Hello Professor @sapwood. Until reading this class I had not fully understood the importance and depth of Fundamental Analysis. Truly On-chain metrics are a true ally to work in conjunction with technical analysis.

Let's start

_{Image edited by me in Powerpoint}

1- What is a HODL wave, how do you calculate the age of a coin(BTC, LTC) in a UTXO accounting structure? How do you interpret a HODL wave in Bull cycles?

A HODL wave is a graph of on-chain metrics that shows the time periods over which a coin has moved through bands or layers of colors representing trades, supply, and accumulation of the asset over different time periods.

The color classification is as follows:

• Warm colors: red, orange, and yellow represent the most recently traded crypto assets (hours, days, weeks, few months).
• Cool colors: purple, blue, green, and light yellow represent crypto assets with no transactions made or movement in a long time (months, years, a decade)

_{HODL Waves. Image taken from my Glassnodestudio account. ^Source}

To each of these bands corresponds a period of time stipulated by the graph. In the graph we also find a black line corresponding to the evolution of the price (logarithmic growth) with which we can see in contrast the relationship of the price change concerning the behavior of the bands (exchange and accumulation).

How do you calculate the age of a coin?

The calculation is purely graphical and is performed by reading and interpreting the color bands. The bands provide us with the activity ratio of the currency.

Fortunately for us, the structure of the indicator shows us through a box how much percentage of the cryptocurrency has moved for each of the bands at any given point. For example:

_{HODL Waves. Image taken from my Glassnodestudio account. ^Source}

If we place ourselves at a point at the beginning of 2019 (the year in which the 10-year band first appears), specifically on Sunday, February 24, we can see that the radius of movement (age) for each band was:

• 10 years: 1.3%.
• 7 - 10 years: 12.6%.
• 5 - 7 years: 6.3% .
• .
• 1 - 2 years: 24.3% .
• .
• 3 - 6 months: 7.6% .
• .
• 1 day - 1 week: 2.9% .

Here we can see that the band that has moved the most is the 1 to 2 years. And the band that has moved the least is the 10-year band.

How do you interpret a HODL wave in Bull cycles?

We must first start from the fundamental law of the market, the law of supply and demand.

For a Bull cycle to occur, obviously the price must go up and up; and for this to occur (based on the law of supply and demand) there must be a shortage of the asset, or in other words, demand must increase due to the lack of circulating assets.

Precisely the HODL wave is an indicator of accumulation and movement of the asset and shows us this through its bands, so during the Bull cycles, we will observe a widening of the older bands (cold colors) which in turn is interpreted as an increase in the percentage of coins that are been accumulated in a long time... and thus increasing demand.

_{HODL Waves. Image taken from my Glassnodestudio account. ^Source}

Another interesting fact seen during a strong bull cycle is that the price line begins to move towards the cooler colored bands without showing sudden and pronounced drops in its structure, in other words, the curve smooths out.

2- Fundamental Analysis Model

For my fundamental analysis, I have selected a not very well-known crypto currency that I have been following for a few months; ENJIN COIN (ENJ).

On the other hand, all on-chain data and graphs are taken from my Santiment account.

Daily Active Addresses

Short-term (3 months)

_{Daily Active Addresses. Image taken from my Santiment account. ^Source}

On the short-term chart, we can see that the number of Active Addresses peaked on June 28th (2860), which coincides with one of its lowest price points (1.08 USD). However thanks to this spike in Daily Active Addresses, the price started to increase over the next 3 months.

As August 18 arrived the amount of Daily Active Addresses started to decrease, however, the price remained around 2 USD. However as a consequence of this, about a month later, on September 7 the price of ENJ dropped to 1.8 USD.

Long term (3 years)

_{Daily Active Addresses. Image taken from my Santiment account. ^Source}

In the long-term chart, we can see that the first peaks of Daily Active Addresses occurred between November 2019 and April 2020. This caused the price to gradually increase until reaching the maximum peak experienced on April 7, 2021 (5575), at the same time this increase in Daily Active Addresses also generated a historical high in the price of the asset (3.27 USD).

However, after that peak, the amount of Daily Active Addresses began to decrease dramatically, and so did the price. Finally, so far this year we can see that the number of Daily Active Addresses is again starting to rebound, which has caused the price to recover as well.

