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Generalized ENS Complete - SNAPSHOT TODO

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TODO: Snapshot
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Carlos R. Mercado 2022-09-14 17:56:44 -04:00
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.gitignore vendored
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@ -5,3 +5,5 @@
api_key.txt
.DS_Store
MKR_delegate_targeting.html
eoa_w_ens_name.csv
unique_eoa_has_ens_or_not.csv

310
MKR_delegate_targeting.Rmd
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@ -7,21 +7,25 @@ output:
code_folding: hide
editor_options:
chunk_output_type: console
markdown:
wrap: 72
---
# Intro
Flipside's governance team works deeply with the MakerDAO team to create, discuss, and vote on proposals that
ultimately improve Maker's market position and revenue model. In order to increase our influence
at Maker, we seek delegation of MKR to our voting address.
Flipside's governance team works deeply with the MakerDAO team to
create, discuss, and vote on proposals that ultimately improve Maker's
market position and revenue model. In order to increase our influence at
Maker, we seek delegation of MKR to our voting address.
This markdown details use of the new Flipside `ethscore` package to identify potential
addresses to target to request/earn delegation of their MKR to our voting address.
This markdown details use of the new Flipside `ethscore` package to
identify potential addresses to target to request/earn delegation of
their MKR to our voting address.
# Package Requirements
ethscore uses shroomDK to access Flipside data for its analysis. The best way to install these
packages is via devtools install_github().
ethscore uses shroomDK to access Flipside data for its analysis. The
best way to install these packages is via devtools install_github().
```{r, eval = FALSE, message = FALSE, warning= FALSE}
# This chunk does not eval
@ -32,13 +36,17 @@ packages is via devtools install_github().
# Addressable Market of MKR Delegation
Not all holders of an ERC20 are externally owned accounts (EOAs). Some are contract addresses,
others are gnosis-safes. Among EOAs, there are central exchange managed EOAs for coordinating deposits and withdrawals
on and off chain which would be inappropriate targets for delegation. Also some EOAs are 'cold storage' in that they hold a balance but have never initiated a transaction. If an EOA has never done a transaction, it is unlikely its first
will be delegation of a token which requires approvals and other contract interactions that the user may find risky.
Not all holders of an ERC20 are externally owned accounts (EOAs). Some
are contract addresses, others are gnosis-safes. Among EOAs, there are
central exchange managed EOAs for coordinating deposits and withdrawals
on and off chain which would be inappropriate targets for delegation.
Also some EOAs are 'cold storage' in that they hold a balance but have
never initiated a transaction. If an EOA has never done a transaction,
it is unlikely its first will be delegation of a token which requires
approvals and other contract interactions that the user may find risky.
Thus, for the purposes of growing our delegation, it is imperative we understand the addressable market
as:
Thus, for the purposes of growing our delegation, it is imperative we
understand the addressable market as:
- EOAs that are active and likely human owned
- Gnosis safes, e.g., DAO multi-sigs.
@ -47,9 +55,11 @@ as:
## Current balance of MKR held by those with 1+ MKR
'Dust' is common in crypto. Users make swaps of non-integer sizes and end up with balances that use many of the
18 decimals permitted by ERC20s, e.g., having 0.0042069 MKR (~ $3 at time of writing). This naturally inflates
the 'holders' number we commonly see in tools like etherscan.
'Dust' is common in crypto. Users make swaps of non-integer sizes and
end up with balances that use many of the 18 decimals permitted by
ERC20s, e.g., having 0.0042069 MKR (\~ \$3 at time of writing). This
naturally inflates the 'holders' number we commonly see in tools like
etherscan.
![Etherscan screenshot of MKR Holders](mkr_holders_screenshot.png)
@ -57,6 +67,7 @@ the 'holders' number we commonly see in tools like etherscan.
library(shroomDK)
library(ethscore)
library(scales)
library(gmp)
library(dplyr)
library(reactable)
library(plotly)
@ -93,28 +104,29 @@ mkr_held_by_active_eoas <- mkr_balances %>%
```
Of the
`r scales::label_comma()(total_holders)` Holders of MKR, only
Of the `r scales::label_comma()(total_holders)` Holders of MKR, only
`r label_comma()(nrow(mkr_balances))` (
`r floor(100*nrow(mkr_balances)/total_holders)`%)
have at least 1 whole MKR (~$700 at time of writing).
`r floor(100*nrow(mkr_balances)/total_holders)`%) have at least 1 whole
MKR (\~\$700 at time of writing).
Given that MKR uses on-chain voting on the Ethereum Layer 1, it may be cost prohibitive in ETH gas
terms for smaller holders to delegate and vote on-chain (as opposed to an off-chain tool like Snapshot).
