Cryptocurrency overvalued

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Triangular arbitrage is a commonly known technique for exploiting price differences between assets to try and make a quick and low-risk profit. Triangular arbitrage involves trading between three different assets and exploiting price differences to try and make a profit.

To find opportunities that are profitable you can do a bit of math to determine if a cross-rate is overvalued, meaning that there is a price discrepancy when trading between three different assets. We will use the following order book data for this example, which shows the ask and bid for each trade pair:. Example order book data with ask and bid rates and amounts. In this example, our trading fees are 0. It's wise to cross-reference this information with discussions of the project.

What are other people saying about it? Are there any red flags raised? Do the goals seem realistic? A strong whitepaper should give us an idea of the use case the crypto asset is targeting. At this stage, it's important to identify the projects it's competing with, as well as the legacy infrastructure it seeks to replace. Ideally, fundamental analysis of these should be just as rigorous. An asset may look appealing by itself, but the same indicators applied to similar crypto assets could reveal ours to be weaker than the others.

Information about how the asset currently trades, what it traded at previously, liquidity, etc. However, other interesting metrics that might fall under this category are those that concern the economics and incentives of the crypto asset's protocol. By itself, market capitalization can be misleading. In theory, it would be easy to issue a useless token with a supply of ten million units. This valuation is obviously distorted — without a strong value proposition, it's unlikely that the wider market would be interested in the token.

Nonetheless, market capitalization is used extensively to figure out the growth potential of networks. Some crypto investors view "small-cap" coins to be more likely to grow compared to "large-cap" ones. Others believe large-caps to have stronger network effects, and, therefore, stand a better chance than unestablished small-caps.

A problem we might encounter with an illiquid market is that we're unable to sell our assets at a "fair" price. This tells us there are no buyers willing to make the trade, leaving us with two options: lower the ask or wait for liquidity to increase.

Being familiar with liquidity can be helpful in the context of fundamental analysis. Ultimately, it acts as an indicator of the market's interest in a prospective investment. An indicator often combines multiple metrics using statistical formulas to create easier to analyze relationships. However, there is still a lot of overlap between a metric and an indicator, making the definition quite loose.

While the number of active wallets is valuable, we can combine it with other data to gain deeper insights. This calculation would give you an average amount held per active wallet. Fundamental analysis tools make gathering all these metrics and indicators easier. While you can look at the raw data on blockchain explorers, an aggregator or dashboard is a more efficient use of your time.

Some tools allow you to create your own indicators with your chosen metrics. Now that we're familiar with the difference between metrics and indicators, let's talk about how we combine metrics to better understand the financial health of the assets we're dealing with. Why do this? Well, as we've outlined in the previous sections, there are shortcomings with every metric. Furthermore, if you're just looking at a collection of numbers for each cryptocurrency project, you're overlooking a lot of crucial information.

Consider the following scenario:. We're only scratching the surface on the kinds of indicators that can be used. Fundamental analysis is all about developing a system that can be used to value projects across the board. The more quality research we do, the more data we have to work with. There are a huge number of indicators and metrics available to choose from.

For a beginner, start with some of the most popular ones first. Each indicator only tells part of the story, so use a variety of them in your analysis. We use the daily transaction volume as a stand-in for the underlying, inherent value of a coin. This concept works on the assumption that the more volume moving around the system, the more value the project has. Prices are rising without there being a matched increase in the underlying value. This scenario could suggest a possible buying opportunity.

The higher the value of the ratio, the more likely a bubble will occur. This point is usually seen when the NVT ratio is above A decreasing ratio indicates that the crypto is becoming less overvalued. Before we dive into this statistic, we need to understand what realized value means for a crypto asset. Market value, otherwise known as market cap, is simply the total supply of coins multiplied by the current market price.

Realized value, on the other hand, discounts for coins lost in inaccessible wallets. Coins sat in wallets are instead valued using the market price at the time of their last movement. To get our MVRV indicator, we simply divide the market cap by the realized cap. A ratio over 3. This number signifies that the coin may currently be overvalued. If the value is too low and under 1, the market is undervalued. This situation would be a good point to buy as buying pressure increases and drives up the price.

