Question

The NEAR Foundation is running an "Insight of the Week" series. Keeping your analysis short and focused on excellent-quality visualization - provide the most fascinating or illuminating fact or insight that you can about the NEAR ecosystem, or any of the projects building on NEAR, over the past 7 to 14 days.

When you tweet about the insight, don't just describe it - do your best to explain "why" what you chose is unusual, valuable, or noteworthy.

If you're stumped, don't worry - we'll be running this type of bounty again in the future, so feel free to use this as a "test case" to begin thinking creatively and searching out clever insights.

Introduction

• what is NEAR ?

  NEAR Protocol is a decentralized application (dApp) platform and Ethereum competitor that focuses on developer and user-friendliness. Its native NEAR tokens are used to pay for transaction fees and storage on the Near crypto platform. NEAR is a Proof-of-Stake blockchain that uses sharding technology to achieve scalability.

  NEAR Protocol is a smart contract capable, public Proof-of-Stake (PoS) blockchain that was conceptualized as a community-run cloud computing platform. Built by the NEAR Collective, NEAR was designed to host decentralized applications (dApps), and strives to compete with Ethereum and other leading smart contract-enabled blockchains like EOS and Polkadot. NEAR’s native token is also called NEAR, and is used to pay for transaction fees and storage. NEAR tokens can also be staked by token holders who participate in achieving network consensus as transaction validators.

  NEAR Protocol is focused on creating a developer and user friendly platform. To accommodate this mission, NEAR has incorporated features like human-readable account names as opposed to only cryptographic wallet addresses, and the ability for new users to interact with dApps and smart contracts without requiring a wallet at all.

  Projects building on NEAR include Mintbase, a non-fungible token (NFT) minting platform, and Flux, a protocol that allows developers to create markets based on assets, commodities, real-world events, and more.[source]

• what is swap?

  A swap is a derivative contract through which two parties exchange the cash flows or liabilities from two different financial instruments. Most swaps involve cash flows based on a notional principal amount such as a loan or bond, although the instrument can be almost anything. Usually, the principal does not change hands. Each cash flow comprises one leg of the swap. One cash flow is generally fixed, while the other is variable and based on a benchmark interest rate, floating currency exchange rate, or index price.

  The most common kind of swap is an interest rate swap. Swaps do not trade on exchanges, and retail investors do not generally engage in swaps. Rather, swaps are over-the-counter (OTC) contracts primarily between businesses or financial institutions that are customized to the needs of both parties.[source]

• what is staking?

  staking is a way of earning rewards for holding certain cryptocurrencies. If a cryptocurrency you own allows staking — current options include Ethereum, Tezos, Cosmos, Solana, and Cardano — you can “stake” some of your holdings and earn a percentage-rate reward over time. The reason your crypto earns rewards while staked is because the blockchain puts it to work. Cryptocurrencies that allow staking use a “consensus mechanism” called Proof of Stake, which is the way they ensure that all transactions are verified and secured without a bank or payment processor in the middle. Your crypto, if you choose to stake it, becomes part of that process.[source]

Methodology

in this dashboard the following methods are used:

1. analyze transactions

   the purpose of this part is to follow the number of transactions through the past 14 day. at the first there is total number of parameters then there is overtime change of these parameters. at last by categorizing users wanted to help you to find the behavior of users then find the top users that have most number of transactions in past 14 days.

   by using near.core.fact_transactions table the transactions data had extracted.

2. analyze stake and unstake

   in this section, at the first, I want to compare the staking and unstaking action in the past 14 days by following parameters overtime then their total value. after that tried to find the top users that have most amount of staking in the past 14 dasy then categorizing users by amount of staking.

   It used near.core.dim_staking_actions table to find the data about staking and unstaking.

3. analyze swap

   the purpose of this part is to follow up the trend of swaps in the past 14 days. at the first tried to find the total and over time of some parameter s in the past 14 days, then categorizing users by swaps volume and finding top usrs that have most swaps volume. at the last section of this part tried to figure out the relation between NEAR token price and swap between NEAR and USDT.

   It used near.core.fact_prices table to find the price of tokens.

   It used near.core.dim_token_labels table to find DECIMALS of tokens.

   It used near.core.ez_dex_swaps table to find the details of swaps.

Part 1 : analyze transactions

• at this section you can find the detail of transactions that done in the past 14 days. at the first there is total number of parameters then there is overtime figure of parameters then tried to categorized users and then find top ten users that have most number of transactions in the past 14 days.

Part 2 : analyze stake and unstake

• in this part, at the first, I want to follow up the trend of staking and unstaking actions overtime. then compare total value of staking and unstaking then finding top users then categorizing users by amount of staking.

