Imagine: the mempool is congested, ETH price on Uniswap and Curve diverges by 0.3% in a second. Your MEV bot must assemble a bundle and send it to Flashbots before dozens of competitors. A delay of more than 10 ms and the profit goes to someone else. We build infrastructure that wins this race: from simulation to bundle submission. Average profit per arbitrage trade is 0.05–0.15% — on a $100k volume that's $50–150 per second. Our bots specialize in DEX arbitrage and position liquidations on Aave, Compound, and other protocols. The task: detect a profitable opportunity, simulate its execution, and send the bundle through Flashbots faster than competitors. Over 5 years, we have launched more than 50 such systems. Each bot is customized to specific strategies and client infrastructure.
How Does an MEV Bot Work?
The bot consists of an off-chain detector, an on-chain executor, and a relay network. The detector analyzes the mempool and blockchain state, the executor performs swaps via smart contracts, and the relay sends bundles directly to builders, bypassing the public mempool.
How We Detect Arbitrage Opportunities
Arbitrage between DEXes is the primary income source. We monitor prices on Uniswap, Curve, Balancer. When price divergence occurs, we compute the optimal swap size using a mathematical model. For Uniswap v2, we use the analytical formula:
optimal_amount_in = sqrt(reserve0 * reserve1 * 997 / 1000) - reserve0
For concentrated liquidity v3, we need step-by-step calculation via QuoterV2. Flash loan fees from Aave v3 are 0.05% — all calculations account for this cost.
Why Transaction Simulation Is Critical
Before sending a bundle, you must know the exact profit after gas and bribe. We use our own simulation engine built on revm (Rust EVM implementation). It executes transactions on a forked copy of mainnet in 1–3 ms, 10–20 times faster than standard eth_call. This advantage allows us to win front-running in competitive situations. The engine uses the Shanghai revision with PUSH0 support. We fork mainnet via eth_createAccessList and execute transactions in an isolated environment. The concept of MEV is described in Ethereum Foundation research.
| Strategy |
Average Profit Margin (3 months) |
Frequency per day |
| Uniswap v2 arbitrage |
0.05–0.15% |
200–500 |
| v3 arbitrage |
0.02–0.10% |
300–800 |
| Aave liquidations |
5–10% of liquidated position |
10–50 |
| Bundle Submission Method |
Latency |
Failure Risk |
Setup Complexity |
| Direct to builder |
<10 ms |
Low |
High |
| Flashbots relay |
20–50 ms |
Medium |
Medium |
| Public mempool |
>100 ms |
High |
Low |
Problems We Solve
High latency. Public RPCs add 50–200 ms. For liquidations this is acceptable, but for arbitrage it's critical. We set up our own geth node with WebSocket subscription to txpool.
Gas optimization. Every extra wei of gas reduces profit. We compile contracts with maximum optimization in Foundry and use minimal executor code.
Competition. Dozens of bots react to the same opportunity simultaneously. The winner is the one who offers a higher bribe to the builder. We analyze MEV-Boost auction history and select the optimal bribe.
Unlike sandwich attacks, our arbitrage and liquidation strategies do not harm regular users. Sandwich attacks manipulate prices around a user's transaction, causing direct damage. We have consciously avoided such practices in favor of legitimate MEV extraction methods.
How We Develop an MEV Bot
The development process is divided into several stages, each agreed with the client.
Strategy and Market Analysis
Determine which MEV sources are priority (arbitrage, liquidations, triangulation). Assess the capital required to start and expected returns.
Architecture Design
Design the off-chain detector and on-chain executor. Choose language: Python for prototype, Rust for production. Determine which DEXes and protocols to support.
Implementation
Write detector, executor, and relay code. Integrate the revm-based simulator. Perform unit testing and fork tests on mainnet.
Testing and Optimization
Run on testnet with real data. Optimize gas, set profit thresholds, test with various builders.
Deployment and Monitoring
Deploy the node, contracts, set up logging in Graylog. Provide 24/7 monitoring via configured alerts.
Bot Architecture
Off-chain Detector
Written in Python for prototype, then Rust for production. Subscribes to mempool via WebSocket, parses transactions, calculates potential profit. For liquidations, it uses The Graph to monitor position health factors.