The stock-to-flow indicator is a popular indicator of the price of a cryptocurrency, typically with a limited supply. The model looks at each cryptocurrency as a fixed, scarce resource similar to precious metals or stones. Because there is a known limited supply without new sources to be found, investors use these assets as a store of value. As you can see, stock-to-flow has been a reasonably good indicator of the price of Bitcoin. The model does have some drawbacks, however.

For example, gold currently has a stock-to-flow ratio of around 60, meaning it would take 60 years to mine the current supply of gold at the current flow. Stock-to-flow models also struggle when deflation happens, as this would suggest a minus price.

As people lose the keys to their wallets and no more bitcoins are produced, we would see a negative ratio. We would see the stock-to-flow ratio flow go towards infinity and then become minus if we displayed this graphically. Baserank is a research platform for crypto assets that aggregates information and reviews from analysts and investors. While there are some premium reviews for subscribers, free users can still see a comprehensive overview of reviews broken down into sections, including team, utility, and investment risk.

Networks with high fees are typically experiencing great demand. Some blockchains are built with low fees in mind, making a comparison with other networks challenging. For example, large market cap coins such as Dogecoin or Cardano are low in the overall charts due to their cheap transaction fees. Glassnode Studio offers a dashboard displaying a wide range of on-chain metrics and data. Like most tools on offer, it is subscription-based. However, the amount of free on-chain data it offers is suitable for amateur investors and quite in-depth.

Done correctly, fundamental analysis can provide invaluable insights into cryptocurrencies in a way that technical analysis cannot. Being able to separate the market price from the "true" value of a network is an excellent skill to have when trading. As with many strategies, there's no one-size-fits-all FA playbook. Hopefully, this article will have helped you understand some of the factors to consider before entering or exiting positions with crypto assets. A Guide to Cryptocurrency Fundamental Analysis.

Table of Contents. Trading Essentials Economics. Trading assets as volatile as cryptocurrencies requires some skill. Selecting a strategy , understanding the vast world of trading , and mastering technical and fundamental analysis are practices that come with a learning curve. When it comes to technical analysis, some expertise can be inherited from the legacy financial markets.

Many crypto traders use the same technical indicators seen in Forex , stock, and commodities trading. As such, these technical analysis tools are also extremely popular in the cryptocurrency space. Technical analysis also yields valuable trading data, but it results in different insights. TA users believe they can predict future price movements based on the past performance of assets. This is achieved by identifying candlestick patterns and studying essential indicators.

Traditional fundamental analysts generally look to business metrics to figure out what they view to be its real value. Indicators used include earnings per share how much profit a company makes for each outstanding share , or the price-to-book ratio how investors value the company versus its book value. They might do this for several businesses within a niche, for example, to figure out how their prospective investment stands in relation to others.

For a more comprehensive introduction to fundamental analysis, see What is Fundamental Analysis? Cryptocurrency networks can't really be assessed through the same lens as traditional businesses. If anything, the more decentralized offerings like Bitcoin BTC are closer to commodities.

But even with the more centralized cryptocurrencies such as those issued by organizations , traditional FA indicators can't tell us much. It's important to note that there's no single measure that can give us a full picture of the network we're assessing. We could look at the number of active addresses on a blockchain and see that it has been sharply increasing. But that doesn't tell us much by itself.

For all we know, that could be a standalone actor transferring money back and forth to themselves with new addresses each time. In the following sections, we'll take a look at three categories of crypto FA metrics: on-chain metrics , project metrics , and financial metrics.

This list will be non-exhaustive, but it should provide us with a decent foundation for the subsequent creation of indicators. On-chain metrics are those that can be observed by looking at data provided by the blockchain. We could do this ourselves by running a node for the desired network and then exporting the data, but that can be time-consuming and expensive. Particularly if we're only considering the investment, and don't want to waste time or resources on the endeavor. A more straightforward solution would be to pull the information from websites or APIs specifically designed for the purpose of informing investment decisions.

For example, CoinMarketCap's on-chain analysis of Bitcoin gives us a myriad of information. Transaction count is a good measure of activity taking place on a network. By plotting the number for set periods or by using moving averages , we can see how activity changes over time.

