mev-analysis

Name: mev-analysis
Author: agiprolabs

210installs53forks

How to install mev-analysis?

agiprolabs/claude-trading-skills/mev-analysis

$npx -y skills add agiprolabs/claude-trading-skills --skill mev-analysis

Installs into the current project.

Summary

MEV exposure assessment, sandwich attack detection, and protection strategies for Solana DEX trading

Files · 1

View on GitHub

SKILL.md

1	# MEV Analysis for Solana DEX Trading
2
3	Maximal Extractable Value (MEV) is the profit that validators and searchers can extract by reordering, inserting, or censoring transactions within a block. On Solana DEXes, MEV primarily manifests as sandwich attacks against swaps, cross-DEX arbitrage, and liquidation extraction. This skill covers detection, estimation, and protection strategies.
4
5	## What Is MEV on Solana?
6
7	MEV occurs when someone with transaction ordering power profits at other traders' expense. On Solana, the MEV supply chain works as follows:
8
9	1. You submit a swap through an RPC endpoint
10	2. Searchers observe your transaction (via RPC forwarding, block engine access, or leader TPU sniffing)
11	3. Searcher constructs a profitable bundle (e.g., sandwich your swap)
12	4. Bundle submitted to Jito block engine with a tip to the validator
13	5. Validator includes the bundle in the block, earning the tip
14	6. You receive worse execution; the searcher profits the difference
15
16	### How Solana MEV Differs from Ethereum
17
18	\| Aspect \| Ethereum \| Solana \|
19	\|--------\|----------\|--------\|
20	\| Block time \| 12 seconds \| ~400ms slots \|
21	\| Mempool \| Public mempool \| No mempool (but tx visible in transit) \|
22	\| Ordering \| Proposer-builder separation (PBS) \| Jito block engine (~85%+ validators) \|
23	\| Bundle system \| Flashbots bundles \| Jito bundles with tips \|
24	\| MEV cost \| Gas priority fees \| Jito tips (SOL) \|
25	\| Latency pressure \| Moderate \| Extreme (sub-100ms decisions) \|
26
27	Key Solana-specific factors:
28	- No public mempool: Transactions flow RPC → TPU → Leader, but searchers tap into this flow via Jito's block engine and modified validators
29	- Known leader schedule: The leader (block producer) schedule is known ~2 epochs ahead, letting searchers target specific leaders
30	- Jito dominance: ~85%+ of validators run the Jito-modified client, making Jito bundles the primary MEV vector
31	- Speed: 400ms slots mean MEV bots must operate in microseconds, favoring co-located infrastructure
32
33	## MEV Types on Solana
34
35	### 1. Sandwich Attacks
36
37	The most common MEV attack against retail traders.
38
39	Mechanics:
40	```
41	1. Attacker sees your pending swap: Buy 10 SOL worth of TOKEN_X
42	2. Front-run: Attacker buys TOKEN_X first → price rises
43	3. Your swap: You buy TOKEN_X at higher price → worse execution
44	4. Back-run: Attacker sells TOKEN_X → profits the difference
45	```
46
47	Your loss = price impact from front-run + attacker's profit margin
48	Attacker profit = your_loss - jito_tip - transaction_fees
49
50	Risk factors:
51	- Trade size: Larger trades = more profitable to sandwich
52	- Token liquidity: Illiquid tokens = easier price manipulation
53	- Slippage setting: Wide slippage = more room for the attacker
54	- Pool type: CPMM pools more vulnerable than CLMM pools at concentrated ranges
55
56	### 2. Arbitrage (Cross-DEX)
57
58	Searchers capture price discrepancies between DEXes.
59
60	```
61	Pool A: TOKEN_X = 1.00 USDC
62	Pool B: TOKEN_X = 1.02 USDC
63	→ Buy on A, sell on B, profit 0.02 USDC per token (minus fees)
64	```
65
66	This is generally beneficial to the market — it equalizes prices across venues. However, your trade may trigger the arbitrage opportunity that the searcher captures.
67
68	### 3. Liquidation Extraction
69
70	When DeFi positions (Solend, Marginfi, Kamino) become undercollateralized, searchers race to liquidate them and claim the liquidation bonus (typically 5-10%).
71
72	### 4. JIT (Just-In-Time) Liquidity
73
74	Searchers add concentrated liquidity to a CLMM pool just before a large swap and remove it immediately after, earning swap fees without sustained impermanent loss exposure. This is a sophisticated MEV form that can actually improve execution for the swapper.
75
76	### 5. Back-Running
77
78	Trading immediately after a large swap that moved the price, capturing the reversion. Less harmful than sandwiching because it does not worsen your execution — it profits from the market response to your trade.
79
80	## Estimating MEV Exposure
81
82	Estimate your MEV risk before executing a trade:
83
84	```python
85	import httpx
86
87	def estimate_mev_risk(
88	trade_size_sol: float,
89	pool_liquidity_usd: float,
90	slippage_bps: int,
91	token_daily_volume_usd: float,
92	) -> dict:
93	"""Estimate sandwich attack profitability for a given trade.
94
95	Returns risk assessment with estimated cost and recommendations.
96	"""
97	# Trade as percentage of pool liquidity
98	sol_price = 150.0 # approximate; fetch live price in production
99	trade_usd = trade_size_sol * sol_price
100	trade_pct_of_pool = (trade_usd / pool_liquidity_usd) * 100
101
102	# Estimated price impact from constant-product AMM
103	# price_impact ≈ trade_size / pool_liquidity (simplified)
104	price_impact_bps = int(trade_pct_of_pool * 100)
105
106	# Sandwich profitability: attacker captures portion of slippage headroom
107	# Rough model: sandwich_profit ≈ 0.5 * slippage_headroom * trade_size

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mev-analysis

210installs53forks

View on GitHub

How to install mev-analysis?

agiprolabs/claude-trading-skills/mev-analysis

$npx -y skills add agiprolabs/claude-trading-skills --skill mev-analysis

Installs into the current project.

