Auto Farming Bot: APY Normalization and Gas Minimization

We design and develop full-cycle blockchain solutions: from smart contract architecture to launching DeFi protocols, NFT marketplaces and crypto exchanges. Security audits, tokenomics, integration with existing infrastructure.
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Auto Farming Bot: APY Normalization and Gas Minimization
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~3-5 days
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Keeping funds in a single yield protocol and manually moving them when rates change means constantly losing to those with automation. APY on Aave, Compound, Curve, Convex, Yearn changes every block. Manual monitoring gives reaction times in hours — a bot reacts in minutes or even seconds. The problem is exacerbated by gas wars and MEV bots that front-run transactions. Without automatic yield farming, you can lose up to 30% of potential yield due to delayed protocol rotation. The main challenge is building a system where the cost of rotation (gas) does not eat the gains from switching protocols, and the switching logic is resilient to interface changes. Our DeFi automation approach, refined through 5 years of experience, covers this in detail.

How to Build an Auto-Farming Bot Without Gas Losses?

Common Pitfalls

Blind APY Chasing Without Gas Consideration

The most common mistake in auto-farming: the bot sees a 2% APY difference between Aave and Compound and rotates. On Ethereum mainnet, a withdrawal + approval + deposit transaction costs $20–50 in gas. On a $1000 position, that's 2–5% of the principal — the rotation becomes unprofitable. Each unnecessary rotation can cost $50 in gas. Manual monitoring averages a 6-hour delay; a bot reacts in 2 minutes — that's 180x faster. Rule: rotate only if (APY_new - APY_current) * position_size * time_horizon > gas_cost * safety_factor. time_horizon is the expected stay in the new protocol, safety_factor is 2–3× to account for uncertainty. On L2 (Arbitrum, Optimism), fees are $0.1–0.5, so the threshold is much lower. Proper gas optimization can save you up to $5,000 per month on a $100k position.

Unsynchronized Yield Data

APY in DeFi is not a fixed rate. It's a projection of the current pool state onto a year. Compound's supplyRatePerBlock is calculated from utilization rate right now. Aave adds reward tokens (AAVE staking) that need to be claimed and accounted separately. Convex shows a boosted APY that depends on your vault's veCRV balance. Without APY normalization, you're comparing apples to oranges. Our yield farming bot for Aave (Aave bot) and Compound (Compound bot) uses a unified methodology: base yield (from protocol) + reward yield (from emissions, normalized to USD) + compound effect (reinvestment), all smoothed with a 7-day moving average. This avoids false triggers from short-term spikes.

Why APY Normalization Matters?

Without normalization, you compare apples to oranges. Our bot applies an adapter for each protocol that normalizes yield to a common standard. For example, for Aave V3 we account for liquidityRate + aToken reinvestment, for Compound V3 — supplyRate + compensation tokens. All data is smoothed with a 7-day moving average to filter out short-term spikes. This provides a stable signal for decision making and enables yield arbitrage between pools.

How to Calculate the Rotation Threshold Considering Gas?

The threshold is computed dynamically for each protocol pair. We use the formula:

  • gas_cost — gas estimate via eth_estimateGas for Multicall3 (single tx for approve+deposit).
  • profit = (APY_new - APY_current) * position * expected_stay_days / 365.
  • Rotation occurs if profit > gas_cost * 2 (double buffer).

For multi-chain farming, bridge cost is also considered — it can be significant, so we typically only allow bridge rotations with manual confirmation. Potential additional yield with proper gas optimization is up to $5,000 per month on a $100k position.

How to Build a Rotation Strategy: Step-by-Step

  1. Normalize APY — bring each protocol's yield to a common standard using a 7-day moving average.
  2. Calculate gas threshold — for each protocol pair, dynamically compute the APY difference at which rotation is profitable.
  3. Apply risk filters — exclude unaudited protocols, low-liquidity pools, forbid bridge rotations without manual confirmation.
  4. Test on a fork — run all scenarios on a real network state using Foundry vm.createFork.
  5. Deploy and monitor — launch the keeper bot with Telegram notifications and alerts on failures.

