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476 lines
17 KiB
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476 lines
17 KiB
Plaintext
---
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title: "Understanding MKR spread for Delegate Targeting"
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author: "Carlos Mercado"
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date: '2022-08-30'
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output:
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html_document:
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code_folding: hide
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editor_options:
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chunk_output_type: console
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markdown:
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wrap: 72
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---
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# Intro
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Flipside's governance team works deeply with the MakerDAO team to
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create, discuss, and vote on proposals that ultimately improve Maker's
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market position and revenue model. In order to increase our influence at
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Maker, we seek delegation of MKR to our voting address.
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This markdown details use of the new Flipside `ethscore` package to
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identify potential addresses to target to request/earn delegation of
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their MKR to our voting address.
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# Package Requirements
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ethscore uses shroomDK to access Flipside data for its analysis. The
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best way to install these packages is via devtools install_github().
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```{r, eval = FALSE, message = FALSE, warning= FALSE}
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# This chunk does not eval
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# library(devtools) # install if you haven't already
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# devtools::install_github(repo = 'FlipsideCrypto/sdk', subdir = 'r/shroomDK')
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# devtools::install_github(repo = 'FlipsideCrypto/ethscore')
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```
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# Addressable Market of MKR Delegation
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Not all holders of an ERC20 are externally owned accounts (EOAs). Some
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are contract addresses, others are gnosis-safes. Among EOAs, there are
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central exchange managed EOAs for coordinating deposits and withdrawals
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on and off chain which would be inappropriate targets for delegation.
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Also some EOAs are 'cold storage' in that they hold a balance but have
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never initiated a transaction. If an EOA has never done a transaction,
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it is unlikely its first will be delegation of a token which requires
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approvals and other contract interactions that the user may find risky.
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Thus, for the purposes of growing our delegation, it is imperative we
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understand the addressable market as:
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- EOAs that are active and likely human owned
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- Gnosis safes, e.g., DAO multi-sigs.
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- MKR in the delegate contract(s)
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- Note: this is 'pvp' in that each MKR we are delegated from this contract is explicitly a MKR taken from another delegate. While we support competition, our first goal is to activate more MKR, not simply fight over a fixed pool.
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## Current balance of MKR held by those with 1+ MKR
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'Dust' is common in crypto. Users make swaps of non-integer sizes and
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end up with balances that use many of the 18 decimals permitted by
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ERC20s, e.g., having 0.0042069 MKR (\~ \$3 at time of writing). This
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naturally inflates the 'holders' number we commonly see in tools like
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etherscan.
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```{r, message = FALSE, warning= FALSE}
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library(shroomDK)
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library(ethscore)
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library(scales)
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library(gmp)
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library(dplyr)
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library(reactable)
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library(plotly)
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mkr <- tolower("0x9f8f72aa9304c8b593d555f12ef6589cc3a579a2")
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total_supply <- 977631
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total_holders <- 87789
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max_block <- 15440000 # August 30th 11AM UTC
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api_key <- readLines("api_key.txt") # get a free key @ https://sdk.flipsidecrypto.xyz/shroomdk
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mkr_balances <- address_token_balance(token_address = mkr, min_tokens = 1,
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block_max = max_block, api_key = api_key)
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plot_ly(mkr_balances, x = ~ADDRESS_TYPE, y = ~log(NEW_VALUE), color = ~ADDRESS_TYPE,
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boxpoints = "all", jitter = 0.3,
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hoverinfo = 'text',
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hovertext = ~paste0(
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'Log-MKR Balance: ',
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round(log(NEW_VALUE), 2),'\n Raw MKR Balance: ',
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scales::label_comma()(floor(NEW_VALUE))
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),
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type = 'box') %>%
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layout(title = '\nDistribution of MKR among those with 1+ MKR',
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xaxis = list(title = 'Address Type'),
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yaxis = list(title = 'LOG(MKR Balance)')
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)
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num_active_eoas <- nrow(mkr_balances %>% dplyr::filter(ADDRESS_TYPE == "EOA"))
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mkr_held_by_active_eoas <- mkr_balances %>%
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dplyr::filter(ADDRESS_TYPE == "EOA") %>%
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summarise(sum(NEW_VALUE)) %>%
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as.numeric() %>% floor()
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```
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Of the `r scales::label_comma()(total_holders)` Holders of MKR, only
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`r label_comma()(nrow(mkr_balances))` (
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`r floor(100*nrow(mkr_balances)/total_holders)`%) have at least 1 whole
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MKR (\~\$700 at time of writing).