Given that MKR uses on-chain voting on the Ethereum Layer 1, it may be
cost prohibitive in ETH gas terms for smaller holders to delegate and
vote on-chain (as opposed to an off-chain tool like Snapshot).
Because holdings of most ERC20s is highly skewed (i.e., most addresses have very few MKR and a few have very large amounts)
a LOG scale is used to more cleanly see differences in the distribution of MKR across Address Types.
Because holdings of most ERC20s is highly skewed (i.e., most addresses
have very few MKR and a few have very large amounts) a LOG scale is used
to more cleanly see differences in the distribution of MKR across
Address Types.
The key insights to note:
- Non-targets like contracts, cold storage EOAs, and central exchange EOAs have a wide variance in their MKR holdings.
- The top holders of MKR are contracts and central exchange EOAs.
In practical terms, of the
`r label_comma()(total_supply)` MKR total supply held by
`r label_comma()(total_holders)` holders (Etherscan above), there are only
`r label_comma()(num_active_eoas)` EOAs active and with enough MKR (1+) to be delegate targets.
These EOAs hold
In practical terms, of the `r label_comma()(total_supply)` MKR total
supply held by `r label_comma()(total_holders)` holders (Etherscan
above), there are only `r label_comma()(num_active_eoas)` EOAs active
and with enough MKR (1+) to be delegate targets. These EOAs hold
`r label_comma()(mkr_held_by_active_eoas)` MKR, only
`r floor(100*mkr_held_by_active_eoas/total_supply)`% of supply.
@ -140,7 +152,6 @@ columns = list(
```
```{r}
mkr_gov <- floor(mkr_balances[
@ -149,32 +160,36 @@ mkr_gov <- floor(mkr_balances[
mkr_available <- mkr_gov + mkr_held_by_active_eoas
```
The MKR governance contract: `0x0a3f6849f78076aefadf113f5bed87720274ddc0` held
`r label_comma()(mkr_gov)` MKR as of block
`r label_comma()(max_block)`. The Largest contract and overall address holder of MKR.
The MKR governance contract:
`0x0a3f6849f78076aefadf113f5bed87720274ddc0` held
`r label_comma()(mkr_gov)` MKR as of block `r label_comma()(max_block)`.
The Largest contract and overall address holder of MKR.
This means of the total
`r label_comma()(total_supply)` MKR:
`r label_comma()(mkr_available)`(~
`r floor(100*mkr_available/total_supply)`%) is practically available for delegation.
This means of the total `r label_comma()(total_supply)` MKR:
`r label_comma()(mkr_available)`(\~
`r floor(100*mkr_available/total_supply)`%) is practically available for
delegation.
- `r label_comma()(mkr_held_by_active_eoas)` in EOAs w/ 1+ MKR that are not in the governance contract.
- `r label_comma()(mkr_gov)` in the governance contract whose delegation can be fought over pvp style.
- Note: excluding the low number of gnosis safes for now.
At time of writing, Flipside Crypto has ~9,000 MKR delegated to it (4.7% of current governance).
At time of writing, Flipside Crypto has \~9,000 MKR delegated to it
(4.7% of current governance).
# Time-Weighted MKR Holders
Instead of analyzing holders based on current balance on MKR, we can add weight for *having held* MKR
for a long time. For example, weighing an address whose held 10 MKR for 100,000 blocks as a better delegate
target than one who has held 100 MKR for only 1,000 blocks.
Instead of analyzing holders based on current balance on MKR, we can add
weight for *having held* MKR for a long time. For example, weighing an
address whose held 10 MKR for 100,000 blocks as a better delegate target
than one who has held 100 MKR for only 1,000 blocks.
Giving users 1 point per MKR for every 1,000 blocks where they held at least 0.1 MKR
in the range of Jan 1, 2021 (block #:11,566,000) to
Aug 30th 11am UTC (block # 15,440,000) it is clear there is a strong correlation
between holding MKR now and having held it in the past- but important outliers and nuance
allow for more precision in targeting potential delegates.
Giving users 1 point per MKR for every 1,000 blocks where they held at
least 0.1 MKR in the range of Jan 1, 2021 (block #:11,566,000) to Aug
30th 11am UTC (block \# 15,440,000) it is clear there is a strong
correlation between holding MKR now and having held it in the past- but
important outliers and nuance allow for more precision in targeting
potential delegates.