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Let's remove all of the zero values from the dataframe, since we know that the price of Bitcoin has never been equal to zero in the timeframe that we are examining. We can now calculate a new column, containing the average daily Bitcoin price across all of the exchanges. Yup, looks good. We'll use this aggregate pricing series later on, in order to convert the exchange rates of other cryptocurrencies to USD.

Now that we have a solid time series dataset for the price of Bitcoin, let's pull in some data for non-Bitcoin cryptocurrencies, commonly referred to as altcoins. For retrieving data on cryptocurrencies we'll be using the Poloniex API. Most altcoins cannot be bought directly with USD; to acquire these coins individuals often buy Bitcoins and then trade the Bitcoins for altcoins on cryptocurrency exchanges.

Now we have a dictionary with 9 dataframes, each containing the historical daily average exchange prices between the altcoin and Bitcoin. Now we can combine this BTC-altcoin exchange rate data with our Bitcoin pricing index to directly calculate the historical USD values for each altcoin. Now we should have a single dataframe containing daily USD prices for the ten cryptocurrencies that we're examining. This graph provides a pretty solid "big picture" view of how the exchange rates for each currency have varied over the past few years.

Note that we're using a logarithmic y-axis scale in order to compare all of the currencies on the same plot. You might notice is that the cryptocurrency exchange rates, despite their wildly different values and volatility, look slightly correlated.

Especially since the spike in April , even many of the smaller fluctuations appear to be occurring in sync across the entire market. We can test our correlation hypothesis using the Pandas corr method, which computes a Pearson correlation coefficient for each column in the dataframe against each other column.

Computing correlations directly on a non-stationary time series such as raw pricing data can give biased correlation values. These correlation coefficients are all over the place. Coefficients close to 1 or -1 mean that the series' are strongly correlated or inversely correlated respectively, and coefficients close to zero mean that the values are not correlated, and fluctuate independently of each other.

Here, the dark red values represent strong correlations note that each currency is, obviously, strongly correlated with itself , and the dark blue values represent strong inverse correlations. What does this chart tell us? Essentially, it shows that there was little statistically significant linkage between how the prices of different cryptocurrencies fluctuated during Now, to test our hypothesis that the cryptocurrencies have become more correlated in recent months, let's repeat the same test using only the data from These are somewhat more significant correlation coefficients.

Strong enough to use as the sole basis for an investment? Certainly not. It is notable, however, that almost all of the cryptocurrencies have become more correlated with each other across the board. The most immediate explanation that comes to mind is that hedge funds have recently begun publicly trading in crypto-currency markets [1] [2]. These funds have vastly more capital to play with than the average trader, so if a fund is hedging their bets across multiple cryptocurrencies, and using similar trading strategies for each based on independent variables say, the stock market , it could make sense that this trend of increasing correlations would emerge.

For instance, one noticeable trait of the above chart is that XRP the token for Ripple , is the least correlated cryptocurrency. The notable exception here is with STR the token for Stellar , officially known as "Lumens" , which has a stronger 0. What is interesting here is that Stellar and Ripple are both fairly similar fintech platforms aimed at reducing the friction of international money transfers between banks.

It is conceivable that some big-money players and hedge funds might be using similar trading strategies for their investments in Stellar and Ripple, due to the similarity of the blockchain services that use each token.

Quick Plug - I'm a contributor to Chipper , a very early-stage startup using Stellar with the aim of disrupting micro-remittances in Africa. This explanation is, however, largely speculative. Maybe you can do better.

With the foundation we've made here, there are hundreds of different paths to take to continue searching for stories within the data. Hopefully, now you have the skills to do your own analysis and to think critically about any speculative cryptocurrency articles you might read in the future, especially those written without any data to back up the provided predictions. Thanks for reading, and please comment below if you have any ideas, suggestions, or criticisms regarding this tutorial.

If you find problems with the code, you can also feel free to open an issue in the Github repository here. I've got second and potentially third part in the works, which will likely be following through on some of the ideas listed above, so stay tuned for more in the coming weeks. Note - Disqus is a great commenting service, but it also embeds a lot of Javascript analytics trackers.