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Given that MKR uses on-chain voting on the Ethereum Layer 1, it may be
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cost prohibitive in ETH gas terms for smaller holders to delegate and
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vote on-chain (as opposed to an off-chain tool like Snapshot).
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Because holdings of most ERC20s is highly skewed (i.e., most addresses
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have very few MKR and a few have very large amounts) a LOG scale is used
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to more cleanly see differences in the distribution of MKR across
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Address Types.
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The key insights to note:
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- Non-targets like contracts, cold storage EOAs, and central exchange EOAs have a wide variance in their MKR holdings.
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- The top holders of MKR are contracts and central exchange EOAs.
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In practical terms, of the `r label_comma()(total_supply)` MKR total
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supply held by `r label_comma()(total_holders)` holders (Etherscan
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above), there are only `r label_comma()(num_active_eoas)` EOAs active
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and with enough MKR (1+) to be delegate targets. These EOAs hold
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`r label_comma()(mkr_held_by_active_eoas)` MKR, only
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`r floor(100*mkr_held_by_active_eoas/total_supply)`% of supply.
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```{r}
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mkr_smmry <- mkr_balances %>% group_by(ADDRESS_TYPE) %>%
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summarise(num = n(),
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total = scales::label_comma()(floor(sum(NEW_VALUE))),
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avg = scales::label_comma()(floor(mean(NEW_VALUE))),
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median = scales::label_comma()(floor(median(NEW_VALUE))),
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max = scales::label_comma()(floor(max(NEW_VALUE))),
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sd = scales::label_comma()(floor(sd(NEW_VALUE))))
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reactable(mkr_smmry,
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columns = list(
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ADDRESS_TYPE = colDef(name = 'Address Type'),
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num = colDef(name = 'Count', align = 'right'),
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total = colDef(name = 'Total', align = 'right'),
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avg = colDef(name = 'Average', align = 'right'),
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median = colDef(name = 'Median', align = 'right'),
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max = colDef(name = 'Max', align = 'right'),
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sd = colDef(name = 'Standard Deviation', align = 'right')
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))
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```
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```{r}
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mkr_gov <- floor(mkr_balances[
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mkr_balances$ADDRESS == tolower('0x0a3f6849f78076aefaDf113F5BED87720274dDC0'), "NEW_VALUE"]
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)
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mkr_available <- mkr_gov + mkr_held_by_active_eoas
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```
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The MKR governance contract:
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`0x0a3f6849f78076aefadf113f5bed87720274ddc0` held
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`r label_comma()(mkr_gov)` MKR as of block `r label_comma()(max_block)`.
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The Largest contract and overall address holder of MKR.
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This means of the total `r label_comma()(total_supply)` MKR:
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`r label_comma()(mkr_available)`(\~
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`r floor(100*mkr_available/total_supply)`%) is practically available for
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delegation.
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- `r label_comma()(mkr_held_by_active_eoas)` in EOAs w/ 1+ MKR that are not in the governance contract.
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- `r label_comma()(mkr_gov)` in the governance contract whose delegation can be fought over pvp style.
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- Note: excluding the low number of gnosis safes for now.
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At time of writing, Flipside Crypto has \~9,000 MKR delegated to it
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(4.7% of current governance).
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# Time-Weighted MKR Holders
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Instead of analyzing holders based on current balance on MKR, we can add
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weight for *having held* MKR for a long time. For example, weighing an
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address whose held 10 MKR for 100,000 blocks as a better delegate target
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than one who has held 100 MKR for only 1,000 blocks.
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Giving users 1 point per MKR for every 1,000 blocks where they held at
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least 0.1 MKR in the range of Jan 1, 2021 (block #:11,566,000) to Aug
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30th 11am UTC (block \# 15,440,000) it is clear there is a strong
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correlation between holding MKR now and having held it in the past- but
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important outliers and nuance allow for more precision in targeting
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potential delegates.
