How On-chain Data Analysis Can Be Used to Value Crypto Companies

9 min readNov 19, 2020

Disclaimer: This is not in any way financial advice. I am not your financial advisor. The content below is strictly for research purposes only. Investor, beware!

Back in late August 2020, I made a bold prediction on the cool off on alt coins especially DeFi projects. As anon devs, anon founders, non VC backed projects took off in the form of Sushi, Yam, Pickle and many others; it was clear that there was an astounding amount of euphoria in the DeFi sector. My bold prediction was also based of TA which showed daily bullish divergence on Bitcoin Dominance. This would mean that Bitcoin would take back dominance of the entire Crypto market either by: 1. Increasing in value so much that it would drain liquidity from alt coins OR 2. Bitcoin falls in value bringing down alt coins in an aggressive manner. The latter being more likely as trading history shows it. As my prediction did hold true, DeFi and Uniswap coins did correct on average about 60% from the start of September to the start of November. However, Bitcoin at first did get rejected around the 12.5k area, it made an impressive recovery to near all time high prices. Looking at Bitcoin Dominance again, it is clear that alts have been undervalued and will appreciate in value against Bitcoin for the short term (a few weeks to a couple months). As you can see in Chart 1.1, there is daily bearish divergence however Elliot wave theory would tell us that we are still trending higher on the weekly time frame. I am expecting one more leg up within the next 6 months:

Since DeFi has been such a hot topic in the last 9 or so months, I’d expect the DeFi sector to continue to outperform other alt coins due to the anticipation of Ethereum 2.0. Some undervalued DeFi projects that caught my eye are SNX, YFI, KNC, REN, BAND, and Akropolis. Let us see how on-chain data analytics can help traders generate alpha.

In Table 1.1, I used the following metrics: ITM, OTM, and ATM which are a calculations representing the average buy price of said token held within a wallet. In the money or ITM would mean that a percentage of wallet holders are in profit. Out the money or OTM would represent a percentage of wallet holders that are holding said token at a loss. At the money would mean the wallet owner is near or at break even. ATM is an interesting metric because an unusual high percentage of ATM wallets could mean that price action can be in a period of consolidation. Either investors are selling at break even to free up capital, or there are investors who are accumulating dips. Elliot wave theory suggests that a break of consolidation would be followed by an impulse (either up or down depending on the trend). A high ITM percentage could hint towards taking profits. This is because trading any financial asset is governed by game theory. This theory suggests that one investors loss, is another investors profit.

Holding period between 1–12 months could represent investors who are only holding an asset and not utilizing those assets. This can represent wallets used for governance, staking, multisig-functions, or protocols that allow lending or collateralization of said assets. In other words, wallets that have a holding period between 1–12 months are not actively trading the asset. Whereas, wallets that have a holding period less than 1 month are likely to be active DEX traders. Both metrics can represent network utility because wallets that hold said token for more than a month are more likely to be owned by protocols that are providing utility. A high percentage of wallets with more than a month of holding period would be a good representation of value if the project offers LP, vault strategies, staking incentives etc. A low percentage of wallets with less than a month of holding period would also be good because it diminishes exchange supply volume. In other words, the smaller the percentage of wallets holding said token under a month would mean there is less sell pressure.

In Table 1.1, to measure adoption, I accounted for daily new wallet creation. This metric represents investors who made a new wallet to hold said token. I calculated the ratio of newly created wallets everyday over a 30-day period. A result over 1 would mean more wallets were created over 30 days in the last 30 days. A value less than 1 would mean less wallets were created in the same period. Creation of new wallets is good because it shows that more people are utilizing services.

In Table 1.1, I made my own version of the popular NVT (Network Value to Transaction) ratio. My custom metric measures network utility by calculating the 7-day average of 24-hour transactional volume subtracted by 7-day average of trading volume on Uniswap.org and Sushiswapclassic.org. I subtracted DEX trading volume because it does not represent utility. However, trading volume can represent utility on Ethereum only if trading volume is done on the Ethereum blockchain. Transactional volume can represent utility, for example, sending tokens to a staking contract to support the network. Transactional volume could account for transfer of assets, staking, collateralizing, lending, and providing liquidity of assets. All of which provide some sense of utility to an extent. One can take this a step further by also including transactions that add/remove liquidity from liquidity pools on DEXs. Participating in Liquidity Pools can account for utility to an extent.