Step 1 - Setup Your Data Laboratory The tutorial is intended to be accessible for enthusiasts, engineers, and data scientists at all skill levels. Step 1. Step 2. A Guide to Machine Learning in Python. Get the latest posts delivered to your inbox. I hate spam. Are there any red flags raised?

Do the goals seem realistic? A strong whitepaper should give us an idea of the use case the crypto asset is targeting. At this stage, it's important to identify the projects it's competing with, as well as the legacy infrastructure it seeks to replace. Ideally, fundamental analysis of these should be just as rigorous. An asset may look appealing by itself, but the same indicators applied to similar crypto assets could reveal ours to be weaker than the others. Information about how the asset currently trades, what it traded at previously, liquidity, etc.

However, other interesting metrics that might fall under this category are those that concern the economics and incentives of the crypto asset's protocol. By itself, market capitalization can be misleading. In theory, it would be easy to issue a useless token with a supply of ten million units.

This valuation is obviously distorted — without a strong value proposition, it's unlikely that the wider market would be interested in the token. Nonetheless, market capitalization is used extensively to figure out the growth potential of networks. Some crypto investors view "small-cap" coins to be more likely to grow compared to "large-cap" ones.

Others believe large-caps to have stronger network effects, and, therefore, stand a better chance than unestablished small-caps. A problem we might encounter with an illiquid market is that we're unable to sell our assets at a "fair" price. This tells us there are no buyers willing to make the trade, leaving us with two options: lower the ask or wait for liquidity to increase. Being familiar with liquidity can be helpful in the context of fundamental analysis.

Ultimately, it acts as an indicator of the market's interest in a prospective investment. An indicator often combines multiple metrics using statistical formulas to create easier to analyze relationships. However, there is still a lot of overlap between a metric and an indicator, making the definition quite loose.

While the number of active wallets is valuable, we can combine it with other data to gain deeper insights. This calculation would give you an average amount held per active wallet. Fundamental analysis tools make gathering all these metrics and indicators easier. While you can look at the raw data on blockchain explorers, an aggregator or dashboard is a more efficient use of your time.

Some tools allow you to create your own indicators with your chosen metrics. Now that we're familiar with the difference between metrics and indicators, let's talk about how we combine metrics to better understand the financial health of the assets we're dealing with. Why do this? Well, as we've outlined in the previous sections, there are shortcomings with every metric.

Furthermore, if you're just looking at a collection of numbers for each cryptocurrency project, you're overlooking a lot of crucial information. Consider the following scenario:. We're only scratching the surface on the kinds of indicators that can be used. Fundamental analysis is all about developing a system that can be used to value projects across the board. The more quality research we do, the more data we have to work with. There are a huge number of indicators and metrics available to choose from.

For a beginner, start with some of the most popular ones first. Each indicator only tells part of the story, so use a variety of them in your analysis. We use the daily transaction volume as a stand-in for the underlying, inherent value of a coin. This concept works on the assumption that the more volume moving around the system, the more value the project has. Prices are rising without there being a matched increase in the underlying value.

This scenario could suggest a possible buying opportunity. The higher the value of the ratio, the more likely a bubble will occur. This point is usually seen when the NVT ratio is above A decreasing ratio indicates that the crypto is becoming less overvalued. Before we dive into this statistic, we need to understand what realized value means for a crypto asset. Market value, otherwise known as market cap, is simply the total supply of coins multiplied by the current market price.

Realized value, on the other hand, discounts for coins lost in inaccessible wallets. Coins sat in wallets are instead valued using the market price at the time of their last movement. To get our MVRV indicator, we simply divide the market cap by the realized cap.

A ratio over 3. This number signifies that the coin may currently be overvalued. If the value is too low and under 1, the market is undervalued. This situation would be a good point to buy as buying pressure increases and drives up the price.

The stock-to-flow indicator is a popular indicator of the price of a cryptocurrency, typically with a limited supply. The model looks at each cryptocurrency as a fixed, scarce resource similar to precious metals or stones. Because there is a known limited supply without new sources to be found, investors use these assets as a store of value.

