in this dashboard I want to analyze the top 2 platform from NEAR and ETH.

as you see in the NEAR protocol the platform that has the most transaction is v2-ref-finance-near, and as you see the percentage of that is over 97%.

and in the ETH the most transaction related to the uniswap-v2 platform and as you see the percentage of that is over 70 %.

so now I choose the platform and lets explain the dashboard.

after this part it is time to the user number of each platform.

about dashboard

in this dashboard I choose two blockchain,

one of them is ETH and the other one is NEAR.

and after this part I choose the top platform from DEX and start to explain them

and after that search about the:

• user number
• transaction number
• amount in
• amount out
• top token that in to each one
• top token that out from each one

and I want to note that I said

uni for the uniswap-v2

ref for the v2-ref-finance-near

in this dashboard.

in this part I want to explain about the user number of each platform.

as you see most of the user related to the uni but we should not ignore the ref.

as I said most of the user number is for the uni and we see that both of the platform increase and decrease at the same time and at this time we can say that in the user number platforms performance is too close to each other.

and if we want to speak about the time we can say that user number from 3 Jun started to increase up to now and as you see from the first of the year up to the 3 Jun this number decrease.

after this part I want to explain about the transaction number.

lets start.

as you see in this part I want to explain about the transaction number.

as you see again in this part we see that most of the transaction related to the uni and low number of them related to the ref, as you know we expected this because ETH platform is stronger that NEAR but you see that NEARs platform is not deficient.

so as you see transaction number started to decrease up to the 3 Jun and after that transaction number started to increase up to now, and we know the same information from previous part, so we can say that user number and transaction number impact to each other, because both of them had the same reaction by the time.

and I want to add that both platform had the same reaction by the time, as you see both of them at the most of the time increase and decrease at the same time, and we can say that impact to each other, but I want to add this point that this sentence that in all part like each other is false and we can just say in the most of the time performance of the is look like each other.

so after this two part lets explain about the amount.

so it is time to the amount.

as you see again we see that most of the amount is related to the uni but as you see in some day amount that input to the ref is more than uni and you can see that in the 21 Sep the amount of the ref 3.5 time more than uni.

if we want to make example for the better performance of the ref from uni I can say in first of the year up to the 10 Mar amount of the ref is 1.5 time more than uni in the most of the time, but as you see in the total view uni amount is 2 time more than ref.

I want to note that I choose the more or less time from the line of the chart you can get more information from that.

after this part I want to explain about the amount that out from the platforms, lets start.

in this part I want to analyze the amount that out from each platform.

as you see we can say the same thing of the previous part, in this part with just some edit.

as you see again most o the amount is related to the uni and as you see in some day again ref performance got better than uni but in the total view uni is too better than ref.

and if we want to speak about the difference of this chart from previous we can say that in the first of the year amount that out from the ref was low than in amount.

and as you see for the similarity of them we can say that in the 21 Sep both of the ref was in their max and this number was 3.5 time more than uni.

so lets explain the token in to the platform and token out from the platform.

in this part I want to speak about the token that in to the platform.

and I want to take part for two part,

ETH:

in the platform of the ETH(uni) as you see most of the percent related to the WETH with the high percent.(WETH), and as you see the stable coin is after that with the order USDC, USDT and DAI and as you see we can say that all of them have the close percent to each other.

NEAR:

in this platform as you see like previous part WNEAR has the top percent of the transaction with the close to 55 %.

and after that USDT, USDC and DAI are with the 17.5, 10.9 and 7.62%.

we can see that in the ETH most of the user prefer USDC to USDT but in the NEAR we see that user prefer USDT to USDC.

so in the final step lets analyze the token that out from platform.

conclusion

as you know in this dashboard I found the top platform in each blockchain and as you know ref has the most transaction number in the near and uni had the most transaction number from the ETH.

and after this as you see I found the user number, transaction number , amount that in / out -- to/from platforms

and get some result and they are:

