Types of MEV Strategies
Types of MEV Strategies
In ZENMEV, we categorize MEV (Maximal Extractable Value) strategies into several core classes, each governed by distinct logic and time-sensitive execution. Although well-known techniques like arbitrage or front-running might appear straightforward, the real power unfolds when these tactics are combined or continuously run at scale by our AI (ZENBOTS). Below, we examine each major strategy in depth, focusing on concept, execution flow, and ethical considerations.
1. Arbitrage
Concept: Exploit price discrepancies for the same token across different DEXs (decentralized exchanges).
Buy Low, Sell High
Example: Token A is priced at $1.00 on Exchange X but $1.02 on Exchange Y. Buying at $1.00 and selling at $1.02 yields a near risk-free profit if the gas and slippage remain minimal.
ZENBOTS’ AI Factor
Slippage & Gas: Before executing, the system calculates whether slippage or gas fees outweigh the price difference.
Concurrence: Multiple bots might spot the same arbitrage; ZENBOTS assess if there’s enough volume left to secure a net gain.
Execution Flow
Atomic Swap: Often done in a single block, buying on one DEX and instantly selling on the other.
Profit Calculation:
If the final profit meets a threshold, the trade proceeds automatically.
Why It Matters: Arbitrage aligns DEX prices, improving market efficiency. ZENMEV harnesses it to achieve stable returns for stakers whenever a cross-DEX mismatch arises.
2. Front-Running
Concept: Identify a large, pending swap likely to move the price, then place your own transaction immediately before it in the same block.
Price Impact
If a major buy order is pending, a well-timed front-run can buy cheaper tokens just before that order, then sell once the big swap inflates the price.
Profit Formula:
Where
P_sell_afteris the inflated price resulting from the victim’s large buy.
ZENBOTS’ Ethical Constraints
Our system avoids extreme front-runs that produce significant negative externalities (like drastically increasing user slippage).
Nonetheless, smaller front-run scenarios can be profitable while retaining a measure of fairness.
Short Timing Window
Mempool scanning is crucial; ZENBOTS must detect the large order in real time and outbid other bots in gas to secure the earlier transaction spot.
Why It Matters: Front-running can generate quick, high-yield gains but must be tempered so it doesn’t degenerate into purely predatory tactics that erode user trust.
3. Back-Running
Concept: Jump in right after a large transaction to capture residual price movements often a short-lived rebound or “correction.”
Price Rebound Effect
If a huge sell drags a token’s price down momentarily, the next block might see partial recovery if liquidity or arbitrage bots intervene. A back-run can buy at the newly depressed price and sell upon rebound.
AI Consideration
Gas & Timing: The system calculates expected rebound magnitude minus gas costs.
Slippage: If the pool remains unsettled, the actual rebound might be smaller than predicted, so a trade becomes borderline or negative.
Execution Flow
Mempool watchers see the big transaction finalize, then quickly broadcast a buy at the newly lowered price. If the rebound occurs promptly, the profit is collected within the next block or two.
Why It Matters: While front-running is more notorious, back-running is arguably less disruptive, often helping restabilize the token price after a sudden shock. ZENMEV uses it to add incremental profits on trades that partially offset extreme volatility.
4. Sandwich Attacks
Concept: “Sandwich” a victim’s swap by placing a buy just before it, then a sell right after, effectively capturing the victim’s price movement for profit.
Mechanics
The bot’s buy transaction front-runs the victim, pushing the token’s price up. The victim then pays a slightly higher price. Immediately afterward, the bot’s sell transaction back-runs the victim, netting a margin from that artificially boosted price.
Profit:
Where
P_beforeis the original price,P_afteris the inflated price post-victim trade.
Ethical Angle
Sandwich attacks are frequently called predatory because they exploit unsuspecting users, effectively taxing every large swap.
ZENMEV imposes internal parameter controls so that the system doesn’t constantly saturate networks with destructive sandwich attempts, preferring a more balanced approach.
Short-Term Gains vs. Reputation
Excessive sandwiching can degrade the user experience on DEXs, leading to a loss of trust. ZENBOTS weigh the short-term profit against potential negative externalities.
5. Synergies & Combinations of Strategies
While the four categories arbitrage, front-running, back-running, and sandwich are typically described separately, real-world MEV can merge multiple tactics for optimal yield
Arbitrage + Front-Run
A large swap might present both a front-run opportunity and a cross-DEX arbitrage route. ZENBOTS can chain these if net returns remain positive.
Back-Run + Arbitrage
A massive sell could cause a token’s price to overshoot downward, prompting not only a back-run rebound but also an arbitrage across multiple liquidity pools.
Partial Sandwich + Slippage Minimization
Instead of fully sandwiching a victim, a more “moderate” approach might insert a front-run and skip the back-run if the price movement is uncertain. ZENBOTS adapt based on real-time data, ensuring no single strategy is blindly forced.
6. Why Strategy Matters for ZENMEV
ZENMEV leverages these MEV strategies to secure higher yields for stakers automating every step:
AI-Assisted Decision Making
Each scenario is scored on net profitability. If slippage, gas, or concurrency from rival bots overshadow potential gains, we skip it.
Ethical & Balanced
While front-run or sandwich tactics can yield big profits, ZENMEV aims for a balanced approach that doesn’t excessively harm everyday DeFi users. Overly predatory actions often short-circuit trust in liquidity pools.
Continuous Learning
The system refines success rates by monitoring how each executed trade performs. Over time, it learns which strategy combos yield the best results in varying chain conditions (gas spikes, NFT mania, etc.).
7. Putting It All Together
Below is a conceptual outline showing how these different MEV strategies might integrate in a single block or series of blocks:
The mempool reveals a high-value swap.
AI logic scores front-running, potential post-trade (back-run) rebounds, or a combined sandwich approach, factoring in concurrency and gas.
If net positive, it executes a sequence of trades.
Net Gains route back to the staking contract, automatically updating user shares.
8. Conclusion
Types of MEV Strategies in ZENMEV revolve around four primary techniques Arbitrage, Front-Running, Back-Running, and Sandwich Attacks and how they can combine or adapt for real-time profitability. ZENBOTS systematically determine whether each attempt meets a profit threshold after considering gas fees, slippage, and concurrency. Ultimately:
Arbitrage fosters market efficiency by aligning DEX prices, delivering consistent earnings.
Front-Running capitalizes on a victim’s pending swap, but must be handled prudently to avoid undue negative impact.
Back-Running captures aftershock rebounds, often less disruptive yet still profitable.
Sandwich Attacks provide large short-term gains but risk harming user trust if done excessively.
ZENMEV aims to exploit these strategies in a thoughtful, “ethical MEV” manner maintaining a stable environment while maximizing yields for stakers. By dynamically combining them under an AI-driven approach, the platform transforms ephemeral mempool events into robust, predictable yield generation.
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