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```{r, message = FALSE, warning= FALSE}
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min_block <- 11566000
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mkr_timeweighted <- address_time_weighted_token_balance(mkr,
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min_tokens = 0.1,
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block_min = min_block,
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block_max = max_block,
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amount_weighting = TRUE,
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api_key = api_key
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)
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plot_ly(mkr_timeweighted, x = ~ADDRESS_TYPE, y = ~log(TIME_WEIGHTED_SCORE),
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color = ~ADDRESS_TYPE,
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boxpoints = "all", jitter = 0.3,
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hoverinfo = 'text',
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hovertext = ~paste0(
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'Log-MKR TW Score: ',
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round(log(TIME_WEIGHTED_SCORE), 2),
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'\n Raw MKR TW Score: ',
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scales::label_comma()(floor(TIME_WEIGHTED_SCORE))
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),
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type = 'box') %>%
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layout(title = '\nDistribution of MKR Time Weighted Scoring',
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xaxis = list(title = 'Address Type'),
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yaxis = list(title = 'LOG(MKR TW Score)')
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)
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# merging and imputing current balance 0 to compare tw score and balance
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mkr_tw_bal <- merge(x = mkr_timeweighted[, c("ADDRESS","TIME_WEIGHTED_SCORE","ADDRESS_TYPE")],
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y = mkr_balances[,c("ADDRESS","NEW_VALUE")],
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all.x = TRUE, by = "ADDRESS")
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mkr_tw_bal$NEW_VALUE[is.na(mkr_tw_bal$NEW_VALUE)] <- 0
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plot_ly(mkr_tw_bal, x = ~log(NEW_VALUE), y = ~log(TIME_WEIGHTED_SCORE),
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color = ~ADDRESS_TYPE, type = 'scatter',
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hoverinfo = 'text',
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hovertext = ~paste0(
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'RAW-MKR Balance: ',
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scales::label_comma()(floor(NEW_VALUE)),
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'\n Raw MKR TW Score: ',
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scales::label_comma()(floor(TIME_WEIGHTED_SCORE))
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)
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) %>%
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layout(title = '\n Current MKR Balance vs Time-Weighted Score',
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xaxis = list(title = 'LOG(Current MKR Balance)'),
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yaxis = list(title = 'LOG(MKR TW Score)')
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)
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```
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# Address Selection
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```{r, warning=FALSE, message=FALSE}
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select_eoas <- mkr_tw_bal %>% dplyr::filter(ADDRESS_TYPE == "EOA",
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TIME_WEIGHTED_SCORE >= 10000,
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NEW_VALUE >= 50)
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plot_ly(select_eoas, x = ~NEW_VALUE, y = ~TIME_WEIGHTED_SCORE,
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color = ~ADDRESS_TYPE, type = 'scatter',
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hoverinfo = 'text',
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hovertext = ~paste0(
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'RAW-MKR Balance: ',
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scales::label_comma()(floor(NEW_VALUE)),
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'\n Raw MKR TW Score: ',
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scales::label_comma()(floor(TIME_WEIGHTED_SCORE))
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)
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) %>%
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layout(title = '\n Current MKR Balance vs Time-Weighted Score',
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xaxis = list(title = 'Current MKR Balance'),
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yaxis = list(title = 'MKR TW Score')
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)
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```
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Subsetting to target EOAs (i.e., not central exchange associated nor
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cold storage) with at least 50 MKR and a time-weighted score of 10,000
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(equivalent of holding 100 MKR for 100,000 blocks) results in
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`r nrow(select_eoas)` MKR holders. Unlike previous visuals, the above
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visual is *not* LOG adjusted, so the skew in both amount and time held
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is very noticeable.
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To assess fitness for direct outreach, the number of transactions, days
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active, and last transaction date are pulled for the select EOAs.
