# MEV in fixed gas price blockchains: Terra Classic as a case of study

Facundo Carrillo  CONICET-Universidad de Buenos Aires, Instituto de
Investigación en Ciencias de la Computación (ICC), Buenos Aires, Argentina
Elaine Hu  Flashbots

###### Abstract

Maximum extractable value (MEV) has been extensively studied. In most papers,
the researchers have worked with the Ethereum blockchain almost exclusively.
Even though, Ethereum and other blockchains have dynamic gas prices this is
not the case for all blockchains; many of them have fixed gas prices.
Extending the research to other blockchains with fixed gas price could broaden
the scope of the existing studies on MEV. To our knowledge, there is not a
vast understanding of MEV in fixed gas price blockchains. Therefore, we
propose to study Terra Classic as an example to understand how MEV activities
affect blockchains with fixed gas price. We first analysed the data from Terra
Classic before the UST de-peg event in May 2022 and described the nature of
the exploited arbitrage opportunities. We found more than 188K successful
arbitrages, and most of them used UST as the initial token. The capital to
perform the arbitrage was less than 1K UST in 50% of the cases, and 80% of the
arbitrages had less than four swaps. Then, we explored the characteristics
that attribute to higher MEV. We found that searchers who use more complex
mechanisms, i.e. different contracts and accounts, made higher profits.
Finally, we concluded that the most profitable searchers used a strategy of
running bots in a multi-instance environment, i.e. running bots with different
virtual machines. We measured the importance of the geographic distribution of
the virtual machines that run the bots. We found that having good geographic
coverage makes the difference between winning or losing the arbitrage
opportunities. That is because, unlike MEV extraction in Ethereum, bots in
fixed gas price blockchains are not battling a gas war; they are fighting in a
latency war.

##  1 Introduction

MEV has been extensively studied, from mathematical models to data analysis
applications [1, 2, 3, 4]. In most studies, researchers have worked with the
Ethereum blockchain almost exclusively, so extending the studies to other
blockchains could help broaden the scope of MEV research. Understandably, one
of the most important aspects of MEV is gas bidding.

In Ethereum, the miners include transactions from the mempool and propose a
block with a subset of transactions in an arbitrary order. However, this
arbitrary order is not random; many miners use gas to sort transactions and
create new blocks since this strategy maximises their rewards. This incentive
increases the gas price to a point that makes the network almost impossible to
use for normal users.

Although this incentive is common in Ethereum, it is not present in all
blockchains. Many blockchains based on Tendermint don’t take dynamic gas
prices into account to prioritise transactions. They use a fixed gas price and
process transactions in the same order in which the validators receive them
111 https://docs.tendermint.com/v0.34/tendermint-core/mempool.html . This
difference could heavily impact the role and behaviour of MEV searchers. In
blockchains with fixed gas prices, the only resource that searchers have is to
recognize an MEV opportunity before other actors and respond quickly. This
contrast could have enormous repercussions on the opportunities that MEV
searchers choose to pursue. At first, there would be no possibility, by non-
privileged users, of positioning one transaction ahead of another once the
first one was already broadcasted. This restriction would forbid extracting
value based on front-running and sandwich strategies, leaving only back-
running as a resource for searchers. Even though searchers only have this
strategy, arbitrages could be performed.

Arbitrage is a particular way to extract value. It is a concept broadly used
in traditional finances that has presented a great opportunity in blockchains,
particularly since decentralised exchanges have become so popular. Arbitrage
consists of taking advantage of a difference in prices in two or more markets.
Users can buy an asset in one market at price A and sell it in another market
at price B. This action results in a profit equals to B minus A. This practice
is well established in many economic systems and allows different markets to
stay synchronised between them. Many studies have presented profits higher
than hundred of millions of dollars by applying arbitrage in Ethereum[5, 6, 7,
8, 9].

The community understands the importance of this practice. Every day more data
allows us to follow the progress in value extraction in different blockchains.
For example, the Flashbots community offers a dashboard that measures MEV
extraction on Ethereum in real-time222https://explore.flashbots.net/, Skip
Protocol provides data for Osmosis 333https://satellite.skip.money/, and
Marlin.Org for Polygon 444https://explore.marlin.org/, among others. Although
all these data allow us to quantify the amount of MEV extracted, they do not
provide the full picture. There isn’t a complete understanding of the type of
extraction, the type of pairs exploited, and cycles, among others. In
particular, those sites do not give enough information about searchers’
behaviour and how they maximise profit.

We believe blockchains that don’t use gas to prioritise transactions present
MEV opportunities with their own dynamics. As we have previously mentioned,
even though MEV has been extensively studied (market designs and their
implications [10], MEV in other blockchains [11], among others), to our
knowledge, there is not a full understanding of MEV in blockchains with fixed
gas price mechanisms. Therefore, we propose the following general objective:
To study the MEV opportunities in a blockchain with fixed gas price. To
contribute to this general object, we propose to study the Terra Classic
blockchain during the period from September 2021 until the de-peg event in May
2022, with the following specific objectives:

  * •

Specific Objective 1: Study the characteristics of arbitrages in Terra Classic
in the defined period.

  * •

Specific Objective 2: Understanding which strategies perform searchers to
improve profit.

  * •

Specific Objective 3: Understanding the timing-related characteristics of
arbitrage.

  * •

Specific Objective 4: Create a dashboard to help with the analysis of this
data

##  2 Methods

###  2.1 Dataset

Terra Classic suffered a particular and non-typical behaviour during and after
the de-peg event in May 2022. Due to this phenomenon, we decided to use the
last version of Terra Classic (Columbus-5) before the de-peg event. This
dataset contains 2,801,944 blocks, from block 4734841 (2021-10-01 08:31:52
UTC) to block 7536784 (2022-05-07 00:00:04 UTC).

In order to get the data, we ran a Terra Classic node using the snapshot
available in ChainLayer 555https://quicksync.io/networks/terra.html. This
website offers different blockchain snapshots to speed up synchronisation. We
used the Terra Classic’s code node available on its repository
666https://github.com/terra-money/classic-core.