Auto-Farming Bot Architecture

System Layers

  • Data layer: Aggregates data from protocols. Each protocol has its own adapter. Aave V3 via IPool.getReserveData(), Compound V3 via CometSupply events + getSupplyRate(), Curve via get_virtual_price() and Convex rewardRate. Data is written to Redis with a 60-second TTL to avoid hitting the RPC on every calculation.
  • Strategy engine: Decision-making logic. Evaluates the current position, all rotation candidates, calculates net APY considering gas, and makes the decision. Also includes risk filters: avoid unaudited protocols, no more than X% in one protocol, exclude pools with TVL below threshold (liquidity risk on withdrawal).
  • Execution layer: Builds and sends transactions. Uses viem for typed contracts via ABI and correct gas estimation. For batching: Multicall3 combines approve and deposit in one transaction, reducing gas by 30–40%.
  • Monitoring: Telegram notifications for each rotation (amount invested, destination, expected APY), alerts on transaction errors, dashboard with position history via The Graph or custom indexer.

On-chain vs Off-chain Logic

Two approaches:

  • Off-chain keeper (simpler, cheaper): bot runs on a server, holds a private key (or connects to Gnosis Safe via Safe Transaction Service API), sends transactions directly. Downside: dependence on server uptime and trust in the server.
  • On-chain vault with keeper (more complex, safer): a smart contract vault holds funds; the keeper (bot) only calls rebalance(). The strategy is encoded in the contract — users can verify the logic. The keeper's private key can only call rebalance(), not withdraw funds. This is the standard for Yearn-style vaults, described in EIP-4626.

For a personal bot with a small amount, an off-chain keeper suffices. For a protocol with third-party funds, only an on-chain vault with an audit.

Characteristic Off-chain keeper On-chain vault
Fund security Medium (key on server) High (contract controls)
Complexity Low High (audit, gas)
Transparency Operator only Full (contract verified)

Multi-Network Support

Network Features Major Protocols
Ethereum mainnet High gas, high TVL Aave V3, Compound V3, Curve, Convex
Arbitrum Cheap gas, rich DeFi Aave V3, GMX, Radiant, Camelot
Optimism Cheap gas, Velodrome Aave V3, Velodrome, Extra Finance
Base New, growing TVL Aave V3, Aerodrome, Moonwell

Multi-chain farming requires bridge rotations, which we usually don't automate due to bridge exploit risks. For multichain bots, we only use manual confirmation for bridge operations.

Tech Stack

TypeScript + viem for on-chain interactions. Node.js for the keeper process. Redis for data caching. PostgreSQL for rotation history and analytics. Docker for deployment. PM2 or systemd for process monitoring.

Hardhat/Foundry for on-chain vault contracts, if we choose that path. Tests on forked mainnet via Foundry vm.createFork — we run rotation scenarios on the real network state.

What's Included in the Work

  • Analysis of selected protocols and networks, APY normalization.
  • Development of adapters for each protocol (including data collection and transaction submission).
  • Implementation of the strategy engine with gas awareness and risk filters.
  • Monitoring setup (Telegram, dashboard).
  • Testing on forked mainnet (Foundry).
  • Documentation for setup and operation.
  • 3-month code warranty.

Time Estimates

  • Off-chain keeper bot for 2–3 protocols on one network: 3–5 days.
  • With APY normalization, gas consideration, and Telegram monitoring: closer to 1 week.
  • On-chain Yearn-style vault with separate strategies: 2–4 weeks including tests.

Cost is calculated after specifying protocols and networks. With 5 years of experience and 15+ completed projects, we ensure reliable DeFi automation. Get a consultation — we'll assess your project for free.

Example config for off-chain keeper
protocols:
  - name: aave-v3-eth
    adapter: aave-v3
    pool: 0x...
    gas_estimate: 200000
  - name: compound-v3-eth
    adapter: compound-v3
    comet: 0x...
strategy:
  min_profit_multiple: 2
  max_rotations_per_day: 10
  excluded_protocols:
    - convex (outdated audit)

Order development from us — we've built 15+ such systems for various protocols. We guarantee transparent code and regular adapter updates. Contact us to discuss your scenario. Get a consultation — we'll assess your project for free.

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.