Chorus One
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Chorus One

Analyzing MEV Instances on Solana — Part 2


This is the second article of the Solana MEV outlook series. In this series, we use a subset of transactions to extrapolate which type of Maximum Extractable Value (MEV) is being extracted on the Solana network and by whom.

MEV is an extensive field of research, ranging from opportunities created by network design or application-specific behaviour to trading strategies similar to those applied in the traditional financial markets. As a starting point, our attempt is to investigate if sandwich attacks are happening. In the first article, we examined Orca’s swap transactions searching for evidence of this pattern. Head to Solana MEV Outlook — part 1 for a detailed introduction, goals, challenges and methodology. A similar study is performed in the present article. We are going to look at on-chain data, considering approximately 8 h of transactions on the Raydium DEX. Given the magnitude of 4 x 10⁷ transactions per day, considering only Decentralized Exchanges (DEX) applications on the Solana ecosystem. This simplification is done to get familiarity with data, extrapolating as much information as we can to extend towards a future analysis by employing a wider range of transactions.

Raydium DEX

Raydium is a relevant Automated Market Maker (AMM) application on the Solana ecosystem, the second program in the number of daily active users and the third in terms of program activity.

Fig. 1: Solana programs activity breakdown, source from

Raydium program offers two different swap instructions:

  1. SwapBaseIn: take as input the amount of token that the user wants to swap, and the minimum amount of token in output needed to avoid excessive slippage.
  2. SwapBaseOut: take the amount of token that the user wants to receive, and the maximum amount of token in input needed to avoid excessive slippage.

Although the user interface (“UI”) interacting with the smart contract sets the swap instruction to use the first instruction type, leaving SwapBaseIn responsible for 99.9% of successfully executed swap instructions:

Fig. 2: Swap instructions from here.

We built a dataset, extracting the inputs from the data byte array passed to the program, and the actual swap token amounts by looking at the instructions contained in the transaction. Comparing the minimum amount of tokens specified in the transaction and the actual amount the user received, we estimate the maximum slippage tolerance for every transaction. By computing the corresponding slippage, we obtain the histogram:

Fig 3: Number of transactions per slippage.

The default value for slippage on the Raydium App is set to 1%. We can assume that at least 28% of transactions use the default value. Since it is not possible to know the state of the pool when creating the transaction, this number could be a bit higher.

It can be assumed that nearly 0% of slippage values are only achieved by sophisticated investors using automated trading strategies. Orca swaps’ histogram, presented in Fig 2.2 of the previous article, shows a peak in transactions with slippage of around 0.1%. On Raydium, a relevant proportion of transactions lies below 0.05%. This fact can suggest that trading strategies with lower risk tolerance, i.e price-sensitive strategies correspond to 25% of the swaps transactions (accumulating the first two bars in the histogram).

Other evidence of automated trading being common on this DEX is that on average, 40% of transactions fail, mostly because of the tight slippage allowed by user settings.

Fig 4.1: Number of transactions successfully executed (blue) and reverted (gray) by Raydium program. Source:
Fig 4.2: Error messages in reverted transactions breakdown. Source:


We are considering more than 30,000 instructions interacting with the Raydium AMM program, from time 02:43:41 to time 10:25:21 of 2022–04–06 UTC. For statistics purposes, failed transactions are ignored.

Although 114 different liquidity pools are accessed during this period, the SOL/USDC pool is the most traded pool, with 4,000 transactions.

Fig. 5: 40 most relevant pools — representing 75% of all Raydium swap transactions.

The sample contains 1366 different validators as leaders in more than 35000 slots we are considering, representing 93% of the total stake and 78% of the total validator population by the time of writing, according to Solana Beach.

Fig. 6: The proportion of slots for each of the 20 most relevant leaders.

Of 5,101 different addresses executing transactions, 10 accounts concentrate 23% of the total transactions. One of the most active accounts on Raydium, Cwy…3tf also appears in the top 5 accounts in Orca DEX.