After we set up the node, we iterated over all the blocks using Terra
Classic’s API to consume data. For every block, we used the Tendermint RPC to
get the block with its transactions coded in base 64. Then we also used the
Terra RPC to get the block result. This entry returns the logs of every
transaction after the block is proposed. Having this information is very
useful as it helps us understand if the transactions succeeded and
characterise its nature. Information on these two APIs is available in the
official documentation 777https://classic-
docs.terra.money/docs/develop/endpoints.html.

###  2.2 Arbitrage identification

We created an algorithm to understand if a successful transaction performed an
arbitrage. To do this, we used the transaction logs. Terra transactions’ logs
have a lot of information. Of all the information available, we used only the
wasm[12] log to understand if a transaction performed an arbitrage. For this,
we define a function that identifies a series of actions. These actions try to
represent a swap between tokens. Typically, these actions are simple swaps,
but sometimes they are more difficult to understand, for example, burning a
token and minting a new one. For this purpose, we define an action with the
following fields: pair address, token in, token out, amount in and amount out.
A transaction then contains a list of actions. With all the information, we
define an entire arbitrage as a list of these actions with the following
constraints:

  1. 1.

Successive tokens match

$\forall i\in[0...|actions|)\ actions[i].token_{out}=actions[i+1].token_{in}\
$ (1)

Note that $actions[i]$ represents the i-th action, $actions[i+1]$ represents
the following action, and $actions[i-1]$ represents the previous action.

  2. 2.

Arbitrage generates profit

$actions[|actions|-1].amount_{out}>actions[0].amount_{in}$ (2)

Note that fees are not taken into account.

  3. 3.

Arbitrage starts and ends with the same token

$actions[|actions|-1].token_{out}=actions[0].token_{in}$ (3)

For example, Figure 1 shows the actions list as a table for a particular
transaction.

Figure 1:  Example of how the platform shows a list of actions (link to
dashboard).

If we can parse the logs and create a consistent list of actions that complies
with the restrictions, we flag this transaction as a successful arbitrage. In
summary, we deem an arbitrage successful if it generates profit (without
taking into account fees); and the transaction is included in a block; and its
execution ends without being reverted. The code for parsing logs and creating
the list of actions are available in the repository (see Data, Code and
Analysis notebooks)

####  2.2.1 Inference of arbitrage in failed transactions

Transactions revert for different reasons. In most cases, lack of profit is
the cause. When transactions revert, they don’t produce the same wasm logs we
use to understand if the transactions are arbitrage. Therefore, we developed a
heuristic to flag arbitrage attempts. We define a transaction as failed
transaction if:

  1. 1.

The sender address has signed at least one successful arbitrage

  2. 2.

The contract address has executed at least one successful arbitrage

  3. 3.

The execute-message resembles another one produced by the same contract in at
least one successful arbitrage.

###  2.3 Dashboard

An interactive dashboard is developed using Django [13] to display the charts
and findings. The dashboard is available at: facuzeta.github.io/frp/dashboard.

####  2.3.1 Time Analysis

In order to analyse the effect of latency, we need to record transactions in
different geographic locations. For this, we designed an experiment in which
we deployed 84 instances well distributed around the world using AWS cloud
services. We created instances in the following regions: US East (Ohio), US
East (N. Virginia), US West (N. California), US West (Oregon), Africa (Cape
Town), Asia Pacific (Hong Kong), Asia Pacific (Jakarta), Asia Pacific
(Mumbai), Asia Pacific (Osaka), Asia Pacific (Seoul), Asia Pacific
(Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central),
Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Milan), Europe
(Paris), Europe (Stockholm), Europe (Zurich), Middle East (Bahrain), Middle
East (United Arab Emirates), South America (São Paulo).

After we deployed the instances, we recorded the transactions that arrived at
the mempool. Every node started with the same list of peers to connect (the
default peer list offered by the official documentation). But these peers are
not necessarily connected with the same set of nodes throughout the
experiment. This is due to the fact that Tendermint P2P layer connects,
disconnects, and asks for new peers depending on different statistics. Besides
the default configuration, we added two constraints : 1) our 84 nodes are not
connected to each other, and 2) Our nodes do not broadcast transactions from
their mempool.

Finally, we measured the operation system time differences among instances to
make sure that they had an error of less than 15 milliseconds.

###  2.4 Data, Code and Analysis notebooks

All the data and code used in this work are available in the following
repositories:

  * •

https://github.com/facuzeta/frp-mev-fixed-gas-price-dashboard

  * •

https://github.com/facuzeta/frp-mev-fixed-gas-price-analysis

##  3 Results

Blocks in Terra contain different types of transactions. Among them are:
transactions to initialise a contract, transactions to send tokens,
transactions to vote on governance decisions, and transactions to execute
smart contracts, and others. Arbitrages are usually carried out through
transactions that execute smart contract functions. Taking this into account,
we only use Execute-Msg type transactions to analyse the data.

Our dataset contains 2,801,944 blocks, from block 4734841 2021-10-01 08:31:52
UTC to block 7536784 2022-05-07 00:00:04 UTC. These blocks contain more than
117M transactions of any type and more than 37M execute-message transactions.
The number of transactions in blocks varies depending on the transactions
available in the mempool at the time of the block creation. On average, there
are 28.10$\pm$ 22 (mean and standard deviation) transactions per block with a
median of 23. Even though there are blocks with more than 800 transactions,
90% of blocks contain less than 50 transactions. Figure 2 shows the
distribution of the number of transactions with execute contract message per
block.

Figure 2: Distribution of transactions with execute contract message per
block.

###  3.1 Successful arbitrages

We found 188,564 arbitrage that were mined by the validators. These
transactions are distributed in many blocks. However, only 4.48% (125K) of the
blocks presented at least one successful arbitrage.

Figure 3: Distribution of successful arbitrages per block with at least one
successful arbitrage.

In the blocks where there is at least one arbitrage, in 73% of the cases, only
one arbitrage occurs. In 16% of the blocks, two successful arbitrages happen,
and in 5% of the cases, three successful arbitrages occur. That is, in 94% of
the blocks with successful arbitrages, a maximum of 3 arbitrages per block
occur. The Figure 3 illustrates the distribution of the number of arbitrages
in the blocks with at least one successful arbitrage. This variability is
probably due to different arbitrage opportunities occurring on certain blocks.