As far as can be seen, in both cases, the price goes in tandem and is influenced by the number of Daily Active Addresses.

Transaction Volume

In the case of transaction volume, it is expected to see that the price action will not vary immediately after a peak in transaction volume, in other words, the price action will react with some delay and in response to the movement of transaction volume.

Short-term (3 months)

_{Transaction Volume. Image taken from my Santiment account. ^Source}

For this chart, you can see price spikes after approximately 15 days following an increase in Transaction Volume. For example, the first spike on June 24 is followed by a price increase of 1.39 USD almost two weeks later.

When the price reaches 1.39 USD, another spike in the Transaction Volume is generated again, perhaps as a result of closing some positions or selling some assets. Later the price falls but almost 3 weeks later it returns to the price it had before.

Finally, around the 26th of July, there is again another increase in the number of Transaction Volume, so that in consequence 2 weeks later the price reaches 2 USD.

We can see that in this case the price reacts at intervals of about 2 weeks since a sharp movement in the Transaction Volume is generated.

Long term (3 years)

_{Transaction Volume. Image taken from my Santiment account. ^Source}

In this graph, two points caught my attention. First, the historical peak of the Transaction Volume (159M) on January 18, 2021, accompanied by an increase in transactions throughout that month. The result can be seen 4 months later in April when the price reaches an all-time high of 3.48 USD.

However, during the period between May and June, there is a large drop in Transaction Volume, which also explains the drop in the price of ENJIN a month later.

In both cases again the price reacts based on the Transaction Volume movements after a certain period of time.

NVT

The NTV or Network Value to Transaction Ratio graph shows the value of the asset concerning the market. Therefore, the graph generates high peaks and low peaks that represent that the asset is overvalued or undervalued.

Whereby, after the NTV shows a high peak (overvalued), the price is expected to fall, and conversely when the NTV shows a low peak (undervalued), the price should rise.

Short-term (3 months)

_{NVT. Image taken from my Santiment account. ^Source}

For example, on the chart, we can see that after the high peak on June 18, the price drops to a low point of 0.9 USD.

On the other hand, during the period of the whole month of July several low peaks accumulate, so the result is that the price starts to rise in the middle of that month.

Long term (3 years)

_{NVT. Image taken from my Santiment account. ^Source}

In this graph occurs something very curious and significant that is in line with what is expected from the indicator. At the end of 2019, there was an accumulation of several high peaks, with which price always remained low, below 0.1 USD.

However, starting in January 2021 there was a very low peak (3.96) and an accumulation of low peaks, leading to an increase in price the following 4 months.

Finally, in mid-June, there was a new high peak and an accumulation of high peaks all that month which is related to the fall of the asset price to almost 0.9 USD.

Exchange Flow Balance

This indicator is personally one of the ones I like the most and one of the first I understood. Again we will rely on the law of supply and demand.

Exchange Flow Balance refers in simple terms to the balance between the number of coins entering and leaving an Exchange. It is expected that the more coins entering the Exchange the more supply there will be because there will be more assets in circulation. When the supply goes up, naturally the price will tend to go down, thus a bearish cycle will occur.

On the other hand, the more coins leave the Exchange the less supply there will be and the more demand there will be due to the lack of circulation of the asset in the market. When demand rises the price will tend to rise and produce bullish cycles.

Short-term (3 months)

_{Exchange Flow Balance. Image taken from my Santiment account. ^Source}

The short-term chart highlights a massive outflow from the exchanges (-6.9M) that occurred in mid-July. This in turn triggered the beginning of a bullish cycle in less than a week.

The following month saw more outflows than inflows, so the ENJIN price continued to rise.

So, it can be seen that the expected results were obtained.

Long term (3 years)

_{Exchange Flow Balance. Image taken from my Santiment account. ^Source}

In this period we see even more varied results. First, a massive outflow (-28.6M) occurred in early April 2020, which triggered a price increase from 0.05 USD to 0.2 USD.

Then, a new inflow period occurred in January 2021 which is related to the overwhelming price increase from 0.4 USD to 3.4 USD.

However, there are also inconsistencies in the signals, since at the beginning of March there was a massive inflow to the exchanges, and yet the price continued to rise until April, when, due to the supply of the asset, it began to fall.