On-chain Executor
Simple Solidity smart contract. Accepts token address, amount, router. Performs swap via Uniswap or Curve router. Checks that profit is above a minimum threshold (minProfit). Protects against reentrancy with nonReentrant from OpenZeppelin.
Relay Network
Bundle submission is done via Flashbots MEV-Boost directly to builders (beaverbuild, rsync, titan). We do not rely on the public mempool.
What You Get After Development
Full source code of the bot (detector, executor, relay). Documentation for installation, configuration, and monitoring. Access to git repository with change history. Team training on bot management and log interpretation. 2 months of support with guaranteed stable operation. Option to extend support on individual terms.
Our Expertise
We have specialized in MEV for over 5 years. Developed and launched bots for 50+ clients. We ensure transparency: you get full access to code and operation logs. We provide a warranty on stable operation for the first 2 months. Gas savings up to 20% through executor optimization. Potential profit from 0.05% per trade, average daily profit from $500 to $5000.
Contact us for a consultation — we will assess your strategy and propose the optimal solution. If you want an MEV bot optimized for your infrastructure, get in touch for a detailed audit. Order turnkey MEV bot development and start earning on arbitrage within 4 weeks.
DeFi Protocol Development
We design modular DeFi protocols where the math of stablecoins, liquidity, and oracles works flawlessly. Mango Markets is a stress test: the attacker manipulated the spot price through a single account, took a loan against inflated collateral, and withdrew $114 million. The oracle took the price from a single source without TWAP. Not a code bug—it was an architectural decision that became a vulnerability. Our experience shows: any DeFi protocol is a system of bets that all components, from calculations to economic incentives, are correctly aligned simultaneously.
We don't write code under the 'if it works, don't touch it' mindset. We model stress scenarios: cascading liquidations, depegs, flash loans. Only then do we build events that won't break the protocol.
Why are oracles a critical component of DeFi?
Most major DeFi hacks started with oracle manipulation. Let's break down the three layers we use in every project.
Spot price as oracle—not an option. Uniswap v2 spot price can be shifted by a flash loan in one transaction. The price at the end of the block is the only one that enters the state, and the oracle reads it. Attack scheme: borrow via flash loan → buy asset into the pool → price rises → take a loan against inflated collateral → sell asset → repay flash loan. One transaction.
TWAP as protection. Uniswap v3 observe() averages the price over a period (30 minutes). Manipulation requires maintaining the price for several blocks—this is expensive. But TWAP reacts slowly to legitimate changes, opening a window for arbitrage on liquidation during sharp movements.
Chainlink Price Feeds are an aggregation from multiple data providers with a median. Standard for lending. Problem: heartbeat 1–24 hours and deviation threshold 0.5%. If the price doesn't move, the feed may not update for a day. In volatile markets—lag.
| Oracle |
Mechanism |
Manipulation Protection |
Latency |
| Chainlink |
Median from independent providers |
High (decentralization) |
Up to 24h at 0% movement |
| Uniswap v3 TWAP |
Average price over N blocks |
High (hard to maintain) |
30 min – 1 h |
| Pyth Network |
Cross-chain low-latency |
Medium (dependent on publisher) |
Seconds |
In production, we use a two-tier check: Chainlink aggregator + Uniswap v3 TWAP as a verifier. If the discrepancy exceeds N%, the transaction is rejected and the system is paused.
How to protect a DeFi protocol from flash loan attacks?
Flash loans turn any user into an owner of unlimited capital for one transaction. Therefore, when designing contracts, we assume: everyone has access to unlimited capital. This completely changes the threat model.
Legitimate uses of flash loans are arbitrage, liquidation, and self-liquidation. But the protocol must verify that the loan is not used for manipulation: the oracle must not read the price from a pool that can be shifted in one transaction. We add checks on block.timestamp and minimum liquidity depth.