###  3.2  Identifying searchers

Identifying searchers is a difficult task because of the on-chain data
availability. We can only see addresses signing transactions to execute smart
contracts. In the 188K arbitrages, we found 517 different sender addresses and
167 different smart contracts.

Grouping the arbitrages by the sender, only the top 10 generate 40% of the
successful arbitrages. (see Table 1). Grouping by the smart contract, the top
10 contracts used by searchers account for 49% of successful arbitrages (see
Table 2).

Sender | Successful arbitrages | Contracts | %
---|---|---|---
terra1fgef888tuj3g8tmpxp4klvthq4eqk8a2yw7vsw | 27197 | 6 | 14.42
terra1lywrs72lpsptu68usehlae5yq94wsuk2g6fa3m | 10232 | 13 | 5.42
terra1n3s52w97j599sq3k4wcr3j0027j0stnfttnjvg | 7385 | 7 | 3.91
terra1nyud8zzctrks6ltnhhn4q820umnvqg39hc6ydz | 7193 | 2 | 3.81
terra16kd4ucrkj3mu2xpt25075s3jsztkx9ltf3tj9y | 4921 | 1 | 2.60
terra1xjpy3hu7qzun8p3h3cuf4w2v4f98w376mjj74l | 4797 | 13 | 2.54
terra140d3kgus5nxnqextv6fahzncfvp90r7t0s6mjp | 3684 | 5 | 1.95
terra1lxyhvhhdjc3sxk0kjeljtqnxjf7ek6asxxyp7q | 3645 | 3 | 1.93
terra1cxspyydlp4qdu0pzetg9au549n29tnvs4t3kej | 3334 | 2 | 1.76
terra1qfg2exflsshg23gs5w4ml37hwnunzthtx2gpp6 | 3288 | 2 | 1.74
Table 1: Top 10 arbitrage senders Contract | Successful arbitrages | Senders | %
---|---|---|---
terra1p9zmnwqrfpzx0k585r3lhrqhlakmrnar2cljt9 | 17130 | 1 | 9.08
terra1kz93zt3ng7kags09qy06a079wjl3qpzfp0axsn | 14195 | 8 | 7.52
terra1nuzl3sppu0suws9zml5psu8tmuqk5dnr88n8kc | 10664 | 8 | 5.65
terra10j88evvssl4m0ztj2hcelf993j5a0xd3ezd7se | 10314 | 80 | 5.46
terra1myl5pk2a7qj37yu6dgmy68rxkznmq9nrk69nrj | 8367 | 50 | 4.43
terra1wswy9763nhugvphchcdc438r3ejvskjjz5h0rs | 7731 | 8 | 4.09
terra139y02s9urkkyesukndrqdjmqj7gkk5dltd05v8 | 7060 | 8 | 3.74
terra19rltg2vaurffa25cvtm5zey9muhaxflfxt5e2p | 6152 | 1 | 3.26
terra1egqmcaupc87sdg6sqmzypuv06ykxkek3j7s4w5 | 5927 | 1 | 3.14
terra1sg877xutwmyvsz45jrlwcdhx07jac33433499e | 5634 | 3 | 2.98
Table 2: Top 10 arbitrage contracts

A contract is not necessarily used by a single address and an address does not
necessarily use a single smart contract to carry out arbitrages. In fact, only
55% of the contracts are used by a single sender, while 61% of the senders use
a single contract.

Given this, we proposed a method to infer searchers. The method consists of
grouping sender addresses and smart contracts related to each other. We
defined that an address is related to a smart contract if the sender signed a
transaction that executed the smart contract. Then, we extended this using a
transitive property.

To compute this, we created a graph where each node represented a sender
address or a smart contract. Then, the two nodes had a common edge if there
was a transaction calling the smart contract signed by the address. Next, we
executed the analysis of connected components. Every connected component was a
searcher, and the addresses and smart contracts belonged to it.

Figure 4 shows the graph and the inferred searchers.

Figure 4:  In this graph, each node represents a sender address or a smart
contract. Nodes have a common edge if there is a transaction calling the smart
contract and it is signed by the sender’s address. The largest nine connected
components are coloured. The rest of the connected components are grey. The
size of the node represents the node degree. The distance between nodes does
not add extra information.

Our method identified 56 connected components or searchers. 50% of them used
only one smart contract, and 57% of the searchers used only one address to
sign transactions. Figure 5 shows the distribution of contracts and senders by
searchers.

Figure 5:  The left panel shows the histogram of the number of contracts per
searcher. The right panel shows the distribution of the number of sender
addresses per searcher.

There was a positive correlation (Pearson rho=0.3487, p-value=0.0084) between
the number of contracts and addresses that searchers used. The number of
contracts and addresses could describe the complexity of searchers’
strategies. Some searchers used only one smart contract and one wallet to sign
transactions. More sophisticated searchers could have multiple contracts and
wallets or sender addresses. Figure 6 shows this correlation.

Figure 6:  Scatter plot of the number of different senders’ addresses and
contracts by searchers. The colour code follows the one presented in Figure 4.

###  3.3 Other characteristics of arbitrage

####  3.3.1 Token In

Arbitrages start with different tokens. In our dataset, 87% (165K) of the
arbitrages started with UST. This seems reasonable since UST was the biggest
stable coin in Terra Classic before the de-peg event. Following UST, 10% (19K)
of the arbitrages started with LUNA, the native token of Terra Classic. Figure
7 shows the distribution of the token-in.

Figure 7:  Number of arbitrages that start with different tokens.

####  3.3.2 Arbitrage Amount in

Arbitrages had a different range of amounts - those started with the UST token
shows a median of 998 UST and the 25 percentile is 260 UST. For arbitrage that
started with LUNA, the median is 88 LUNA and the 25 percentile is 20 LUNA.
Figure 8 shows these two distributions.

Figure 8:  The left panel shows the distribution of the number of arbitrages
that start with UST. The right panel shows the distribution of the number of
arbitrages that starts with LUNA.

