This post is the first of a series on technical analysis in the cryptocurrency space. In this post, we will explain the technical indicators that we use to generate trading signals.
Bitcoins price has increased rapidly over the past year and a lot of newcomers are getting into digital currencies as a means of investment or speculation. If you are new to cryptocurrencies, you might want to know how Bitcoins price looks like.
The idea of this post is to analyze Bitcoins price in the time domain. We will look at its statistical properties and apply some technical indicators to see if there are any trading signals we can extract from them.
We will start by looking at Bitcoins historical prices and proceed by applying some simple statistical tools that allow us to get more information from the data. After that, we will take a look at some common technical indicators and discuss whether they are useful in predicting future prices.
There are many factors that play into the price of cryptocurrency assets. Some are country-specific, some are specific to the exchange and some are rooted in the technology and its application. Carbonblack’s recent report on North Korean hackers stealing over $571 million in cryptocurrency is a perfect example of how geopolitical events can impact price.
In this blog, we look at one particular factor that impacts price: order book depth. Order book depth is defined as the number of buy and sell orders for a particular asset at a given point in time. It is a measure of liquidity and market efficiency. A deep order book can be considered a proxy for market efficiency, because it indicates that there are enough players in the market to efficiently absorb large orders without moving the price significantly. In other words, market participants can enter or exit positions without affecting the price.
In this blog, we will see whether or not order book depth has an impact on cryptocurrency prices using data from Poloniex. We will also look at whether increases in order book depth have an effect on long-term returns
This paper presents a descriptive analysis of the price properties of Bitcoin and other cryptocurrencies. We analyze daily returns and volatility of Bitcoin and four other cryptocurrencies, namely Ethereum, Ripple, Litecoin, and Dash, in comparison with the US dollar (USD) as well as with each other. We find that cryptocurrency returns are significantly more volatile than USD returns over the sample period. Furthermore, we find that Bitcoin volatility is not contemporaneously related to a cryptocurrency index or other traditional asset classes such as stock indices, currencies or commodities returns. The correlation between Bitcoin and other cryptocurrencies is also very low, indicating that cryptocurrencies are independent of each other. Our results indicate that cryptocurrencies have an investment potential and financial characteristics different from traditional asset classes.
Keywords: Cryptocurrency; Bitcoin; Volatility; Correlation; Financial Crisis; Blockchain
Early on in the cryptocurrency trading game, I was primarily interested in the fundamentals of each digital asset. However, as time went on and I accrued hundreds of coins from airdrops and ICOs, it became difficult to track them all. As such, I began to rely more and more on price charts to help me understand where my assets were headed.
In this post, I will lay out some basic price analysis concepts that will hopefully serve as a gentle introduction for anyone unfamiliar with technical analysis. If you find yourself to be more knowledgeable than the average bear when it comes to charting, feel free to skip ahead.
For those who are unfamiliar with how crypto prices are graphed, here is an example:
A few things to note about this chart:
1. The Y-axis (vertical axis) represents the price of Bitcoin in US dollars (USD).
2. The X-axis (horizontal axis) represents time moving from left to right; in this case, the chart depicts price action over one day (each candle represents a 1 hour interval).
We begin by examining the distribution of returns for the period from January 1, 2017 to December 31, 2017. To be precise, we calculate daily returns as the difference in closing prices between days and then annualized returns as the difference in closing prices between January 1 and December 31. In Figure 1 below, we see that most cryptocurrencies have a median return of around 0%, with some currencies having a median return above 100%. On the tails of these distributions, there are a few currencies with negative returns. While not shown here, the mean return is typically positive and much higher than the median return (the same is true for all other distributions in this post).
The cryptocurrency market is still in its initial stage. This post will use a simple statistical approach to analyze historical prices of the world’s most popular cryptocurrency, Bitcoin. We will also see how this analysis can be extended to other cryptocurrencies. In particular, we will study autocorrelations and distributions of different time spans as well as investigate the daily returns of Bitcoin prices. To perform our analysis, we use Quandl’s historical data for Bitcoin from 2013-09-05 to 2017-09-05.
Autocorrelation is a measure of correlation between the values of the same variable over different times (i.e., how correlated a variable is with itself). An autocorrelation plot graphs autocorrelation scores on the y-axis and lag on the x-axis. Here we present an autocorrelation plot for each day, week, month and year for Bitcoin prices:
The plots show that there are strong correlations between consecutive days, weeks and months but no correlations between years (at least between 2013 and 2017). Hence in our subsequent analysis we consider daily, weekly and monthly time spans but not yearly ones.
Next we study the distribution of daily, weekly and monthly log