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```{r, warning = FALSE, message = FALSE}
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query <- {
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"
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with select_tx AS (
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SELECT BLOCK_TIMESTAMP, TX_HASH, FROM_ADDRESS as ADDRESS FROM ethereum.core.fact_transactions
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WHERE FROM_ADDRESS IN ('ADDRESSLIST') AND
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BLOCK_NUMBER >= _MIN_BLOCK_ AND
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BLOCK_NUMBER <= _MAX_BLOCK_
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)
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SELECT ADDRESS, COUNT(*) as num_tx,
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count(DISTINCT(date_trunc('DAY', block_timestamp))) as num_days,
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MAX(block_timestamp) as last_tx_date FROM
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select_tx
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GROUP BY ADDRESS
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"
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}
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alist <- paste0(select_eoas$ADDRESS, collapse = "','")
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query <- gsub('ADDRESSLIST', replacement = alist, x = query)
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query <- gsub('_MIN_BLOCK_', replacement = min_block, x = query)
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query <- gsub('_MAX_BLOCK_', replacement = max_block, x = query)
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select_stats <- auto_paginate_query(query, api_key)
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eoa_stats <- merge(select_eoas, select_stats, by = 'ADDRESS')
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eoa_stats$LAST_TX_DATE <- as.Date(eoa_stats$LAST_TX_DATE)
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final_eoa <- eoa_stats %>% dplyr::filter(
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NUM_TX < 100000,
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NUM_DAYS <= 600,
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NUM_DAYS >= 5,
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LAST_TX_DATE > (max(eoa_stats$LAST_TX_DATE) - 180)
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)
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```
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Some noticeable issues are present among this selection:
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- `r sum(eoa_stats$NUM_TX >= 100000)` EOAs with 100,000+ Transactions
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(bots?).
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- `r sum(eoa_stats$NUM_DAYS >= 600)` EOAs active nearly every single
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day in time period (bots?).
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- `r sum(eoa_stats$NUM_DAYS <= 5)` EOAs active less than 5 days in
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time period.
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- `r sum(eoa_stats$LAST_TX_DATE < (max(eoa_stats$LAST_TX_DATE) - 180))`
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EOAs inactive for last 6+ months.
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Subsetting to addresses with \< 100,000 tx; \< 600 days active; at least
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5 days active; and active within the last 6 months results in
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`r nrow(final_eoa)` addresses holding a total of
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`r label_comma()(sum(final_eoa$NEW_VALUE))` MKR.
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# Address Contact
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Address to address communication for direct outreach is still immature.
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To identify potential willingness to be contacted and pitched
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delegation, two outreach avenues are identified:
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- The Ethereum Name Service of each address (if available) is pulled to see if any addresses willingly doxx their
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social media (e.g., by having their ENS in their Twitter name).
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- Governance activity in other DAOs (i.e., Snapshot votes) for potential reach out via DAO partnerships.
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## ENS
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Using ENS NFT address `0x57f1887a8bf19b14fc0df6fd9b2acc9af147ea85` a one
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to many test of NFT ownership is identified.
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```{r, warning = FALSE, message = FALSE}
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# add these to ethscore lol
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hex_to_bigint <- function(x = "0x5ae081a11c9e42983640ad6d4c5b2fa9dd0a0b886e1def6057c0037c33d1bba5"){
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as.character(as.bigz(x))
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}
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bigint_to_hex <- function(x = "41104824783848331047501863836715107956672917465157448818057950770477717896101"){
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fill_hex <- function(x){
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if(nchar(x) == 65){
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x <- gsub("0x","0x0",x)
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return(x)
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}
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return(x)
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}
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hx <- paste0("0x", as.character(as.bigz(x), b = 16))