####  3.3.3 Path length

Arbitrage path lengths could be variable. 80% of the arbitrages use 2 or 3
swaps. Arbitrages with paths greater than five swaps are present in 0.4% of
the arbitrages. Increasing the length of the cycles also increases the cost of
gas which reduces profit. So it is reasonable to see the length of the cycles
is mostly 2 or 3.

Figure 9:  Percentage of arbitrages according to the cycle length.

####  3.3.4 Pairs

Arbitrages used 300 different pairs/pools. However, the 10 most used pairs
cover 38% of the swaps; the 50 most used pairs cover 82% and the 100 most used
pairs cover 95%. The top 10 most used pairs are shown in Table 3.

Pair | Rate | Token | Token
---|---|---|---
terra1m6ywlgn6wrjuagcmmezzz2a029gtldhey5k552 | 0.07316 | UST | LUNA
terra1tndcaqxkpc5ce9qee5ggqf430mr2z3pefe5wj6 | 0.07297 | UST | LUNA
terra106a00unep7pvwvcck4wylt4fffjhgkf9a0u6eu | 0.04285 | UST | LOOPC
terra1jxazgm67et0ce260kvrpfv50acuushpjsz2y0p | 0.03370 | bLUNA | LUNA
terra163pkeeuwxzr0yhndf8xd2jprm9hrtk59xf7nqf | 0.03260 | UST | PSI
terra1cda4adzngjzcn8quvfu2229s8tedl5t306352x | 0.03056 | bLUNA | nLUNA
terra1c0afrdc5253tkp5wt7rxhuj42xwyf2lcre0s7c | 0.02925 | UST | bETH
terra1sgu6yca6yjk0a34l86u6ju4apjcd6refwuhgzv | 0.02562 | UST | LUNA
terra1v5ct2tuhfqd0tf8z0wwengh4fg77kaczgf6gtx | 0.02375 | UST | PSI
terra1j66jatn3k50hjtg2xemnjm8s7y8dws9xqa5y8w | 0.02339 | bLUNA | LUNA
Table 3: Top 10 most used pairs

It is worth mentioning that some of the top 10 pairs allow swapping the same
pair of tokens. This duplication is because different DEXes offer to swap the
same pair of tokens.

###  3.4 Profit

The distribution of arbitrages by searchers is very irregular. Some searchers
had good performance while others did not get high profits. 78% of searchers
had ten or more successful arbitrages, 59% had 100 or more successful
arbitrages, and only 35% had more than 1000 successful arbitrages.

As mentioned before, the token-in and the token of the profit can vary. Since
97% of arbitrages used UST or LUNA to start the arbitrage, in this section, we
focus only on the arbitrages that used these tokens as token-in.

For those using UST as a token-in, the total profit was 16.24M UST and for
LUNA it was 20,569. The value of LUNA can be volatile, unlike stablecoins. In
the time range of our dataset, the LUNA price ranged from 35 to 100 UST. Using
100 UST as the upper bound, we limited the profit per 2M UST above (although
the value was likely lower).

One way to compare searchers’ performance is to use the profit rate. We
defined the profit rate as the percentage of profit out of the initial value
of the arbitrage - a ratio between profit and the starting value of the
arbitrage. For example, if an arbitration starts with 105 UST and 115 UST is
obtained, the profit rate is (115-105)/105=0.095. Taking this normalised value
into account, 99% of arbitrages have a profit rate of less than 1. Besides the
99% of the sample, there were arbitrages with outlier values. For example,
this transaction
$7ad339227b991c402efaa4c8e02cd7af3c76c5d820d1234cbb759f5f9968aa1e$
888https://facuzeta.github.io/frp/dashboard/7ad339227b991c402efaa4c8e02cd7af3c76c5d820d1234cbb759f5f9968aa1e/
reached a profit rate higher than 2000.

Discarding profit rate values greater than 1, Figure 10 shows the distribution
of this measure.

Figure 10:  Distribution of Profit Rate for arbitrages with Profit rate less
or equal to 1.

Grouping by searchers, we saw that the profit is not uniformly distributed.
The three searchers with the highest profit cover more than 75% of the MEV.

Various characteristics can explain why some searchers have more profit than
others. For example, the number of arbitrages they conducted, the number of
instances they used to run the bots, and the number of contracts they used,
among others.

For arbitrages beginning with UST, the correlation between the profit and the
number of arbitrages was positive and statistically significant (Pearson
correlation rho=0.8987, p-value$<10^{-18}$). They also were significant and
positive correlations, the number of different contracts against the number of
arbitrages (rho=0.6945, p-value$<10^{-7}$) and the number of senders against
the number of arbitrages (rho=0.5615, p-value$<10^{-5}$). It is relevant to
clarify that there was also a significant positive correlation between the
number of senders and the number of arbitrages. And also between the number of
contracts and the number of arbitrages. Therefore, the previous correlation
might only be the product of this phenomenon.

For arbitrages beginning with LUNA, the correlation between the profit and the
number of arbitrages is positive and significant (Pearson correlation
rho=0.8551, p-value=0.0016). Correlations are also significant and positive in
the number of different contracts versus the number of arbitrages (rho=0.6903,
p-value=0.0271); and the number of senders versus the number of arbitrages
(rho=0.6792, p-value=0.0307). In this case, there is a significant positive
correlation between the number of arbitrages and the number of senders.
However, there is no correlation between the number of different contracts and
the number of arbitrages.

This information is not enough to explain everything. For example whether
using more contracts promotes a higher profit. This question can not be
answered because profit could also be affected by the number of arbitrages.

To identify which factors contribute to higher profits, it is useful to have a
history of the arbitrage attempts that did not work. In the next section, we
analyse this factor.

###  3.5 Failed arbitrages

Similar to other blockchains, arbitrages that fail or revert in Terra Classic
happened for various reasons. It could be due to estimation errors, or other
reasons that caused the swaps to not land on chain. But most likely the
transactions revert because another transaction won the opportunity and the
profit was already extracted.

