# Opportunity taxonomy (reference)

**Last Updated:** 2026-04-13  
**Document Version:** 1.0  
**Status:** Reference (not legal or trading advice)

This document groups common **MEV and arbitrage** patterns as **state transition inefficiencies** in AMMs and surrounding orderflow. Wording is descriptive; viability varies by chain, fee regime, mempool visibility, and regulation.

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## 1. Cross-DEX arbitrage (atomic)

**Idea:** The same asset pair trades at different effective prices across venues (different pools or routers).

**Execution sketch:** Buy on the relatively cheap pool and sell on the relatively expensive one within **one atomic** on-chain transaction (often via a router or custom executor).

**Typical traits:** High event frequency for liquid pairs; per-event profit often small; **very high** competition.

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## 2. Triangular and multi-hop arbitrage

**Idea:** Mispricing along a **cycle** of pools, for example `A → B → C → A`, not necessarily visible as a two-pool spread.

**Sources:** Routing blind spots, stale aggregator paths, fee tier fragmentation, thin intermediate legs.

**Typical traits:** Medium frequency; pathfinding cost; size limited by the weakest pool on the path.

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## 3. Backrun arbitrage

**Idea:** Profit from **another actor’s trade** that moves prices: observe a pending or included trade, then trade **into** the post-trade price (often immediately after, hence “backrun”).

**Dependencies:** Mempool or private-flow visibility, fast simulation, and competitive ordering.

**Typical traits:** Very high event count on public mempools; outcome depends on **ordering** and builder/relay dynamics.

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## 4. Sandwich (front-run + back-run)

**Idea:** Place trades **before and after** a victim swap so the victim executes at a worse price; the searcher unwinds into the moved price.

**Typical traits:** Potentially large profit per victim trade; **high** execution and revert risk; **wallet protections**, builder policies, and **legal/reputational** exposure vary by jurisdiction and venue.

**Internal policy note:** Treat user-harming extraction as a **compliance and product ethics** topic, not only a technical optimization. Many teams **exclude** sandwich strategies by policy.

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## 5. Liquidation arbitrage

**Idea:** Capture **liquidation incentives** (bonus, spread, or protocol-defined rewards) when positions become undercollateralized.

**Typical traits:** Burst-driven in volatility; can require **inventory** and gas bidding; protocol-specific rules dominate economics.

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## 6. Cross-chain arbitrage

**Idea:** Price differences for the same economic exposure across chains.

**Constraints:** Bridging latency, reorg risk, inventory on each chain, and trust assumptions of bridges.

**Typical traits:** Medium frequency for some pairs; **capital-heavy**; operational complexity dominates.

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## 7. Oracle and pricing lag

**Idea:** A protocol’s **on-chain price** lags tradable spot; actors who understand update rules may trade around the lag (within protocol constraints).

**Typical traits:** Infrequent relative to DEX arb; requires **deep protocol** knowledge; high impact when it appears.

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## 8. AMM curve shape (convexity and fee tiers)

**Idea:** Non-linear pricing (constant product, concentrated liquidity, stable swaps) means large trades create **local** mispricings that other trades can close.

**Typical traits:** Often embedded inside other categories (backrun, triangular, cross-DEX) rather than a standalone label.

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## Related

- [PRODUCTION_PIPELINE.md](PRODUCTION_PIPELINE.md) — how these opportunities are detected and acted on in a production-shaped stack  
- [ARCHITECTURE.md](ARCHITECTURE.md) — diagrams  
- [SCALING_AND_REALITY.md](SCALING_AND_REALITY.md) — which patterns scale under competition