'd like to hear the words from the man himself: [click here](https://www.youtube.com/watch?v=pPXCuf7xLsM)
 
The conceptual proof came only in **July 2013**, when Dutch researchers managed to analyze European Internet usage patterns over a long enough time and found **n^2** proportionality for small values of **n**, and **n log(n)** proportionality for large values of **n**, solidifying what is today known as the '**Network Effect**'.

A few months later, **Metcalfe himself** provided further proof, as he used Facebook's data over the past 10 years to show a good fit for Metcalfe's law (the model is n^2).

**What is new** is that only 3 years ago, [Peterson](https://sci-hub.se/10.2139/ssrn.3356098) linked **time-value-of-money concepts to Metcalfe value**, using **Bitcoin** as numerical examples of the proof and showing that over **70% of the variance** in Bitcoin value was **explained by applying Metcalfe's law to increases in Bitcoin network size**.


## Impact 
Peterson's work has **not only contributed to the statement of Metcalfe's law**, but by using Bitcoin's price action to do it, he also **unleashed the concept of mathematical proof for cryptocurrencies pricing models**. 


## Metcalfe's approach limitations
An old critique ([Odlyzko](https://en.wikipedia.org/wiki/Andrew_Odlyzko), [2006](https://spectrum.ieee.org/metcalfes-law-is-wrong)) states that **Metcalfe is wrong** when assuming that the **value of each node (n, user) is of equal benefit**, and that the applicability of his law is **constrained to 'long enough' time-series**, as patterns may average-out utility discrepancy. 

So with this project, we will try to **find a composition of network metrics (a composed user)** that serves as an **indicator of the future performance** of a given crypto asset. 

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## Project objectives
Inspired by the '**Network Effect**' from Metcalfe's Law, this project aims to retrieve insights from time-series analysis over different crypto tokens 'Users'.

We will collect **3 years worth of daily data**, for **50 different tokens**, for the following metrics:

**Social community data**
 'twitter_followers',
 'reddit_subscribers',
 'reddit_average_posts_48h',
 'reddit_avg_comments_48h'

**Developers community data** | cryptocurrency´s github repository
 'forks',
 'stars',
 'github_subs',
 'total_issues',
 'closed_issues',
 'pull_rqst_merged',
 'pull_request_contributors'

**Market_cap, total_volume, current_price**
In USD, EUR and BRL

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By the end, we will be in the position to answer the question:

'Does any of the considered variables (or any conjuncture of them) help to estimate the 'fair-value' and 'future price' of a cryptocurrency?'

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## Analytics Engineering | Technologies 
The workflow will be hosted by **Google Cloud Platform** services. 


**Cloud processing and Storage**
The database will be **stored in Google Storage**, updated through a python script **scheduled with cron jobs**, which runs in a **scheduled preemptible virtual machine** from **Cloud Compute Engine**.


**Database Distribution**
A **Google Cloud Function** will also be triggered everyday to transfer updated data from **Google Storage** to **Google BigQuery**, making it accessible through **DataStudio** and **Tablaeu**.