In the section Inference of arbitrage in failed transactions, we introduced a
way to estimate reverted arbitrages. The number of arbitrages that reverted
was higher than those succeeded. There were 188,564 successful arbitrages and
670,258 failed one. In other words, for each successful arbitrage, there were
3.55 that failed on average.

Considering only executing message-type transactions, successful arbitrages
represented 5% of these transactions and failed arbitrages represented 19%.

This difference generates negative effects for the network because although
they do not increase the value of gas like other chains, they fill the blocks
with transactions that revert and increase the size of the mempool, resulting
in the network congestion. Analysing it by blocks, the number of failed
arbitrages correlates with the number of successful arbitrages (rho = 0.4551,
p$<10^{-100}$). The amount of gas used for successful arbitrages was 445
billion, and 1357 billion for failed arbitrages, which is approximately three
times of gas used for successful arbitrages.

In the following subsection, we use success rate to help understand the
different aspects that determine searchers’ profits.

###  3.6 Success rate

We defined the success rate of a searcher as the number of successful
arbitrage divided by its own total arbitrage. This measure quantified the
searcher with different information. Then, we assess whether there is a
correlation between the success rate and the total profit using Spearman
correlation. We filter out those searchers with very few successful arbitrages
through experimenting with different minimum successful arbitrages as a
threshold. In all cases, we obtain a negative correlation between the success
rate and profit. This result means that searchers with lower successful-rate
had more profit. Table 4 summarises the correlations.

Threshold Min Successful arbitrages |  Percentile that threshold represented |  Number of Searchers after threshold | Rho | P-value
---|---|---|---|---
10 |  23 |  40 | -0.4869 | 0.0014
50 |  38 |  32 | -0.5821 | 0.0005
100 |  44 |  29 | -0.6084 | 0.0005
250 |  46 |  28 | -0.6349 | 0.0003
500 |  53 |  24 | -0.5983 | 0.0020
750 |  55 |  23 | -0.5652 | 0.0049
Table 4:  Result of correlations for different thresholds

Figure 11 shows as an example the scatter plot between the successful rate and
the profit for threshold=50 successful arbitrages.

Figure 11:  Correlation between success rate and profit by searchers. The
colour code follows the one presented in Figure 4.

We have already established a negative correlation between profit and the
success rate. Those searchers who have a high success rate are not the ones
who have a higher profit. The hypothesis behind this phenomenon is that
searchers that have many transactions with reverted arbitrages are running
bots in multiple instances. They probably have several instances running the
same code in different virtual machines. Every instance responds with the same
attempt to arbitrage, but one arrives before the others, causing the second
and the following to revert.

Multiple running instances could be an effect of the lack of variable gas
prices for this type of blockchain. The only mechanism that searchers have to
arrive earlier is by reducing the response time. Searchers achieved this by
having many instances competing to receive the transaction that generated the
opportunity earlier. In the following subsection, we test this hypothesis.

###  3.7 Searchers with multiple running instances

It was impossible to determine if a searcher was running on multiple instances
using exclusively on-chain data. However, we can address this problem by
studying the reverted transactions and trying to understand, in each case, if
they were reverted because another transaction from the same searcher with the
same execution message arrived first. If we have many of these transactions,
we can assume that this is an effect of the same searcher computing the
opportunity to generate a transaction and send it from many instances that are
running concurrently.

To test this hypothesis, we built the rate repeated transaction index. This
rate measures the times a searcher has repeated the same transaction (in terms
of the same execute message) in the same block on average. A high value of
this measure indicates that searchers have more than one transaction with the
same executed message in the same block. For searchers with at least 50
successful arbitrages, the mean and standard deviation of this measure were
1.3125$\pm$0.5574.

We also studied the correlation between the rate of repeated transactions and
the profit. We found a positive and significant correlation (Spearman
correlation rho=0.6022, p-value=0.0011). Figure 12 shows the scatter plot of
this correlation.

Figure 12:  Correlation between repeated transaction rate and profit by
searchers. The colour code follows the one presented in Figure 4.

This positive and significant correlation explained that searchers that, on
average, sent more transactions with the same execute message per block,
obtained a higher profit. This is probably explained by the concurrent
execution of instances running the same code.

In the next section, we look at the timing and how running multiple instances
can make bots see transactions earlier.

###  3.8 Time analysis

In the previous section, we showed that bots with higher profits had higher
repeated transaction rate. This effect is probably due to the searchers having
more than one instance running the arbitrage bots. The motivation behind this
is strictly related to the nature of the fixed gas price blockchains. The only
recourse bots have to win an opportunity is to be the fastest.

We set up the experiment detailed in the Time Analysis Section and measured
how long it took for the 84 instances in different geographic locations
(running a Terra Classic node) to receive the 431K transactions. For each
transaction, we measured the time elapsed relative to the first region that
received the transaction. This analysis generated more than 36M different
times. Figure 13 shows the non-uniform distribution of how many times a region
saw a transaction before others.

Figure 13:  Distribution of times a region saw transactions before others.

Figure 14 shows the distribution of the latency for transactions that took
less than 7 seconds (98% of the sample).

Figure 14:  Distribution of latency in milliseconds of transactions with
latency less than 7 seconds.

Although the sample distribution of all recorded times is informative, it may
be useful to analyse it by region because the effect of geographic proximity
may be relevant. We believe that the physically closer the instances are, the
faster the transaction arrives. Figure 15 shows the median time (in
milliseconds) that it takes to receive transactions taking into account the
time that elapsed since the first region received the transaction. For
example, if Asia Pacific Mumbai received a transaction first, Asia Pacific
Osaka waits 63 milliseconds (in median time) and then receives the
transaction. Asia Pacific Sydney has to wait for 102 milliseconds, and South
America Sao Paulo, 131 milliseconds.

Figure 15:  Heat map of median latency in milliseconds. The median time (in
milliseconds) that it takes to receive transactions, taking into account the
time that elapsed since the first region received the transaction. For
example, if Asia Pacific Mumbai received a transaction first, Asia Pacific
Osaka waits 63 milliseconds (in median time) and then receives the
transaction. Asia Pacific Sydney has to wait for 102 milliseconds, and South
America Sao Paulo, 131 milliseconds.