Summary

MEV exposure assessment, sandwich attack detection, and protection strategies for Solana DEX trading

Files · 1

View on GitHub

SKILL.md

1	# MEV Analysis for Solana DEX Trading
2
3	Maximal Extractable Value (MEV) is the profit that validators and searchers can extract by reordering, inserting, or censoring transactions within a block. On Solana DEXes, MEV primarily manifests as sandwich attacks against swaps, cross-DEX arbitrage, and liquidation extraction. This skill covers detection, estimation, and protection strategies.
4
5	## What Is MEV on Solana?
6
7	MEV occurs when someone with transaction ordering power profits at other traders' expense. On Solana, the MEV supply chain works as follows:
8
9	1. You submit a swap through an RPC endpoint
10	2. Searchers observe your transaction (via RPC forwarding, block engine access, or leader TPU sniffing)
11	3. Searcher constructs a profitable bundle (e.g., sandwich your swap)
12	4. Bundle submitted to Jito block engine with a tip to the validator
13	5. Validator includes the bundle in the block, earning the tip
14	6. You receive worse execution; the searcher profits the difference
15
16	### How Solana MEV Differs from Ethereum
17
18	\| Aspect \| Ethereum \| Solana \|
19	\|--------\|----------\|--------\|
20	\| Block time \| 12 seconds \| ~400ms slots \|
21	\| Mempool \| Public mempool \| No mempool (but tx visible in transit) \|
22	\| Ordering \| Proposer-builder separation (PBS) \| Jito block engine (~85%+ validators) \|
23	\| Bundle system \| Flashbots bundles \| Jito bundles with tips \|
24	\| MEV cost \| Gas priority fees \| Jito tips (SOL) \|
25	\| Latency pressure \| Moderate \| Extreme (sub-100ms decisions) \|
26
27	Key Solana-specific factors:
28	- No public mempool: Transactions flow RPC → TPU → Leader, but searchers tap into this flow via Jito's block engine and modified validators
29	- Known leader schedule: The leader (block producer) schedule is known ~2 epochs ahead, letting searchers target specific leaders
30	- Jito dominance: ~85%+ of validators run the Jito-modified client, making Jito bundles the primary MEV vector
31	- Speed: 400ms slots mean MEV bots must operate in microseconds, favoring co-located infrastructure
32
33	## MEV Types on Solana
34
35	### 1. Sandwich Attacks
36
37	The most common MEV attack against retail traders.
38
39	Mechanics:
40	```
41	1. Attacker sees your pending swap: Buy 10 SOL worth of TOKEN_X
42	2. Front-run: Attacker buys TOKEN_X first → price rises
43	3. Your swap: You buy TOKEN_X at higher price → worse execution
44	4. Back-run: Attacker sells TOKEN_X → profits the difference
45	```
46
47	Your loss = price impact from front-run + attacker's profit margin
48	Attacker profit = your_loss - jito_tip - transaction_fees
49
50	Risk factors:
51	- Trade size: Larger trades = more profitable to sandwich
52	- Token liquidity: Illiquid tokens = easier price manipulation
53	- Slippage setting: Wide slippage = more room for the attacker
54	- Pool type: CPMM pools more vulnerable than CLMM pools at concentrated ranges
55
56	### 2. Arbitrage (Cross-DEX)
57
58	Searchers capture price discrepancies between DEXes.
59
60	```
61	Pool A: TOKEN_X = 1.00 USDC
62	Pool B: TOKEN_X = 1.02 USDC
63	→ Buy on A, sell on B, profit 0.02 USDC per token (minus fees)
64	```
65
66	This is generally beneficial to the market — it equalizes prices across venues. However, your trade may trigger the arbitrage opportunity that the searcher captures.
67
68	### 3. Liquidation Extraction
69
70	When DeFi positions (Solend, Marginfi, Kamino) become undercollateralized, searchers race to liquidate them and claim the liquidation bonus (typically 5-10%).
71
72	### 4. JIT (Just-In-Time) Liquidity
73
74	Searchers add concentrated liquidity to a CLMM pool just before a large swap and remove it immediately after, earning swap fees without sustained impermanent loss exposure. This is a sophisticated MEV form that can actually improve execution for the swapper.
75
76	### 5. Back-Running
77
78	Trading immediately after a large swap that moved the price, capturing the reversion. Less harmful than sandwiching because it does not worsen your execution — it profits from the market response to your trade.
79
80	## Estimating MEV Exposure
81
82	Estimate your MEV risk before executing a trade:
83
84	```python
85	import httpx
86
87	def estimate_mev_risk(
88	trade_size_sol: float,
89	pool_liquidity_usd: float,
90	slippage_bps: int,
91	token_daily_volume_usd: float,
92	) -> dict:
93	"""Estimate sandwich attack profitability for a given trade.
94
95	Returns risk assessment with estimated cost and recommendations.
96	"""
97	# Trade as percentage of pool liquidity
98	sol_price = 150.0 # approximate; fetch live price in production
99	trade_usd = trade_size_sol * sol_price
100	trade_pct_of_pool = (trade_usd / pool_liquidity_usd) * 100
101
102	# Estimated price impact from constant-product AMM
103	# price_impact ≈ trade_size / pool_liquidity (simplified)
104	price_impact_bps = int(trade_pct_of_pool * 100)
105
106	# Sandwich profitability: attacker captures portion of slippage headroom
107	# Rough model: sandwich_profit ≈ 0.5 * slippage_headroom * trade_size

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