Supply on Exchanges as a percentage of Total Supply

This indicator, in the same way as the previous one, is a reflection of the supply in the exchanges, therefore it is expected that if it decreases, the price of the asset will increase. If, on the other hand, the percentage of Total Supply increases, the price will decrease.

Short-term (3 months)

_{Supply on Exchanges as a percentage of Total Supply. Image taken from my Santiment account. ^Source}

In this first graph, another inconsistency of results occurred. During the first period, the expected result is observed, while the percentage of Total Supply decreases, the price increases.

Then, as of July 19, after a drop in both indicators, we observe that both the price and the percentage of Total Supply begin to rise in tandem.

Long term (3 years)

_{Supply on Exchanges as a percentage of Total Supply. Image taken from my Santiment account. ^Source}

In this graph, the result is more consistent, since we see that as the percentage of Total Supply decreases, the price gradually increases.

In fact, when the percentage of Total Supply has its steepest drop in January 2021, that is when the steepest price rise begins.

3- Are the on-chain metrics that you have chosen helpful for short-term or medium-term or long term(or all)? Are they explicit w.r.t price action? What are its limitations? Examples/Screenshot?

The on-chain metrics are fabulous tools, however, they are not without flaws, so it is important to always use several in the fundamental analysis because, in the case of a false signal in an indicator, this can be filtered by the other indicators.

In the case of the on-chain metrics chosen, it is my opinion that they performed best in the long-term period. It was in this period that more signals consistent with price action were observed. It may be because these were more consolidated time periods.

On the other hand in short-term periods (and possibly medium-term as well) many false or confusing signals are observed as a product of rapid price movement or perhaps external factors such as a sociological or psychological factor. Such factors that can easily affect on-chain metrics analysis in the short term are smoothed out in the long term. Some examples of inconsistencies are as follows:

_{Inconsistency 1.Image taken from my Santiment account. ^Source}

In this first example, we see the indicator Supply on Exchanges as a percentage of Total Supply, which should behave inversely to the price action. However as we can see, in this period both behaved the same way.

We can also find other types of inconsistencies, such as:

_{Inconsistency 2.Image taken from my Santiment account. ^Source}

For the Exchange Flow Balance indicator, it is expected that when there is an outflow from the exchanges, the price will increase due to lack of supply of the asset, and when inflows are generated, the price will decrease due to an increase in supply. However, we can see that during two massive inflows between February and March, the price continued in an uptrend.

On the other hand, in April, several outflows could be evidenced. However, the price of the asset continued to fall.

Limitations

Firstly I should again point out that the selected asset (ENJIN COIN) Is relatively new (2017) relative to other currencies. So this in itself represents a limitation since compared to Bitcoin or Ether, for example, there is not enough data available for this cryptocurrency to give more accurate historical data.

Another limitation is that if you don't know, ENJIN started with an ICO in 2017, and sometimes projects coming from an ICO do not possess reliable data.

Remember that the purpose of an ICO is to gain capital through an offering where the developers of the project want to show an image of greatness and profitability. However not all the time this happens, so some projects alter the data that then serve as the basis for the platforms that generate the on-chain metrics. So we cannot be completely sure that all the data is true.

Finally, we must clarify that some indicators such as Exchange Flow Balance and Supply on Exchanges as a percentage of Total Supply are not absolute data, but reflect a limited spectrum of the entire real movement of the asset in the market.

Still these on-chain metrics are a relative but not 100% accurate measure.

Conclusion

As I said before, on-chain metrics are wonderful tools to complement our analysis and to be able to see data or information that remains hidden to most people.

This hidden information, if well interpreted can give us a great advantage over the rest of the traders, however, there are some principles that we must manage.

Personally, I believe that fundamental analysis is best done on a long-term basis, therefore we should always take time periods in our charts that show us the complete development of the asset over time.

On the other hand, it is not enough to handle only one on-chain metric scam, it is necessary to know how to use and interpret several, as all together they can give us a more correct view of the underlying data of an asset.

The on-chain metrics are not 100% infallible, so if there is a false signal in one, it can be filtered out by looking at two or three others. The problem comes when we use only one because if there is a false signal, there will be no way to corroborate it.