Figure 15 shows the expected geographic grouping. We performed the following
procedure to quantify the effect. We calculated all pairs of regions and then
measured the correlation between each pair of median times with the distance
in kilometres between cities of the data centres. We found a positive and
significant Pearson correlation (rho=0.7452 and p-value=1.6721e- 33). Figure
16 shows this effect.

Figure 16:  Correlation between median time latency (in milliseconds) and the
distance between regions (in Km).

We have already established that the time to receive a transaction is related
to geographical distances and these values range from 30 to 100 milliseconds
in most cases. These characteristics implied that the time in which a bot
could see a transaction if it was not in the same region where it originated,
was relevant and probably up to an order of magnitude larger than what it may
take to compute a solution for a possible arbitrage. Therefore, if another
searcher had a bot in the same region where the transaction that generates the
arbitrage opportunity originated, it had an advantage of at least 30
milliseconds. This difference suggests that it was relevant for searchers to
have many instances running in different regions of the world so that they
were always close to the node that first received the transaction that created
the opportunity. It is important to note that the time synchronisation error
in different instances is less than 10 milliseconds.

##  4 Discussion

In our work, we studied a Terra Classic as an example of a blockchain with a
fixed gas price. We use Terra as a case study because of its enormous data
volume and the number of decentralised exchanges it has.

We have four specific objectives: Specific Objective 1: Study the
characteristics of arbitrages in Terra Classic in the defined period. Specific
Objective 2: Understanding which strategies improve searchers’ profit.
Specific Objective 3: Understanding the timing-related characteristics of the
arbitrage. Specific Objective 4: Create a dashboard to help with the analysis.

Regarding SO1: In the studied time period, we found more than 188 thousand
arbitrages distributed in time proportional to the network load. Typically,
they started and ended with two tokens: UST and LUNA. The number of tokens in
the arbitrages is very different and the length of the arbitrage cycles is
shorter than expected. 80% of the arbitrages had only 2 or 3 swap and they’re
mostly from the most exploited pairs - UST and LUNA. We also found some
extremely profitable arbitrages, with profit rate greater than 2000. This
phenomenon could be due to volatile movements in the token prices at
distressed times, breaking the synchronisation of two markets in an outrageous
way.

Beyond the individual quantification of the arbitrages, we created a strategy
to identify searchers. This task allowed us to make a more precise analysis.
We found that a small number of searchers contributed to almost all the
profit.

We then proposed a mechanism to identify failed arbitrages which is much
larger than the number of successful arbitrages. The ratio was 3.55 failed
arbitrages for each successful one. This rate can have negative consequences
on the network. Although the gas price cannot be increased, validators have to
create larger blocks which increase computation time for all nodes. Also, the
amount of data transmitted was larger, congesting and increasing network costs
without much real benefits. This could result in transactions staying in the
mempool for a long time.

Regarding Specific Objective 2: We understood that several characteristics
correlate with high profits. We found that searchers with more complexity
(more senders and more contracts) had a higher profit. This complexity could
indicate that searchers who constantly changed their strategies with different
contracts and frequent improvements eventually generated more profits.

Furthermore, we found that searchers with a lower success rate had a higher
profit. At first, this result could sound contradictory. But the fact that
searchers sent simultaneous transactions with the same message could mean that
searchers ran many instances from various places and they tried to respond
with the shortest possible delay. To measure this, we created the rate
repeated transition index that allowed us to quantify and corroborate this
hypothesis.

Regarding Specific Objective 3: We carried out an observational experiment
where we recorded transactions in several instances. After reviewing more than
400K transactions, we found that the physical distance explained the latency
in receiving transactions. That means a node has to wait a time proportional
to its distance to the source node until it receives the transaction.

Regarding Specific Objective 4: we condensed all this analysis into an
interactive platform that allows users to explore the data in a new way. We
believe that making these tools available promotes the testing of new
hypotheses. The platform also allows for data download which could help the
community with further research.

This research study quantifies and characterizes the arbitrage opportunities
in Terra Classic before the de-peg event. We believe it is one of the first to
address the effects of arbitrages on a fixed gas price blockchain.

In some papers [14], authors propose that MEV on blockchains like Ethereum is
a crypto gas war because bots play a game of finding the right gas price. In
the case of blockchains with fixed price gas, we believe that bots are
competing in a different type of war: the bot latency war.

###  4.1 Limitations

Our work studied the effects of fixed gas prices on a single blockchain during
a particular time. We found features that maximize profit for searchers but
these features are not necessarily the same for all blockchains of this type.
For future studies, we could test these hypotheses in other blockchains to
understand if the same features apply.

Regarding the time experiment, we believe that two improvements could
contribute to more robust results. First, our experiment was purely
observational. We could study the timing results by sending transactions
ourselves in order to have a more precise understanding of the location of the
transaction. Secondly, although we control the synchronisation among the
different nodes with an error of less than 10 milliseconds, it is possible to
reduce this error further and to make our findings more accurate.

## Acknowledgement

We thank Dr Enzo Tagliazucchi for providing the infrastructure to run the
analysis of this work. We thank the Flashbot team for supporting this project.

## Funding

This research was supported by the Flashbot Research Program.