• uni users are more than refs.
• in the user number graph, both of the ref and uni have the same reaction to the market, it means most of the reaction of the platform(increase and decrease) are same to each other.
• in the user number graph ,in the both part, number started to decrease from the first of the year up to the 3 Jun and after this date again start to increase up to now.
• in the transaction number again we see that reaction of the transaction related to he user number and both of them have the same reaction by the time.
• in the amount in part, most of the amount is related to the uni but in some day amount that input to the ref is more than uni and in the 21 Sep the amount of the ref 3.5 time more than uni.
• in the amount out part, is look like the in part , but with the low difference that was in the first of the year amount of out is low than in part.
• in the token in part, we see that most of the amount in the ETH part related WETH and in the NEAR part the most of the amount related to the WNEAR. and this action is the same in the token out part.
• in the token in part, in the ETH part user of the ETH prefer to the use USDC to USDT. and in the NEAR part user of the NEAR prefer use USDT to USDC. again this action is the same in the token out part.

thank you because of your attention.

in the previous part we speak about the top token that swap to the NEAR and ETH in the uni and ref platform, and in this part we want to explain the opposed of that.

as you see the order of the token is look like the previous part but the percentage of them is different. so we can say

ETH:

in the platform of the ETH(uni) as you see most of the percent related to the WETH with the high percent.(WETH), and as you see the stable coin is after that with the order USDC, USDT and DAI and as you see we can say that all of them have the close percent to each other.

NEAR:

in this platform as you see like previous part WNEAR has the top percent of the transaction with the close to 51 %.

and after that USDT, USDC and DAI are with the low increase in the percentage be 19.4, 17.3 and 8.36%.

and as you see again in this part user of the ETH prefer USDC to USDT and in the NEAR part this action is opposed.

explain--uniswap-v2

Uniswap V2 implements new functionality that enables highly decentralized and manipulation-resistant on-chain price feeds. This is achieved by measuring prices when they are expensive to manipulate, and cleverly accumulating historical data. This allows external smart contracts to create gas-efficient, time-weighted averages of Uniswap prices across any time interval.

On-chain price feeds are a critical component for many decentralized financial applications including those similar to derivatives, lending, margin trading, prediction markets and more.

most of the time, Uniswap V1 cannot be used safely as a price oracle because the price can move significantly in a short period of time.

Uniswap V2 includes a number of improvements for price feeds built on top of it. First, every pair measures (but does not store) the market price at the beginning of each block, before any trades take place. This price is expensive to manipulate because it was set by the last transaction in a previous block.

To set the measured price to one that is out of sync with the global market price, an attacker has to make a bad trade at the end of a previous block , typically with no guarantee that they will be able to arbitrage it back in the next block. Attackers will lose money to arbitrageurs, unless they can "selfishly" mine two blocks in a row.

This alone is not enough. If significant value settles based on the price resulting from this mechanism, then the profit of an attack likely can outweigh the loss.

Instead, Uniswap V2 adds this end-of-block price to a single cumulative-price variable in the core contract weighted by the amount of time this price existed. This variable represents a sum of the Uniswap price for every second in the entire history of the contract.

A few notes:

• For a 10-minute TWAP, sample once every 10 minutes. For a 1-week TWAP, sample once every week.
• For a simple TWAP, the cost of manipulation increases (approx. linear) with liquidity on Uniswap, as well as (approx. linear) with the length of time over which you average.
• Cost of an attack is relatively simple to estimate. Moving the price 5% on a 1-hour TWAP is approximately equal to the amount lost to arbitrage and fees for moving the price 5% every block for 1 hour.

How Ref Finance Works

Ref Finance is an open-source software.

Inspired by Uniswap v2 and Curve Finance, Ref Finance is an automated liquidity protocol powered by two constant product functions:

• Swap function: x * y = k
• StableSwap function: χDn−1 * ∑ xi + ∏ xi = χDn + ( D / n )n
• StableSwap function for yield-bearing tokens (Rated pools)

Ref Finance is implemented on the NEAR blockchain. The platform is fully permissionless and removes the need for trusted intermediaries, prioritising decentralisation and censorship resistance. Anyone can trade and/or become a liquidity provider (LP) for a pool by depositing an equivalent value of each underlying token in return for pool tokens (LP tokens). These tokens track pro-rata LP shares of the total reserves, and can be redeemed for the underlying assets at any time.

The Ref Finance ecosystem is primarily comprised of five types of users: traders, liquidity providers, stakers, voters and developers.

• Liquidity providers are incentivised to provide these tokens to liquidity pools
• Stakers are receiving pro-rata shares of the shared protocol revenue
• Voters can participate in the governance of the project and the allocation of liquidity incentives
• Developers can integrate directly with Ref Finance smart contracts to empower users in their interactions with tokens, trading interfaces, trading strategies, and more