How To Identify Potential Investment Opportunities With DappRadar

With hundreds of blockchains and decentralized apps (dapps) listed on CoinGecko, it can be overwhelming to determine which ones are worth researching and/or investing in.

Instead of looking frantically at price charts, randomly throwing darts at a board, or (god forbid) acting on our feelings, we should carefully analyze these blockchains/dapps to determine which ones are growing, and which ones are withering away.

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We wrote previously about helpful sources of information (like X and Discord) that you can use to keep yourself apprised of blockchain/dapp developments. In addition to info from social media platforms, a website like DappRadar can provide you with even more insights.

Analyzing Blockchains Using DappRadar

For example, it would be helpful to know which blockchains are getting the most usage, and which specific dapps are attracting new users and generating transactions.

By constantly polling transparent data from a variety of public blockchain nodes, DappRadar is able to generate statistics and charts that we can use to monitor dapp activity.

In this post, we are going to rank blockchains by unique active wallets (UAW), and drill down into specific dapps from there.

Ranking Blockchains Based On Active Users

Head on over to chain rankings on DappRadar and set the time-frame to 30d to get the most comprehensive data.

Notice that the top column allows you to sort the blockchains by UAW, total transactions, and total dapp volume among other categories. Click on Total UAW to sort the chains by active unique wallets. This should give us an idea as to which chains have the most active users.

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Currently the top three blockchains ranked by unique active wallets (over the past 30 days) are opBNB, NEAR, and Solana. For demonstration purposes, let's dig deeper into NEAR's data.

Checking Blockchain Activity

Click on NEAR, scroll down to historical activity, and select the 1y time-frame.

If we select only the UAW and Transactions, we will see that both have been steadily increasing over the past year (aside from a sharp decline near the end, which warrants further investigation).

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At first glance, it would seem that the NEAR ecosystem has been growing over the past year.

Next, we want to know which specific NEAR dapps are attracting users and generating transactions.

Checking Dapp Activity

Scroll down further to Topp Dapps and select the 30d time-frame. Here you will see that the top three dapps over the past 30 days are KAI-CHING, Hot Game, and Sweat Economy.

Click on Sweat Economy, scroll down to historical activity, and select the 1y time-frame. You will see that both UAW and Transactions in the Sweat Economy dapp have been steadily increasing throughout the year.

sweat_historic_activity.png

At this point, you could overlay the chart of Sweat Economy's activity with its price chart. If the number of active wallets and transactions is increasing while the token price is falling or remains stagnant, it might indicate a possible investing opportunity.

This has just been a simple example of how you can use DappRadar's statistics and charts to analyze a particular dapp's historic activity. You can now experiment with ranking dapps by other categories like volume, transaction count, and total value locked (TVL).

Pitfalls

At this time, DappRadar appears to be limited to tracking only certain blockchains. For example, data seems to be missing on Cosmos and Polkadot chains.

We also need to consider that some of these transactions could just be spam, and that some of the active wallets could be nothing but bots.

That said, these charts give us a general idea as to whether or not a blockchain/dapp is increasing in popularity, and might be worth further investigation.

If you learned something new from this article, be sure to check out my other posts on crypto and finance here on the HIVE blockchain. You can also follow me on InLeo for more frequent updates.

Until next time...

Resources

Screenshots from DappRadar [1]
Image generation courtesy of Venice AI [2]

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