## References

  * [1] Kaihua Qin, Liyi Zhou, and Arthur Gervais.  Quantifying blockchain extractable value: How dark is the forest?  In 2022 IEEE Symposium on Security and Privacy (SP), pages 198–214. IEEE, 2022.
  * [2] Ben Weintraub, Christof Ferreira Torres, Cristina Nita-Rotaru, and Radu State.  A flash (bot) in the pan: measuring maximal extractable value in private pools.  In Proceedings of the 22nd ACM Internet Measurement Conference, pages 458–471, 2022.
  * [3] Alex Obadia.  Flashbots: Frontrunning the MEV Crisis.  https://medium.com/flashbots/frontrunning-the-mev-crisis-40629a613752, 2022\.
  * [4] Philip Daian, Steven Goldfeder, Tyler Kell, Yunqi Li, Xueyuan Zhao, Iddo Bentov, Lorenz Breidenbach, and Ari Juels.  Flash boys 2.0: Frontrunning in decentralized exchanges, miner extractable value, and consensus instability.  In 2020 IEEE Symposium on Security and Privacy (SP), pages 910–927. IEEE, 2020.
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  * [7] Hai Jin, Chenchen Li, Jiang Xiao, Teng Zhang, Xiaohai Dai, and Bo Li.  Detecting arbitrage on ethereum through feature fusion and positive-unlabeled learning.  IEEE Journal on Selected Areas in Communications, 40(12):3660–3671, 2022.
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  * [10] Flashbots.  Why your blockchain needs an MEV solution.  https://www.youtube.com/watch?v=sYFuFLe9kp0, 2022.
  * [11] Alex Obadia.  A brief Survey of MEV on Ethereum, BSC, Avalanche and Polygon in 2021 .  https://www.youtube.com/watch?v=OYE9uAf_v18, 2022.
  * [12] Andreas Haas, Andreas Rossberg, Derek L Schuff, Ben L Titzer, Michael Holman, Dan Gohman, Luke Wagner, Alon Zakai, and JF Bastien.  Bringing the web up to speed with webassembly.  In Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation, pages 185–200, 2017.
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The upcoming spectroscopic powerhouses at the Isaac Newton Group of Telescopes
Balcells, M.1,2,3
1 Isaac Newton Group, 38700 Santa Cruz de La Palma, Spain

2 Instituto de Astrofísica de Canarias, 38200 La Laguna, Tenerife, Spain

3 Universidad de La Laguna, 38200 La Laguna, Tenerife, Spain

## Abstract

The Isaac Newton Group of Telescopes is completing a strategic change for the
scientific use of its two telescopes, the 4.2-m William Herschel Telescope
(WHT) and the 2.5-m Isaac Newton Telescope (INT). After more than 30 years
operating as multi-purpose telescopes, the telescopes will soon complete their
shift to nearly-single instrument operation dominated by large surveys.

At the WHT, the WEAVE multi-fibre spectrograph is being commissioned in late
2022. Science surveys are expected to launch in 2023. 30% of the available
time will be offered in open time. For the INT, construction of HARPS-3, a
high-resolution ultra-stable spectrograph for extra-solar planet studies, is
underway, with deployment planned for late 2024. The INT itself is being
modernised and will operate as a robotic telescope. An average of 40% of the
time will be offered as open time.

The ING will maintain its student programme. Plans call for moving student
work from the INT to the WHT once the INT starts operating robotically.

##  1 Introduction

Since the mid 1980’s, the Isaac Newton Group111www.ing.iac.es (ING) operates
the 4.2-m William Herschel Telescope (WHT) and the 2.5-m Isaac Newton
Telescope (INT) at the Observatorio Roque de los Muchachos on the Canarian
island of La Palma. The telescopes have provided front-line multi-instrument
observing capabilities to the ING astronomical communities of the UK, Spain
and the Netherlands. Instrumentation comprised facility instruments (most
recently ISIS, LIRIS, ACAM, PF, AF2, WYFFOS, INGRID, LDSS, NAOMI, OASIS,
TAURUS-II, WFC, IDS), as well as powerful visiting instruments for science or
for technology demonstration (AOLI, CANARY, DIPOL, GASP, GHaFaS, HiPERCAM,
iQuEyE, LEXI, PAUCAM, PN.S, SPIFS, CIRPASS, CIRSI, ExPo, INTEGRAL, MAOJCam,
PLANETPOL, PWFS, SAURON, ULTRACAM, Stereo-SCIDAR, Sodium Profiler).

In 2010, following a strategic review and community
consultation222https://www.ing.iac.es/about-ING/strategy/, and following the
Astronet Roadmap for European Astronomy [5] and the European Telescope
Strategy Review Committee 2010 report [15], ING started making steps to allow
the WHT to become a powerful spectroscopic survey facility. This
transformation had been announced at previous SEA conferences[3].

This paper provides a much needed update, after the pandemia years, coinciding
with the exciting times when the WHT is starting to collect sky light with its
new instrumentation.

Figure 1: The WHT prime focus equiped with the WEAVE fibre positioner. Photo
credit: J. Méndez, ING.

##  2 The new WHT

With a strategic view focused on science from massive spectroscopic surveys
doable on a 4-m class telescope, there were clear design drivers for new
instrumentation[1]. The WEAVE instrument (Fig. 1) is the result of that
strategy. Three main elements comprise the new WHT:

Prime-focus corrector.

A new optical corrector delivers a corrected field of view (FOV) of 2 degree
(40 cm) diameter, with good transmission from 360 nm to 1000 nm.

Fibre positioner.

A pick-and-place system, based on the 2dF system at the AAT, employs two
robots to place up to 960 fibres on the focal plane. In addition to the
standard single-fibre multi-object mode, the instrument comprises 20 fully-
deployable mini-integral-field units (mIFU) of 10 arcsec FOV, as well as a
monolithic integral-field unit (LIFU) with field of view 90 arcsec. A total of
3243 fibres transmit the light from the MOS system, the mIFU system and the
LIFU to the spectrograph.

Spectrograph.

A new two-arm spectrograph provides spectroscopy at resolving power of either
5,000 (LR) or 20,000 (HR). Wavelength coverage at LR is the entire optical
range, 366 – 959 nm.

###  2.1 WEAVE highlights

The WEAVE instrument has been described elsewhere [7][8][17][2]. The
definitive description of the as-built instrument, at the time of integration
at the telescope, pre-commissioning, is given in [12]. And an updated
description is being maintained at the ING web
site333https://www.ing.iac.es/astronomy/instruments/weave/weaveinst.html.

We direct the reader to the above references, and here just note the three
input modes: multi-object mode (MOS), the large-integral field mode (LIFU) and
the mini-integral field mode (mIFU). We discuss WEAVE’s main highlights in the
global context of multi-object spectroscopy instruments.