As you can see, stock-to-flow has been a reasonably good indicator of the price of Bitcoin. The model does have some drawbacks, however. For example, gold currently has a stock-to-flow ratio of around 60, meaning it would take 60 years to mine the current supply of gold at the current flow. Stock-to-flow models also struggle when deflation happens, as this would suggest a minus price. As people lose the keys to their wallets and no more bitcoins are produced, we would see a negative ratio.

We would see the stock-to-flow ratio flow go towards infinity and then become minus if we displayed this graphically. Baserank is a research platform for crypto assets that aggregates information and reviews from analysts and investors. While there are some premium reviews for subscribers, free users can still see a comprehensive overview of reviews broken down into sections, including team, utility, and investment risk.

Networks with high fees are typically experiencing great demand. Some blockchains are built with low fees in mind, making a comparison with other networks challenging. For example, large market cap coins such as Dogecoin or Cardano are low in the overall charts due to their cheap transaction fees.

Glassnode Studio offers a dashboard displaying a wide range of on-chain metrics and data. Like most tools on offer, it is subscription-based. However, the amount of free on-chain data it offers is suitable for amateur investors and quite in-depth.

Done correctly, fundamental analysis can provide invaluable insights into cryptocurrencies in a way that technical analysis cannot. Being able to separate the market price from the "true" value of a network is an excellent skill to have when trading. As with many strategies, there's no one-size-fits-all FA playbook. Hopefully, this article will have helped you understand some of the factors to consider before entering or exiting positions with crypto assets.

A Guide to Cryptocurrency Fundamental Analysis. Table of Contents. Trading Essentials Economics. Trading assets as volatile as cryptocurrencies requires some skill. Selecting a strategy , understanding the vast world of trading , and mastering technical and fundamental analysis are practices that come with a learning curve. When it comes to technical analysis, some expertise can be inherited from the legacy financial markets. Many crypto traders use the same technical indicators seen in Forex , stock, and commodities trading.

As such, these technical analysis tools are also extremely popular in the cryptocurrency space. Technical analysis also yields valuable trading data, but it results in different insights. TA users believe they can predict future price movements based on the past performance of assets. This is achieved by identifying candlestick patterns and studying essential indicators. Traditional fundamental analysts generally look to business metrics to figure out what they view to be its real value.

Indicators used include earnings per share how much profit a company makes for each outstanding share , or the price-to-book ratio how investors value the company versus its book value. They might do this for several businesses within a niche, for example, to figure out how their prospective investment stands in relation to others. For a more comprehensive introduction to fundamental analysis, see What is Fundamental Analysis?

Cryptocurrency networks can't really be assessed through the same lens as traditional businesses. If anything, the more decentralized offerings like Bitcoin BTC are closer to commodities. But even with the more centralized cryptocurrencies such as those issued by organizations , traditional FA indicators can't tell us much. It's important to note that there's no single measure that can give us a full picture of the network we're assessing.

We could look at the number of active addresses on a blockchain and see that it has been sharply increasing. But that doesn't tell us much by itself. For all we know, that could be a standalone actor transferring money back and forth to themselves with new addresses each time. In the following sections, we'll take a look at three categories of crypto FA metrics: on-chain metrics , project metrics , and financial metrics.

This list will be non-exhaustive, but it should provide us with a decent foundation for the subsequent creation of indicators. On-chain metrics are those that can be observed by looking at data provided by the blockchain. We could do this ourselves by running a node for the desired network and then exporting the data, but that can be time-consuming and expensive. Particularly if we're only considering the investment, and don't want to waste time or resources on the endeavor.

A more straightforward solution would be to pull the information from websites or APIs specifically designed for the purpose of informing investment decisions. For example, CoinMarketCap's on-chain analysis of Bitcoin gives us a myriad of information.

Transaction count is a good measure of activity taking place on a network. By plotting the number for set periods or by using moving averages , we can see how activity changes over time. Not to be confused with the transaction count, the transaction value tells us how much value has been transacted within a period. Perhaps more important for some crypto assets than others, the fees paid can tell us about the demand for block space.