```{r, message = FALSE, warning= FALSE}
@ -230,10 +245,10 @@ plot_ly(mkr_tw_bal, x = ~log(NEW_VALUE), y = ~log(TIME_WEIGHTED_SCORE),
# Address Selection
```{r}
```{r, warning=FALSE, message=FALSE}
select_eoas <- mkr_tw_bal %>% dplyr::filter(ADDRESS_TYPE == "EOA",
TIME_WEIGHTED_SCORE >= 100000,
TIME_WEIGHTED_SCORE >= 10000,
NEW_VALUE >= 50)
plot_ly(select_eoas, x = ~NEW_VALUE, y = ~TIME_WEIGHTED_SCORE,
@ -253,20 +268,24 @@ plot_ly(select_eoas, x = ~NEW_VALUE, y = ~TIME_WEIGHTED_SCORE,
```
Subsetting to target EOAs (i.e., not central exchange associated nor cold storage) with
at least 50 MKR and a time-weighted score of 100,000 (equivalent of holding 100 MKR for 1M blocks)
results in
`r nrow(select_eoas)` MKR holders. Unlike previous visuals, the above visual is *not* LOG adjusted, so the skew in both amount and time held is very noticeable.
Subsetting to target EOAs (i.e., not central exchange associated nor
cold storage) with at least 50 MKR and a time-weighted score of 10,000
(equivalent of holding 100 MKR for 100,000 blocks) results in
`r nrow(select_eoas)` MKR holders. Unlike previous visuals, the above
visual is *not* LOG adjusted, so the skew in both amount and time held
is very noticeable.
To assess fitness for direct outreach, the number of transactions, days active, and last transaction date
are pulled for the select EOAs.
To assess fitness for direct outreach, the number of transactions, days
active, and last transaction date are pulled for the select EOAs.
```{r}
```{r, warning = FALSE, message = FALSE}
query <- {
"
with select_tx AS (
SELECT BLOCK_TIMESTAMP, tx_hash, FROM_ADDRESS as ADDRESS FROM ethereum.core.fact_transactions
WHERE FROM_ADDRESS IN ('ADDRESSLIST')
SELECT BLOCK_TIMESTAMP, TX_HASH, FROM_ADDRESS as ADDRESS FROM ethereum.core.fact_transactions
WHERE FROM_ADDRESS IN ('ADDRESSLIST') AND
BLOCK_NUMBER >= _MIN_BLOCK_ AND
BLOCK_NUMBER <= _MAX_BLOCK_
)
SELECT ADDRESS, COUNT(*) as num_tx,
@ -279,29 +298,180 @@ GROUP BY ADDRESS
alist <- paste0(select_eoas$ADDRESS, collapse = "','")
query <- gsub('ADDRESSLIST', replacement = alist, x = query)
query <- gsub('_MIN_BLOCK_', replacement = min_block, x = query)
query <- gsub('_MAX_BLOCK_', replacement = max_block, x = query)
select_stats <- auto_paginate_query(query, api_key)
eoa_stats <- merge(select_eoas, select_stats, by = 'ADDRESS')
eoa_stats$LAST_TX_DATE <- as.Date(eoa_stats$LAST_TX_DATE)
final_eoa <- eoa_stats %>% dplyr::filter(
NUM_TX < 100000,
NUM_DAYS <= 600,
NUM_DAYS >= 5,
LAST_TX_DATE > (max(eoa_stats$LAST_TX_DATE) - 180)
)
```
Some noticeable issues are present among this selection:
```{r}
plot_ly(eoa_stats, x = ~log(NEW_VALUE), y= ~as.Date(LAST_TX_DATE),
color = ~NUM_TX, type = 'scatter',
- `r sum(eoa_stats$NUM_TX >= 100000)` EOAs with 100,000+ Transactions
(bots?).
- `r sum(eoa_stats$NUM_DAYS >= 600)` EOAs active nearly every single
day in time period (bots?).
- `r sum(eoa_stats$NUM_DAYS <= 5)` EOAs active less than 5 days in
time period.
- `r sum(eoa_stats$LAST_TX_DATE < (max(eoa_stats$LAST_TX_DATE) - 180))`
EOAs inactive for last 6+ months.
Subsetting to addresses with \< 100,000 tx; \< 600 days active; at least
5 days active; and active within the last 6 months results in
`r nrow(final_eoa)` addresses holding a total of
`r label_comma()(sum(final_eoa$NEW_VALUE))` MKR.
# Address Contact
Address to address communication for direct outreach is still immature.
To identify potential willingness to be contacted and pitched
delegation, two outreach avenues are identified:
- The Ethereum Name Service of each address (if available) is pulled to see if any addresses willingly doxx their
social media (e.g., by having their ENS in their Twitter name).
- Governance activity in other DAOs (i.e., Snapshot votes) for potential reach out via DAO partnerships.