Table 1: WEAVE highlights Parameter | Interest
---|---
MOS lowres $R=5,000$ |  $\delta v\sim 3\,\mathrm{km\,s^{-1}}$ @ $V\sim 20$, match to Gaia $v_{\mathrm{T}}$
MOS highres $R=20,000$ | Improved continuum for line strengths
MOS, mIFU FOV $\sim 2\,\mathrm{deg}$ | High multiplex
LIFU highres $R=10,000$ | Resolving vertical vel. disp. of galaxy disks
LIFU FOV $\sim 90\,\mathrm{arcsec}$ | Evolution of PPAK
20 mIFU, FOV $\sim 11\,\mathrm{arcsec}$ | MANGA but on a 4-m, $R=5,000$
End-to-end pipeline | Science-ready data
Offered for surveys and open time | Accommodates both large and small projects

When compared to other MOS instruments on 4-m class telescopes, WEAVE shines
in a number of aspects. The more salient of them are noted in Table 1:

  * •

In its default, low-resolution mode, WEAVE’s resolving power $R=5000$ is high
among high-multiplex MOS instruments built for galactic and extra-galactic
science, and makes WEAVE ideal for Milky Way dynamics and archaeology, as it
provides stellar radial velocities with accuracies similar to those of
tangential velocity data from Gaia.

  * •

In its high-resolution mode, WEAVE’s resolving power $R=20,000$, when combined
with its high multiplex, represents a significant step forward for stellar
line strength determinations, as continua adjacent to the lines are less
affected by instrumental broadening.

  * •

The MOS FOV, 2 degree diameter, is unique in its mIFU mode and in the high-
resolution MOS.

  * •

The LIFU high-resolution configuration, which delivers R=10,000 spectra, can
resolve the vertical velocity structure of galaxy disks.

  * •

The LIFU FOV, which was patterned after the PPAK LIFU[13], has 50% higher FOV,
and higher wavelength coverage ($3660-9590~{}\AA$), and represents a true
next-generation large IFU for the study of extended sources.

  * •

The mIFU FOV matches the smaller end of the distribution of FOV for the MANGA
units[4]. With their wide wavelength coverage and with spectral resolutions
2.5[10] times higher than the SDSS-III spectrographs in the low[high] WEAVE
resolutions, the WEAVE mIFU’s will be powerful tools for low-mass galaxies and
for compact star-forming regions in our Galaxy.

  * •

Data will be delivered fully reduced and containing a range of science-ready
products.

###  2.2 WEAVE surveys

Eight surveys (Table 2) have been approved for execution over 5 years of WEAVE
operation; they will be allocated 70% of the available time on WEAVE. As of
this writing, the most comprehensive description of the surveys is given in
[12]. For updates and contact points for the surveys and for WEAVE science,
the reader is directed to the WEAVE web
site444https://ingconfluence.ing.iac.es/confluence/display/WEAVEDEV/WEAVE%3A+The+Science.

Table 2: The eight WEAVE surveys | Title |  | Title
---|---|---|---
GA | Galactic Archaeology | SCIP | Stellar, Circumstellar and Interstellar Physics
WC | Galaxy Clusters[6] | StePS | Stellar Populations At Intermediate Redshift[11]
WA | WEAVE Apertif [10] | WL | WEAVE LOFAR[16]
WQ | WEAVE QSO[14] | WD | WEAVE White Dwarfs

###  2.3 Using the WHT: surveys and open time

ING is offering time on WEAVE for large surveys as well as through the ING
national time-allocation committees. The International Time programme of the
Canarian Observatories555https://www.iac.es/en/observatorios-de-
canarias/observing-time/observing-time/international-time-programme provides
an additional means of obtaining up to 15 WEAVE nights per year in addition to
time on any of the other ORM telescopes.

WEAVE is due to start commissioning in the fall of 2022. We anticipate LIFU
science verification observations in early 2023, and aim for starting science
in the middle of 2023.

##  3 The third life of the Isaac Newton Telescope

ING is in the middle of transforming the 2.5-m Isaac Newton Telescope (INT) by
installing HARPS3666https://www.terrahunting.org/harps3.html [18], an enhanced
version of the HARPS and HARPS-N spectrographs aimed at achieving 10 cm s-1
radial velocity precision on nearby stars to search for Earth-like planets.
HARPS3 differs from its predecessors by a stabilised beam feed and a
polarimetric sub-unit[9] which will provide a powerful tool for characterising
stellar activity. The Terra Hunting Experiment (THE) consortium, P.I. Didier
Queloz, is building HARPS3 and making it available in exchange for $\sim$50%
of every night over 10 years; the remaining time will be offered as open time
through the usual national time allocation channels. The consortium is also
modernising the INT, which will become a robotic telescope. This will be the
third encarnation of the venerable INT, after its first installation in
Herstmonceux in 1967 and it re-deployment in La Palma in 1982.

With this transformation, we expect the INT will provide the UK, ES and NL
astronomical communities with a much needed tool for extra-solar planet
science.

Current plans call for the robotic INT to be commissioned during the summer of
2024, and for HARPS3 to start scientific operations before the end of 2024.

##  4 Opportunities for students

ING will continue to welcome 4 to 6 students for year-long stays at the ING.
Building on the success of this highly-demanded programme, we are hoping to
increase the number of student positions in the near future.

When the INT closes down for reforms and becomes a robotic telescope, students
will dominantly work at the WHT, partaking in WEAVE survey and open-time
observations. This will open up opportunities for the students to become
familiar with the execution of large projects and to develop expertise in
WEAVE data.

## Acknowledgments

The Isaac Newton Group of Telescopes is operated on behalf of the UK Science
and Technology Facilities Council (STFC) the Nederlanse Organisatie voor
Wetenschappelijk Onderzoek (NWO), and the Instituto de Astrofísica de Canarias
(IAC).

WEAVE construction was funded from generous grants from STFC, NWO, Spanish
Science Ministry, the French CNRS, and the Italian INAF. Additional
contributions were received from Konkoly Observatory in Hungary, INAOE in
Mexico, and PI grants from Lund Observatory, IAP Potsdam, MPIA Heidelberg.

The HARPS3 instrument is being built by the Terra Hunting Experiment
Consortium led by University of Cambridge Cavendish Laboratory.

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