Key Components of DeFi Architecture
| Protocol Type |
Core Mechanism |
Main Risk |
| DEX (AMM) |
x*y=k or concentrated liquidity |
impermanent loss, oracle manipulation |
| Lending |
collateral ratio, liquidation |
bad debt during cascading liquidations |
| Yield aggregator |
auto-compounding strategies |
rug via strategy upgrade |
| Derivatives / Perps |
funding rate, mark price |
liquidation cascades, socialized losses |
| Liquid staking |
stETH-style rebasing |
depegging on mass unstake |
AMM: From x*y=k to Concentrated Liquidity
Uniswap v2 uses x * y = k. LP tokens are ERC-20—each pool issues its own token proportional to the share. Problem: liquidity is spread across the entire curve, most of it unused.
Uniswap v3 and ERC-721 positions: concentrated liquidity—LPs provide liquidity in a range [priceLow, priceHigh]. Capital efficiency up to 4000x for stable pairs. But ERC-721 breaks vault strategies built for ERC-20. Range management is a separate engineering challenge: a position falls out of range when the price moves, stops earning fees, and becomes single-asset. Protocols like Arrakis Finance automatically rebalance. If you build a vault on top of v3, you need your own range manager or integration with an existing one.
Slippage in v3 is calculated via sqrtPriceX96—96-bit fixed-point math. Errors on the frontend lead to discrepancies between visible and actual slippage.
Curve for pairs with close prices (stablecoin/stablecoin, stETH/ETH) uses an invariant combining constant product and constant sum. Lower slippage within the peg range. Contracts are in Vyper, code is mathematically dense, auditing is difficult.
Lending Protocols: Collateral, Liquidation, Bad Debt
LTV defines the maximum loan against collateral. Liquidation threshold is the level for liquidation. The difference is the buffer for the liquidator. Typical example: LTV 75%, liquidation threshold 80%, bonus 5%. If the price drops 20%+, the position is open for liquidation.
Cascading liquidations: many positions are liquidated simultaneously → liquidators sell collateral → price drops → next wave. LUNA/UST 2022 is a classic cascade.
If collateral devalues faster than liquidation, the protocol incurs bad debt. Aave uses a Safety Module (staked AAVE), Compound uses reserves. Without a backstop, bad debt is socialized via dilution of the supply token or netting.
Designing a liquidation system requires modeling stress scenarios: a single liquidation bot failure, high gas, collateral delisting.
Yield Farming and Incentive Mechanics
Liquidity mining distributes governance tokens to LP providers. Problem: mercenary capital—farmers come, sell tokens, leave. TVL is illusory.
Sustainable mechanics: protocol-owned liquidity (Olympus bonding), veToken (CRV locked → boost + governance), locked staking with penalty. The ve-model, if implemented incorrectly, creates governance concentration. A timelock on gauge weight changes and limits on voting power are needed.
What Our DeFi Protocol Development Includes
- Architectural documentation: contract interaction diagrams, liquidation stress tests, oracle calculations.
- Implementation in Solidity 0.8.x with OpenZeppelin 5.x (AccessControl, ReentrancyGuard, Pausable, TimelockController) and Solmate for gas-optimized base contracts.
- Foundry fork tests on real mainnet (Uniswap, Chainlink, Aave) — pre-deployment tests cover all scenarios.
- Audit: at least two independent auditors for TVL over $1M. Code4rena or Sherlock for bug bounty.
- Deployment with Gnosis Safe 3/5 multisig + timelock 48–72 hours.
- Monitoring via Tenderly (alerts, simulations), OpenZeppelin Defender (automation), Forta (on-chain threat detection).
- Post-launch support: updates, patches, upgrades via proxy.
Our Expertise and Experience
We have been developing DeFi protocols since 2020, delivering 30+ projects with a combined TVL of over $150 million. Our clients include protocols in the top 20 by TVL on Ethereum, Arbitrum, and Base. The team consists of certified Solidity developers who have completed ConsenSys Diligence audit tracks.
DeFi basic principles that we apply in practice.
Timelines
- DEX with AMM (Uniswap v2 fork): 6–10 weeks
- Lending protocol (Aave-style, single collateral): 3–5 months
- Yield aggregator with multiple strategies: 2–4 months
- Full-fledged DeFi protocol with governance: 5–8 months including audit
Cost is calculated individually—contact us for a project estimate.
Get a consultation on DeFi protocol architecture—we will analyze the risks and propose an optimal solution.