In Table 1.1, High Activity Whale Concentration is a metric the tracks wallets with an excessive amount of said tokens and has over 300 transactions per wallet. I’ve made sure to sift out wallets that are used for governance, staking, vault strategies etc. to get a clearer picture of whales that are actively trading. A high concentration is bad because the potential sell pressure is larger than if the concentration was low. This metric and the holding period metrics can be used in tandem to reflect potential sell pressure. However, there are some downsides to this metric’s accuracy because at any time, tokens can be withdrawn from a vault and sold on the open market within minutes. A low concentration is ideal because it could mean less potential sell pressure or, more utility in vaults, LP pools, staking etc. Both of which are good for price discovery and potential upside.

The exchange supply vs circulating supply metric in Table 1.1 can show us supply shortages. I calculated this by tracking exchange wallets but also subtracting the liquidity pools on Uniswap and Sushiswap. I chose to include DEX liquidity pools, because it can take only a couple minutes to withdraw liquidity from liquidity pools to sell assets. My purpose of this metric is measure potential sell pressure by allocating all assets that are and can be sold on the market right now. The lower the percentage, the lower the supply, the greater the potential upside.

TVL or Time Value Locked can be used a measure of utility. The TVL/MCAP ratio in Table 1.1 considers the size of the project relative to network utility. Some projects are not intended to have liquidity pool incentives or vault strategies. For example, the amount of REN tokens staked to run a Ren Darknode can be used in substitution of a TVL.

Lastly, December Distribution vs Exchange Supply is a representation of tokens that were previously locked but will enter circulation in December. This is usually tokens owned by investors and the team. A December distribution would refer to the token release schedule. This is different for every project. Similarly, FDV or fully diluted valuation can be used too. A token release schedule can hint at when exchange supply would be increased due to profit taking from founders, team, seed investors etc. Technical Analysis R/R ratio is a risk reward ratio based solely on TA. This can will be different amongst all traders dependent on risk appetite.

Table 1.2 illustrates comparable scores of metrics

Given all these metrics, in Table 1.2 I gave each token a score based on how it compares relative to competing projects in this study. I gave each metric category a weight below in Table 1.3. This weight is based on how important I feel this metric to be, therefore can be subject to bias. Since TA is relative to the traders risk appetite, I made the weight very low

Table 1.3; Weight distribution and Fermi estimation scores

In Table 1.3 I tallied total probability scores. The higher the score, the greater likelihood of potential upside in price. To take this a step further, one could represent these scores as ‘z-scores’ and compare z-scores and standard deviation to see how utility deviates with the mean to represent how undervalued a project can be relative to utility value only. However due to the length of this study, I have decided to withdraw this idea.

Below in Table 1.4, I used the probability scores to represent ideal weights of each token in asset allocation. In other words, using probability and statistics to determine a well constructed portfolio. Leverage was not used for AKRO due to low trading volume.

Table 1.4 represents an ideal risk determined portfolio allocation.

To sum up my findings, Akropolis would have the highest chance of seeing upside versus the other projects listed. This does not mean that Akropolis would have the highest gains out of all other projects, but it does tell us how undervalued Akropolis is. According to my study, I found that REN is the most fairly priced at the moment. Could this mean REN is overvalued compared to the others? This is possible, but given the market conditions, this can tell us that REN is most likely in consolidation. Given no fundamental changes, REN is most likely to see the least volatility. However, my metrics can change a lot over a given time period so as I am writing this, metrics could have changed. Because of bearishness on Bitcoin Dominance, an ideal trading strategy would consist of longs on the above assets in respect of their weights, paired with a small bet on select microcap Uniswap “shitcoins”. Why micro caps? This is because micro caps tend to outperform alt coins in at the peak of the alt coin cycle. This can be the equivalent of emerging markets/emerging tech in traditional finance. All positions could be hedged with put options on Bitcoin for insurance if Bitcoin decides to dump. If Bitcoin does dump, alt coins would dump as well but not as much over a longer time frame. This is under the assumption that Bitcoin dominance will fall in value.

Technical Analysis and Charts: