The Blueprint Behind DeFi AMMs Without External Oracles

Automated Market Makers (AMMs) have become the backbone of modern decentralized finance, allowing users to trade directly against a liquidity pool rather than a counterparty. Most well‑known AMMs—such as Uniswap, Sushiswap, and Balancer—depend on external price oracles to provide reliable market data. Oracles translate off‑chain price information into on‑chain truth, but they introduce centralization risks, potential manipulation vectors, and additional gas costs.

This article explores how to design an AMM that can operate without external oracles, relying instead on on‑chain mechanics, on‑chain price discovery, and carefully engineered incentives to keep the system robust. It covers the underlying principles, security considerations, architectural blueprint, and practical steps for implementation.

Why Rely on Oracles in the First Place?

Oracles are typically used in DeFi to:

1. Feed external price feeds for assets that are not natively tokenized (e.g., fiat currencies, commodities).
2. Resolve price discrepancies between multiple AMMs or exchanges.
3. Trigger automated actions such as liquidation or margin calls.

The core challenge is that oracles require a trusted source or a consensus mechanism to decide which price is “correct.” If the oracle is compromised, the AMM may accept a falsified price, leading to losses, manipulation, or catastrophic failure.

In an oracle‑free design, the AMM must be able to infer asset prices from on‑chain data alone—typically the trades that happen within the pool itself. The intuition is simple: the price a user is willing to trade at is, by definition, a market‑derived price. The difficulty lies in turning this intuition into a reliable, manipulation‑resistant algorithm.

Vision: An Oracle‑Free AMM

The objective is to create a fully autonomous AMM that:

• Relies solely on on‑chain state (liquidity balances, swap events, internal pricing formulas).
• Maintains fair price discovery even in low‑volume or highly volatile markets.
• Provides incentives that align liquidity providers (LPs) and traders against potential attacks.
• Offers comparable performance to oracle‑driven systems in terms of liquidity depth, slippage, and user experience.

To achieve this, we combine several established concepts:

1. Constant‑Product (CP) Formula – The canonical AMM equation (x \times y = k).
2. Dynamic Fee Structures – Fees that respond to pool volatility and trade size.
3. Flash Loan Mitigation – Mechanisms that limit arbitrage opportunities within a single transaction.
4. Governance‑Driven Parameter Tuning – Allowing the community to adjust parameters such as fee tiers and pool limits without hardcoding them.

Core Principles of Oracle‑Free Pricing

1. On‑Chain Price Discovery

The price of token A in terms of token B is defined by the ratio of the two reserves after a swap:

[ P_{\text{A/B}} = \frac{R_B}{R_A} ]

Where (R_A) and (R_B) are the reserve balances of token A and token B, respectively. Because the swap itself enforces the invariant (R_A \times R_B = k), the resulting ratio automatically reflects the current market sentiment.

2. Liquidity Provision as a Price Signal

When a new liquidity provider deposits (x) units of A and (y) units of B, the pool’s invariant remains unchanged. The deposit is accepted only if the ratio (y/x) matches the current on‑chain price within an allowed slippage window. This ensures that deposits do not artificially inflate the pool’s price beyond what trades would justify.

3. Transaction‑Level Price Stability

To prevent a single transaction from manipulating the pool’s price, the AMM restricts the amount that can be swapped in a single block. A common strategy is to cap the trade size as a percentage of the pool’s total liquidity:

[ \text{MaxTrade} = \alpha \times \min(R_A, R_B) ]

Where (\alpha) is a configurable constant (e.g., 5 %). This reduces the feasibility of front‑running or sandwich attacks that attempt to skew the price for a single trade.

Handling External Price Impact

Even with an oracle‑free design, external market dynamics inevitably affect token prices. The AMM must respond to such shocks through:

1. Volatility‑Responsive Fees

Higher volatility should lead to higher fees to compensate LPs for the increased risk of impermanent loss. A simple algorithm is:

[ \text{Fee} = \text{BaseFee} + \beta \times \text{Volatility} ]

Where (\beta) is a sensitivity coefficient and volatility is measured over a sliding window of recent trades.

2. Arbitrage Detection

Since the AMM’s price is derived from on‑chain trades, it can detect price deviations from other on‑chain markets (e.g., other AMMs). An automated script can run off‑chain, monitor price differences, and trigger arbitrage trades that bring the pool’s price in line with the broader market. This self‑correcting behavior reduces price drift over time.

3. Slippage Tolerance

Traders specify a maximum slippage they are willing to accept. The contract calculates the output amount using the current invariant and compares it to the trader’s expectation. If the slippage exceeds the threshold, the trade is reverted. This protects traders from extreme price swings caused by large swaps.

Design Patterns for Oracle‑Free AMMs

Security Considerations

Even without oracles, an AMM can be vulnerable to several attack vectors. The following mitigations should be built into the protocol.

Front‑Running and Sandwich Attacks

Because the price is determined by the last trade, a malicious actor can place a transaction before and after a large swap to extract profit. Mitigations:

• Trade Caps: Limit trade size per block.
• Randomized Order Matching: Shuffle transaction order within a block (possible with optimistic roll‑ups).
• High Slippage Settings: Users set higher slippage tolerance to avoid being front‑run.

Flash Loan Attacks

An attacker can borrow a large amount of capital, manipulate the price, and repay the loan in a single transaction. To guard against this:

• Intra‑Transaction Price Check: Enforce that the price used for the swap is the same as the price before the transaction begins.
• Price Impact Penalty: Charge a fee that increases quadratically with the trade size relative to pool reserves.

Impermanent Loss

LPs face loss when the price of the deposited assets diverges from their original ratio. Mitigations include:

• High Liquidity Incentives: Reward LPs proportionally to the total volume generated.
• Dynamic Fee Adjustment: Increase fees during periods of high volatility to compensate LPs.

Reentrancy

Standard Solidity patterns must be followed: use Checks-Effects-Interactions, employ the nonReentrant modifier, and adopt pull‑over‑push patterns for withdrawals.

Architectural Blueprint

Below is a high‑level smart‑contract architecture for an oracle‑free AMM. Each component is responsible for a specific part of the protocol.

1. Pool Contract

Handles all pool operations:

• State Variables: reserveA, reserveB, k, fee, tradeCap, twapWindow.
• Functions:
  • addLiquidity(uint256 amountA, uint256 amountB): Deposits liquidity, mints pool tokens.
  • removeLiquidity(uint256 lpTokens): Burns LP tokens, returns underlying assets.
  • swapAForB(uint256 amountIn, uint256 minAmountOut): Executes a swap from A to B.
  • swapBForA(uint256 amountIn, uint256 minAmountOut): Executes a swap from B to A.
  • updateFeeParameters(): Called by governance to adjust fee tiers.
  • updateTradeCap(): Adjusts maximum trade size.
  • calculateTwap(): Returns the TWAP over the configured window.

2. LP Token Contract

ERC‑20 compliant token representing shares in the pool. Minted when liquidity is added and burned when removed.

3. Governance Contract

• Parameters: Holds configuration variables (feeTiers, tradeCap, twapWindow, etc.).
• Voting: Token holders can vote to change parameters.
• Execution: After a proposal passes, the governance contract calls the pool contract to apply changes.

4. Oracle‑Free Price Feed (Optional)

While the AMM does not rely on external oracles, an optional on‑chain feed can be provided for off‑chain services (e.g., price alerts). This feed is derived from the pool’s reserves and updated on each trade.

5. Flash Loan Protection Layer

A wrapper around the pool contract that checks price impact and trade size before allowing a swap to proceed.

Event Flow Example

1. Liquidity Addition

   • User sends amountA and amountB to addLiquidity.
   • Contract verifies the ratio matches the current price within slippageWindow.
   • LP tokens minted and transferred to the user.

2. Swap Execution

   • User calls swapAForB with amountIn and minAmountOut.
   • Contract calculates amountOut using the constant‑product formula, accounting for fees.
   • Checks that amountIn does not exceed tradeCap.
   • Transfers amountIn to the pool, updates reserves, then transfers amountOut to the user.

3. Governance Update

   • Proposal to increase fee tier to 0.5 % is submitted.
   • Voting period starts; token holders cast votes.
   • If quorum is reached, governance calls updateFeeParameters on the pool.

Building an AMM on a Testnet: Step‑by‑Step

Below is a simplified outline of how a developer might deploy an oracle‑free AMM on an Ethereum testnet such as Goerli. The code snippets are intentionally high‑level to focus on concepts rather than Solidity syntax.

1. Deploy Core Contracts

# Compile contracts
forge build

# Deploy Pool
forge create --constructor-args <tokenA> <tokenB> --private-key $PK

# Deploy LP Token
forge create LPToken --constructor-args <poolAddress> --private-key $PK

# Deploy Governance
forge create Governance --constructor-args <poolAddress> <lpTokenAddress> --private-key $PK

2. Provide Initial Liquidity

# Mint tokens to deployer
ERC20(tokenA).mint(deployer, 1_000_000)
ERC20(tokenB).mint(deployer, 1_000_000)

# Approve pool
ERC20(tokenA).approve(pool, 1_000_000)
ERC20(tokenB).approve(pool, 1_000_000)

# Add liquidity
pool.addLiquidity(1_000_000, 1_000_000)

3. Execute a Swap

# Approve swap amount
ERC20(tokenA).approve(pool, 10_000)

# Perform swap
pool.swapAForB(10_000, 9_800)  # minAmountOut = 9,800

4. Submit a Governance Proposal

# Propose new fee tier
gov.proposeUpdateFeeTiers(0.05, 0.3, 0.5)

# Vote
gov.vote(proposalId, true)  # 'true' for yes

# Execute after quorum
gov.executeProposal(proposalId)

Comparing Oracle‑Free vs. Oracle‑Based AMMs

Oracle‑free AMMs excel in environments where low gas costs, minimal centralization, and rapid on‑chain price discovery are paramount. However, for assets that do not trade extensively on-chain or that require cross‑chain price feeds, a hybrid approach may still be necessary.

Future Directions and Scaling Opportunities

1. Layer‑2 Integration
   Deploying the AMM on optimistic roll‑ups or zk‑Rollups can reduce gas costs further while maintaining oracle‑free mechanics.

2. Cross‑Chain Liquidity Pools
   Using bridge mechanisms, the pool can support assets from multiple blockchains without relying on external price oracles.

3. Adaptive Pool Configurations
   Machine‑learning models can predict optimal fee tiers and trade caps based on historical data, updating the pool parameters automatically.

4. Composable DeFi
   Exposing the AMM’s liquidity as a modular component allows other protocols (e.g., lending platforms) to tap into the pool without re‑implementing price discovery logic.

Key Takeaways

• An oracle‑free AMM can be built by relying solely on on‑chain trade data and carefully designed invariants.
• Dynamic fee structures, trade caps, and TWAP mechanisms are essential to protect against manipulation and ensure fair price discovery.
• Governance controls provide the community with flexibility to adjust parameters without hardcoding them into the contract.
• Security best practices—such as reentrancy protection, checks‑effects‑interactions, and front‑running mitigations—remain critical.
• While oracle‑free designs reduce centralization and gas costs, they are most effective for markets with sufficient on‑chain liquidity and volatility.

By adhering to these principles and following the architectural blueprint outlined above, developers can create robust, transparent AMMs that empower users to trade directly with a pool of liquidity, all without depending on external price oracles.