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hx <- unlist(lapply(hx, fill_hex))
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return(hx)
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}
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ens_nfts_query <- {
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"
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SELECT BLOCK_NUMBER, NFT_TO_ADDRESS as ADDRESS, TOKENID FROM ethereum.core.ez_nft_transfers
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WHERE NFT_ADDRESS = '0x57f1887a8bf19b14fc0df6fd9b2acc9af147ea85' AND
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NFT_TO_ADDRESS IN ('ADDRESSLIST')
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"
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}
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addresslist <- paste0(final_eoa$ADDRESS, collapse = "','")
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ens_nfts_query <- gsub("ADDRESSLIST", addresslist, ens_nfts_query)
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ens <- auto_paginate_query(ens_nfts_query, api_key)
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ens$HEX_TOKENID <- bigint_to_hex(ens$TOKENID)
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ens_query <- {
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"
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SELECT DISTINCT
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event_inputs:name :: STRING as ens_name,
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event_inputs:label :: STRING as hex_tokenid
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from ethereum.core.fact_event_logs
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where
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hex_tokenid IN ('HEXLIST')
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"
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}
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hexlist <- paste0(ens$HEX_TOKENID, collapse = "','")
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ens_query <- gsub("HEXLIST", hexlist, ens_query)
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ens_label <- auto_paginate_query(ens_query, api_key)
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ens_label_full <- ens_label[!is.na(ens_label$ENS_NAME), ]
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ens_label_problem <- ens_label[!(ens_label$HEX_TOKENID %in% ens_label_full$HEX_TOKENID), ]
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eoa_nfts <- merge(ens, ens_label_full, by = 'HEX_TOKENID', all.x = TRUE)
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eoa_label <- unique(eoa_nfts[, c("ADDRESS", "ENS_NAME")])
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eoa_with_names <- merge(final_eoa, eoa_label, by = "ADDRESS", all.x = TRUE, all.y = TRUE)
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unique_eoa_with_name <- eoa_with_names %>% filter(!is.na(ENS_NAME))
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```
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Of the `r length(unique(eoa_with_names$ADDRESS))` target EOAs there are
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`r length(unique(unique_eoa_with_name$ADDRESS))` EOAs with at least 1
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ENS. These individuals together have
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`r length(unique(unique_eoa_with_name$ENS_NAME))` ENS names total.
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```{r, warning = FALSE, message = FALSE}
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eoa_name_balance <- eoa_with_names
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eoa_name_balance$has_ENS <- ifelse(is.na(eoa_name_balance$ENS_NAME), "No ENS", "Has ENS")
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eoa_name_balance <- unique(eoa_name_balance[, c("ADDRESS","NEW_VALUE","NUM_TX","NUM_DAYS","LAST_TX_DATE","has_ENS")])
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plot_ly(eoa_name_balance, x = ~has_ENS, y = ~log(NEW_VALUE), color = ~has_ENS,
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boxpoints = "all", jitter = 0.3,
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hoverinfo = 'text',
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hovertext = ~paste0(
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'LOG MKR Balance:',
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round(log(NEW_VALUE), 2),'\n Raw MKR Balance: ',
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scales::label_comma()(floor(NEW_VALUE))
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),
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type = 'box') %>%
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layout(title = '\nDistribution of MKR among target EOAs with and without ENS',
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xaxis = list(title = 'Has ENS'),
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yaxis = list(title = 'LOG(MKR Balance)')
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)
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|
|
|
eoa_name_smmry <- eoa_name_balance %>% group_by(has_ENS) %>%
|
|
summarise(num = n(),
|
|
total = scales::label_comma()(floor(sum(NEW_VALUE))),
|
|
avg = scales::label_comma()(floor(mean(NEW_VALUE))),
|
|
median = scales::label_comma()(floor(median(NEW_VALUE))),
|
|
max = scales::label_comma()(floor(max(NEW_VALUE))),
|
|
sd = scales::label_comma()(floor(sd(NEW_VALUE))))
|
|
|
|
reactable(eoa_name_smmry,
|
|
columns = list(
|
|
has_ENS = colDef(name = 'Has ENS'),
|
|
num = colDef(name = 'Count', align = 'right'),
|
|
total = colDef(name = 'Total', align = 'right'),
|
|
avg = colDef(name = 'Average', align = 'right'),
|
|
median = colDef(name = 'Median', align = 'right'),
|
|
max = colDef(name = 'Max', align = 'right'),
|
|
sd = colDef(name = 'Standard Deviation', align = 'right')
|
|
))
|
|
|
|
write.csv(eoa_with_names, "eoa_w_ens_name.csv")
|
|
write.csv(eoa_name_balance, "unique_eoa_has_ens_or_not.csv")
|
|
|
|
```
|
|
|
|
|
|
## Snapshot
|
|
|
|
To improve identification of addresses, snapshot voting history is assessed to identify
|
|
potential DAO partnerships for boosting delegation.
|
|
|
|
TODO |