## ENS
Using ENS NFT address `0x57f1887a8bf19b14fc0df6fd9b2acc9af147ea85` a one
to many test of NFT ownership is identified.
```{r, warning = FALSE, message = FALSE}
# add these to ethscore lol
hex_to_bigint <- function(x = "0x5ae081a11c9e42983640ad6d4c5b2fa9dd0a0b886e1def6057c0037c33d1bba5"){
as.character(as.bigz(x))
}
bigint_to_hex <- function(x = "41104824783848331047501863836715107956672917465157448818057950770477717896101"){
fill_hex <- function(x){
if(nchar(x) == 65){
x <- gsub("0x","0x0",x)
return(x)
}
return(x)
}
hx <- paste0("0x", as.character(as.bigz(x), b = 16))
hx <- unlist(lapply(hx, fill_hex))
return(hx)
}
ens_nfts_query <- {
"
SELECT BLOCK_NUMBER, NFT_TO_ADDRESS as ADDRESS, TOKENID FROM ethereum.core.ez_nft_transfers
WHERE NFT_ADDRESS = '0x57f1887a8bf19b14fc0df6fd9b2acc9af147ea85' AND
NFT_TO_ADDRESS IN ('ADDRESSLIST')
"
}
addresslist <- paste0(final_eoa$ADDRESS, collapse = "','")
ens_nfts_query <- gsub("ADDRESSLIST", addresslist, ens_nfts_query)
ens <- auto_paginate_query(ens_nfts_query, api_key)
ens$HEX_TOKENID <- bigint_to_hex(ens$TOKENID)
ens_query <- {
"
SELECT DISTINCT
event_inputs:name :: STRING as ens_name,
event_inputs:label :: STRING as hex_tokenid
from ethereum.core.fact_event_logs
where
hex_tokenid IN ('HEXLIST')
"
}
hexlist <- paste0(ens$HEX_TOKENID, collapse = "','")
ens_query <- gsub("HEXLIST", hexlist, ens_query)
ens_label <- auto_paginate_query(ens_query, api_key)
ens_label_full <- ens_label[!is.na(ens_label$ENS_NAME), ]
ens_label_problem <- ens_label[!(ens_label$HEX_TOKENID %in% ens_label_full$HEX_TOKENID), ]
eoa_nfts <- merge(ens, ens_label_full, by = 'HEX_TOKENID', all.x = TRUE)
eoa_label <- unique(eoa_nfts[, c("ADDRESS", "ENS_NAME")])
eoa_with_names <- merge(final_eoa, eoa_label, by = "ADDRESS", all.x = TRUE, all.y = TRUE)
unique_eoa_with_name <- eoa_with_names %>% filter(!is.na(ENS_NAME))
```
Of the `r length(unique(eoa_with_names$ADDRESS))` target EOAs there are
`r length(unique(unique_eoa_with_name$ADDRESS))` EOAs with at least 1
ENS. These individuals together have
`r length(unique(unique_eoa_with_name$ENS_NAME))` ENS names total.
```{r, warning = FALSE, message = FALSE}
eoa_name_balance <- eoa_with_names
eoa_name_balance$has_ENS <- ifelse(is.na(eoa_name_balance$ENS_NAME), "No ENS", "Has ENS")
eoa_name_balance <- unique(eoa_name_balance[, c("ADDRESS","NEW_VALUE","NUM_TX","NUM_DAYS","LAST_TX_DATE","has_ENS")])
plot_ly(eoa_name_balance, x = ~has_ENS, y = ~log(NEW_VALUE), color = ~has_ENS,
boxpoints = "all", jitter = 0.3,
hoverinfo = 'text',
hovertext = ~paste0(
'RAW-MKR Balance: ',
scales::label_comma()(floor(NEW_VALUE)),
'\n Raw MKR TW Score: ',
scales::label_comma()(floor(TIME_WEIGHTED_SCORE))
)
) %>%
layout(title = '\n Transaction Count by MKR holdings and most recent tx date',
xaxis = list(title = 'LOG(Current MKR Balance)'),
yaxis = list(title = 'Date of Last TX',
range = c(min(eoa_stats$LAST_TX_DATE), max(eoa_stats$LAST_TX_DATE)))
'LOG MKR Balance:',
round(log(NEW_VALUE), 2),'\n Raw MKR Balance: ',
scales::label_comma()(floor(NEW_VALUE))
),
type = 'box') %>%
layout(title = '\nDistribution of MKR among target EOAs with and without ENS',
xaxis = list(title = 'Has ENS'),
yaxis = list(title = 'LOG(MKR Balance)')
)
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