Shawn Ng

Bitcoin VS Ethereum Analysis

In this project, I scraped and analysed Bitcoin and Ethereum price history using time series analysis techniques.

Bitcoin vs ethereum
Image Credit: Source

Project Overview

Bitcoin and Ethereum are the top 2 cryptocurrencies (aka cryptos) in terms of market capitalization.

I am interested to see if I can discover any patterns for these 2 cryptos using time series analysis.

I was first exposed to cryptos in 2016, at that time, the price of 1 bitcoin is about $500 - $600.

I don't invest in cryptos then because:

  1. Cryptos are unlikely to go mainstream as they are too technical
  2. With the USA's tight financial regulation, there is a potential risk that it may become illegal

If I could time travel, 1 of the things I would do is

Meme
The Wolf of Wall Street

Bitcoin (BTC)

In this project, I will be using closing price (USD).

First, let's look at BTC from 2013-05-01 to 2017-12-31.

BTC corr
BTC correlations between open, high, low, close prices and market capitalization
BTC ts
BTC closing prices
BTC price change percentage year
BTC price change % by year
  • 2014 is a bad year for BTC.
BTC price change percentage quarter
BTC price change % by quarter
  • In general, quarter 2 will yield better return than 1 and 3.
BTC price change percentage month
BTC price change % by month
BTC price change percentage day of month
BTC price change % by day of month
BTC lags
BTC lag plot
  • The correlation decreases when the lag increases, as expected.
BTC ACF
BTC daily ACF plot
BTC PACF
BTC daily PACF plot
  • From ACF plot, it's clear that it's not a white noise process.
  • Using Augmented Dickey-Fuller test, I obtained the p-value of 1. Hence BTC has unit root and is non-stationary and it follows a random walk
BTC returns
BTC returns
BTC returns ACF
BTC returns ACF plot
  • Based on ADF test, with p-value < 0.05, I conclude that BTC returns don't follow random walk process and is stationary
  • This is a close call as based on ACF plot, there are a few lags that seem significant.
BTC calendar
BTC returns in calendar heatmap

Days where BTC prices jump by at least 15%:

['2013–05–04', '2013–11–18', '2013–11–21', '2013–11–26', '2013–12–19', '2014–03–03', '2014–04–11', '2014–11–12', '2015–01–15', '2017–07–17', '2017–07–20', '2017–09–15', '2017–12–06', '2017–12–07']

Days where BTC prices drop by at least 15%:

['2013-07-05', '2013-11-19', '2013-12-01', '2013-12-06', '2013-12-07', '2013-12-16', '2013-12-18', '2014-01-07', '2014-03-27', '2014-04-10', '2015-01-13', '2015-01-14', '2015-08-18', '2016-01-15', '2017-09-14']

BTC ARIMA model

Based on the ACF & PACF plot, I managed to determined the ARIMA model order.

BTC ARIMA 417 fit residuals
ARIMA residual plot
BTC ARIMA 417 fit residuals distplot
ARIMA residual distribution plot
BTC FC 292 steps
ARIMA forecast for 292 steps
  • It's not surprising that the forecast values for 292 steps ahead are off.
BTC FC 292 step by step
ARIMA forecast for 292 steps
  • The result for 1 step forecast is better than what I expected. It appears that the statistical methods are able to handle the extreme volatility of cryptos.
BTC FC 292 step by step diffplot
ARIMA forecast for 292 steps

Ethereum (ETH)

First, let's look at ETH from 2015-08-08 to 2017-12-31.

ETH corr
ETH correlations between open, high, low, close prices and market capitalization
ETH ts
ETH closing prices
ETH price change percentage year
ETH price change % by year
  • ETH increases steadily over the years.
ETH price change percentage quarter
ETH price change % by quarter
ETH price change percentage month
ETH price change % by month
ETH price change percentage day of month
ETH price change % by day of month
ETH lags
ETH lag plot
ETH ACF
ETH daily ACF plot
ETH PACF
ETH daily PACF plot
  • From ACF plot, it's clear that it's not a white noise process.
  • Using Augmented Dickey-Fuller test, I obtained the p-value of 1. Hence ETH has unit root and is non-stationary and it follows a random walk
ETH returns
ETH returns
ETH returns ACF
ETH returns ACF plot
  • Based on ADF test, with p-value < 0.05, I conclude that ETH returns don't follow random walk process and is stationary
ETH calendar
ETH returns in calendar heatmap

Days where ETH prices jump by at least 15%:

['2015-08-11', '2015-08-13', '2015-08-19', '2015-08-20', '2015-10-22', '2015-10-26', '2015-10-27', '2015-10-29', '2015-11-01', '2016-01-23', '2016-01-25', '2016-02-07', '2016-02-09', '2016-02-11', '2016-02-18', '2016-02-22', '2016-03-01', '2016-03-09', '2016-03-12', '2016-04-30', '2016-07-22', '2016-08-03', '2016-12-06', '2017-01-03', '2017-01-04', '2017-02-14', '2017-03-13', '2017-03-15', '2017-03-16', '2017-03-19', '2017-03-24', '2017-04-27', '2017-04-30', '2017-05-04', '2017-05-19', '2017-05-21', '2017-05-30', '2017-06-10', '2017-06-12', '2017-07-12', '2017-07-17', '2017-07-18', '2017-09-15', '2017-09-18', '2017-11-24', '2017-12-11', '2017-12-12']

Days where ETH prices drop by at least 15%:

['2015-08-17', '2015-09-11', '2015-09-28', '2015-11-11', '2016-02-16', '2016-03-07', '2016-06-17', '2016-06-18', '2016-08-02', '2017-03-18', '2017-09-04', '2017-09-14', '2017-12-22']

ETH has more days where the prices jump by at least 15%.

ETH ARIMA model

ETH ARIMA 715 fit residuals
ARIMA residual plot
ETH ARIMA 715 fit residuals distplot
ARIMA residual distribution plot
ETH FC 292 steps
ARIMA forecast for 292 steps
ETH FC 292 step by step
ARIMA forecast for 292 steps
ETH FC 292 step by step diffplot
ARIMA forecast for 292 steps

Comparing growth rate of BTC & ETH

Biteum growth rate

In order to compare the rate of growth for BTC & ETH, I took the data starting from 2015-08-08 to 2017-12-31 and normalized them.

Between 2015-08-08 and 2017-12-31:

  • ETH grows by 1004 times while BTC grows by 54 times
  • ETH's growth is 18.5 times of BTC's growth

Final thoughts

The result from the one-step forecast model is better than what I expected.

I think the cryptos will have a decent future if they provide anonymity for the users. But based on the current call for regulation, I believe that the current cryptos are the "Friendster", a stepping stone before we have a truly decentralized, anonymous cryptocurrency.

2018-01-01

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