/mev-analysis

name: mev-analysis
description: MEV exposure assessment, sandwich attack detection, and protection strategies for Solana DEX trading

MEV Analysis for Solana DEX Trading

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.

What Is MEV on Solana?

MEV occurs when someone with transaction ordering power profits at other traders' expense. On Solana, the MEV supply chain works as follows:

1. **You submit a swap** through an RPC endpoint 2. **Searchers observe** your transaction (via RPC forwarding, block engine access, or leader TPU sniffing) 3. **Searcher constructs a profitable bundle** (e.g., sandwich your swap) 4. **Bundle submitted to Jito block engine** with a tip to the validator 5. **Validator includes the bundle** in the block, earning the tip 6. **You receive worse execution**; the searcher profits the difference

How Solana MEV Differs from Ethereum

| Aspect | Ethereum | Solana | |--------|----------|--------| | Block time | 12 seconds | ~400ms slots | | Mempool | Public mempool | No mempool (but tx visible in transit) | | Ordering | Proposer-builder separation (PBS) | Jito block engine (~85%+ validators) | | Bundle system | Flashbots bundles | Jito bundles with tips | | MEV cost | Gas priority fees | Jito tips (SOL) | | Latency pressure | Moderate | Extreme (sub-100ms decisions) |

Key Solana-specific factors:

• **No public mempool**: Transactions flow RPC → TPU → Leader, but searchers tap into this flow via Jito's block engine and modified validators
• **Known leader schedule**: The leader (block producer) schedule is known ~2 epochs ahead, letting searchers target specific leaders
• **Jito dominance**: ~85%+ of validators run the Jito-modified client, making Jito bundles the primary MEV vector
• **Speed**: 400ms slots mean MEV bots must operate in microseconds, favoring co-located infrastructure

MEV Types on Solana

1. Sandwich Attacks

The most common MEV attack against retail traders.

**Mechanics:**

1. Attacker sees your pending swap: Buy 10 SOL worth of TOKEN_X
2. Front-run:  Attacker buys TOKEN_X first  → price rises
3. Your swap:  You buy TOKEN_X at higher price → worse execution
4. Back-run:   Attacker sells TOKEN_X         → profits the difference

**Your loss** = price impact from front-run + attacker's profit margin **Attacker profit** = your_loss - jito_tip - transaction_fees

**Risk factors:**

• Trade size: Larger trades = more profitable to sandwich
• Token liquidity: Illiquid tokens = easier price manipulation
• Slippage setting: Wide slippage = more room for the attacker
• Pool type: CPMM pools more vulnerable than CLMM pools at concentrated ranges

2. Arbitrage (Cross-DEX)

Searchers capture price discrepancies between DEXes.

Pool A: TOKEN_X = 1.00 USDC
Pool B: TOKEN_X = 1.02 USDC
→ Buy on A, sell on B, profit 0.02 USDC per token (minus fees)

This is generally **beneficial** to the market — it equalizes prices across venues. However, your trade may trigger the arbitrage opportunity that the searcher captures.

3. Liquidation Extraction

When DeFi positions (Solend, Marginfi, Kamino) become undercollateralized, searchers race to liquidate them and claim the liquidation bonus (typically 5-10%).

4. JIT (Just-In-Time) Liquidity

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.

5. Back-Running

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.

Estimating MEV Exposure

Estimate your MEV risk before executing a trade:

import httpx

def estimate_mev_risk(
    trade_size_sol: float,
    pool_liquidity_usd: float,
    slippage_bps: int,
    token_daily_volume_usd: float,
) -> dict:
    """Estimate sandwich attack profitability for a given trade.

    Returns risk assessment with estimated cost and recommendations.
    """
    # Trade as percentage of pool liquidity
    sol_price = 150.0  # approximate; fetch live price in production
    trade_usd = trade_size_sol * sol_price
    trade_pct_of_pool = (trade_usd / pool_liquidity_usd) * 100

    # Estimated price impact from constant-product AMM
    # price_impact ≈ trade_size / pool_liquidity (simplified)
    price_impact_bps = int(trade_pct_of_pool * 100)

    # Sandwich profitability: attacker captures portion of slippage headroom
    # Rough model: sandwich_profit ≈ 0.5 * slippage_headroom * trade_size
    slippage_headroom_bps = slippage_bps - price_impact_bps
    if slippage_headroom_bps < 0:
        slippage_headroom_bps = 0

    sandwich_profit_usd = (slippage_headroom_bps / 10000) * trade_usd * 0.5
    jito_tip_cost = 0.001 * sol_price  # ~0.001 SOL typical tip
    tx_fees = 0.000015 * sol_price * 2  # two transactions for sandwich

    net_mev_profit = sandwich_profit_usd - jito_tip_cost - tx_fees
    is_profitable_to_sandwich = net_mev_profit > 0.10  # $0.10 minimum

    # Volume ratio indicates MEV bot attention level
    volume_ratio = trade_usd / max(token_daily_volume_usd, 1)

    risk_level = "LOW"
    if is_profitable_to_sandwich and trade_pct_of_pool > 1.0:
        risk_level = "HIGH"
    elif is_profitable_to_sandwich or trade_pct_of_pool > 0.5:
        risk_level = "MEDIUM"

    return {
        "risk_level": risk_level,
        "trade_pct_of_pool": round(trade_pct_of_pool, 2),
        "estimated_price_impact_bps": price_impact_bps,
        "slippage_headroom_bps": slip