Part 3 : analyze swap

• in this part you will see the total and overtime of some parameters related to swap actions then tried to categorize users by swaps volume and finding top users that have most swaps volume. at last tried to find the effect of NEAR token price on swap actions between NEAR and USDT.

Observations 7

• at the top there are some figure that shows trend of Near price and number of swaps between NEAR and USDT . by attention to this figures can see, when the price of near has a significant change, the swaps volume between NEAR and USDT had a significant change in the past 14 days.
• total number of swaps from NEAR to USDT is more than total number of swaps from USDT to NEAR in the past 14 days.
• total number of swappers from USDT to NEAR is more than total number of swappers from NEAR to USDT in the past 14 days.
• total swaps volume from USDT to NEAR is more than total swaps volume from NEAR to USDT in the past 14 days.

\

• Thanks for reading my dashboard, please share your insight about it with me :grinning:
• link of tweet that I shared my dashboard : Link

Observations 1

• total number of transactions in the past 16 days is 6.06M.
• total number of success transactions in the past 14 days is 5.52M.
• total number of failed transactions in the past 14 days is 540k.
• total number of unique users in the past 14 days is 590k.
• the ratio of success transactions to total number of transactions is 0.91 so it shows more than 90% of transactions will be success.
• at Jan 24, 2023 most number of transactions had done in the past 14 days, it is 466.38k.
• at Jan 28, 2023 minimum number of transactions had done that is 393.46k in the past 14 days.
• overall the number of transactions does not have a significant change in the past 14 days.
• at Jan 24, 2023 most number of unique users have done transaction in the past 14 days, it is 83.73k.
• at Jan 31, 2023 minimum number of unique users have done transaction that is 61.3k in the past 14 days.
• overall number of unique users that have done transaction does not have a significant change in the past 14 days.

Observations 2

• by categorizing users by number of transactions, can see that most number of users have between 1 and 5 transactions in the past 14 days, it is 261.56k and 44.2% of total users that made transaction in the past 14 days. at the second stage the most number of users have one time transaction that is 216.18k and 36.6% of total users that made transaction in the past 14 days.

• the oracle.sweat had most number of transactions in the past 14 days that is 660.66k. by attention to the left figure you can see top ten users that have most number of transactions in the past 14 days.

Observations 3

• as we can see from overtime figures, the trend of number of stake and unstake is decreasing until Jan 28 then is decreasing.
• the number of unique users that staked or unstaked is like number of stake and unstake, it wa decreasing until Jan 28 then it is increasing.
• the amount of staked and unstaked does not have a stable trend, but the minimum amount that staked is at Jan 26and the maximum of that is at Feb 1.

Observations 4

• by attention to the top figure you can undrstand that tried to compare staking and unstaking in the past 14 days. the result of this comparison shows that the total number of staked, total number of users that staked and total amount that staked is more than unstaked.
• by analyzing top ten users can see that the d5cc0633e6bccfe417130c8c1bf9710f7ef024a2f95a396eee8dd4f61e477cdc has most amount of staking that is 1.03M in the past 14 days..
• for the last part of this section I tried to categorize users by amount of staking in the past 14 days. the results shows that most number of users staked amount between 10 and 100 near that is 1416 and 39.2% of total users that staked in the past 14 days.

Observations 5

• total number of swaps in the past 14 days is 6.77M.
• total number of swappers un the past 14 days is 2750.
• total swaps volume in the past 14 days is 6.68M $.
• the number of swaps has two increasing and decreasing trends and in Feb had decreasing trend.
• swaps volume in Feb has a significant decrese than Jan and has a decreasing trend in Feb.

Observations 6

• by categorizing users by swaps volume(usd) can see the most of swappers had swaps volume less than 5$ that is 2521 and 97.8% of total swappers. in the second stage the most number of swappers had swaps volume between 1k and 10k in the past 14 days, it is 18 .

• the left figure shows the top ten swappers that have most swaps volume in the past 14 days ago. the had most swaps volume(usd) in the past 14 days, it is 3.6M $.

Conclusion

• the number of transactions have a normal trend in the past 14 days ago. more than 90% of transactions were success.
• by analyzing staking and unstaking actions can conclude that in most users like to stake their tokens
• by analyzing swap actions can see the swaps volume in the Feb had a significant drop.
• in swap section tried to analyze the effect of NEAR token price on swap action, from figures can conclude a change in NEAR token price make a bigger change in swap parameters.
• the number of swappers from NEAR to USDT is more than USDT to NEAR, as a interested result can see the number of swaps from NEAR to USDT is more than USDT to NEAR so can conclude the swappers from NEAR to USDT have more number of swaps averagely than swappers from USDT to NEAR.