Fig. 7: Top 10 accounts by number of Raydium swaps

The graph below shows the total number of transactions for accounts with at least two transactions in the same slot. If used as a proxy to identify automated trading, on average 9 different accounts can be classified:

  • high-frequency behaviour: accounts with 3 successful executed transactions per second;
  • moderate frequency: accounts with approximately 1 transaction per second.
Fig. 8: number of transactions for the 60 more active accounts with multiple transactions in at least one slot

We can also look at the pools where these accounts execute more often. It is possible to notice they tend to specialize in different pools. The table below shows the two pools with more transactions for each of the 5 more active addresses:

By deep-diving into account activity by pool, we can see that two accounts concentrate transactions on WSOL/USDT pool; one account is responsible for half of all transactions in the mSOL/USDC pool; most of the transactions in the GENE/RAY pool are done by only one account (Cwy…3tf).

Fig. 9: Transactions owner breakdown for the 5 pools with the highest number of transactions. Each different account is represented by a new color.


Searching for sandwich behaviour means we need to identify at least 3 transactions executed in the same pool in a short period of time. For the purpose of this study, only consecutive transactions would be considered. The strategy implies the first transaction to be in the same direction of the sandwiched transaction and a transaction in the opposite direction of the initial trade, closing out the positions of the MEV player.

Fig. 10: 3 steps of a sandwich attack

The need for price impact implies a dependence on the amount of capital available to be used in every trade. Some MEV strategies can be performed atomically, with a sequence of operations executed in the same transaction. These strategies usually benefit from flash loans, allowing for anyone to apply it disregarding the capital they have access to. This is not the case for sandwich attacks, since the profit is realized after the successful execution of all the transactions (Fig. 10).

As shown in the first article, the amount of capital needed in order to create value depends on the Total Value Locked in the pool — the deeper the liquidity, the more difficult it is to impact the price. Head to Fig. 2.4 of the first article for the results of simulation into the Orca’s SOL/USDC pool. The figure shows the initial capital needed in order to extract a given percentage of the swap.

In the current sample, we have found 129 blocks with more than three swaps in the same pool, most of the swaps are in the same direction — no evidence of profit-taking. As shown in Fig. 11 below, the pool SAMO_RAY is the pool with more occurrences of multiple swaps in the same slot.

Fig. 11: pools presenting more than 3 swaps in a single slot

When searching for blocks and pools with swaps in opposite directions as a proxy to profit-taking, 9 occurrences are left with a potential sandwich attack pattern, as shown in the table below (Fig 12). After further investigation of the transactions and the context in which the instructions were executed, it is fair to assume the operations are related to arbitrage techniques between different trading venues or pools.

Fig. 12: slots and pools with more than 3 swaps and evidence of profit-taking


In this report, we were able to access the activity of the Raydium DEX. The conclusions are based on a limited amount of data, assuming our sample is comprehensive enough to reflect the general practices involving the dApp.

It is possible to notice relevant activity from automated trading and price-sensitive strategies such as arbitrage, which corresponds to 25% of swap transactions. On average, only 40% of transactions are successfully executed and 72% of all reverted transactions fail because of small slippage tolerance. Approximately, 28% of transactions can be classified as manual trading, since they use the default slippage value.

Of 5101 different accounts interacting with the Raydium program, 10 accounts concentrate 23% of the total transactions. One of the most active accounts on Raydium, Cwy…3tf also appears in the top 5 accounts in Orca DEX transactions. This same account is responsible for 77% of swaps in the GENE/RAY pool.

There were 9 occurrences of a potential pattern of a Sandwich attack discarded after further investigation.

It is important to mention that this behaviour is not only dependent on the theoretical possibility but largely biased by market conditions. The results in $13m MEV during Wormhole Incident and $43m Total MEV from Luna/ UST Collapse on Solana demonstrate the increase in profit extracted from MEV opportunities during stressful scenarios. Although the study focuses attention on different strategies and does not mention sandwich attacks, the probability of this strategy happening can also increase, given the smaller liquidity in pools (TVL) and the occurrence of trades with bigger size and slippage tolerance.

This is my first published article. I hope you enjoyed it. If you have questions, leave your comment below and I will be happy to help.



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I’m passionate about data and enjoy coding. Follow me: @ethalita_