Algorithmic Peg Controller

A supply adjustment mechanism where the quantity of tokens in every holder's wallet is programmatically increased or decreased to change the token's market price. For example, if the price is below the peg, the protocol burns tokens from all wallets, reducing supply to increase scarcity and price. This is a core feature of tokens like Ampleforth (AMPL).

A dual-token system that uses a governance token to absorb volatility and incentivize peg maintenance. When the stablecoin trades above its peg, new stablecoins are minted and sold for the governance token, distributing profits to governance holders. When below peg, the protocol mints and sells governance tokens to buy back and burn the stablecoin. This model was pioneered by Basis Cash and is used by Frax Finance.

A permissionless, on-chain module that autonomously executes monetary policy to manage the peg. An AMO can perform functions like:

• Minting stablecoins to provide liquidity in a DEX pool.
• Using protocol-owned liquidity to buy back and burn tokens.
• Earning yield on collateral to strengthen the protocol's balance sheet. Frax Finance extensively uses AMOs for its fractional-algorithmic stablecoin.

A smart contract that allows users to swap the algorithmic stablecoin for a hard-collateralized asset (like USDC) at a 1:1 rate, creating a direct arbitrage pathway to the peg. The PSM acts as a liquidity pool of last resort, backed by the protocol's reserves. This hybrid approach, used by MakerDAO's DAI and Frax Finance, significantly reduces peg volatility by providing a guaranteed redemption floor.

Algorithmic controllers rely entirely on decentralized oracle networks (like Chainlink) for accurate, real-time price data of their stablecoin. The oracle price is the critical input that triggers all supply adjustments, rebases, or AMO actions. Oracle manipulation or failure is a primary systemic risk, as incorrect price data can cause the protocol to execute harmful monetary policy, breaking the peg.

A critical vulnerability where price declines lead to negative feedback loops. If the token price falls below peg, the protocol may mint and sell more governance tokens to buy it back. This can dilute the governance token's value, eroding confidence and causing further stablecoin sell-offs. This reflexivity was a key failure mode in early designs like Basis Cash and Terra's UST, highlighting the need for robust collateral backstops.

A supply-elastic mechanism where the total token supply is algorithmically expanded or contracted across all wallets to adjust the market price. For example, if the price is below the peg, the protocol burns tokens from every holder's balance, increasing scarcity. If above, it mints new tokens, increasing supply. This changes the token quantity in each wallet but maintains the holder's percentage of the total supply.

A multi-token system that separates the stablecoin unit from a governance/volatile share token. When demand is high and the stablecoin trades above peg, new stablecoins are minted and sold for profit (seigniorage), which is used to buy back and burn the share token. When below peg, the protocol mints and sells new share tokens to buy back and burn the stablecoin, supporting its price.

A control loop feedback mechanism borrowed from engineering that uses three terms to calculate the required supply adjustment:

• Proportional (P): Reacts to the current size of the price error.
• Integral (I): Accounts for accumulated past errors.
• Derivative (D): Predicts future error based on its rate of change. This sophisticated model aims for smooth, dampened corrections to avoid over-shooting the target peg.

A system that creates a secondary market for absorbing excess supply. When the stablecoin is below its peg, users can purchase discounted bonds (IOUs) using the stablecoin. These bonds can be redeemed for the stablecoin at full value once the peg is restored, or for the protocol's share token. This creates a direct incentive for arbitrageurs to remove supply from circulation, increasing price pressure.

The controller relies on a decentralized price oracle (like Chainlink) to obtain the real-time market price of its token. This external data feed is the primary input for all algorithmic decisions. The oracle price triggers the smart contract logic for rebases, seigniorage distribution, or bond sales. Reliable, manipulation-resistant oracles are critical to prevent faulty peg corrections.

Algorithmic stablecoins face significant systemic risks:

• Death Spiral: A loss of confidence can lead to a downward spiral of selling, failed peg corrections, and hyperinflation of supply.
• Oracle Manipulation: Incorrect price data can trigger destructive contract actions.
• Reflexivity: The token's price and the value of its supporting share token are deeply intertwined, creating volatile feedback loops.
• Adoption Dependency: The system requires continuous growth and demand to maintain stability.

A debt-based, algorithmic stablecoin that maintained its peg to the US Dollar via an arbitrage mechanism with its sister token, LUNA.

• Minting/Burning: 1 UST could always be minted by burning $1 worth of LUNA, and vice versa. This created a direct arbitrage loop to correct peg deviations.
• Critical Failure: The mechanism relied on perpetual faith in LUNA's market cap. A loss of confidence triggered a death spiral where UST de-pegging led to hyperinflation of LUNA supply, collapsing both.

A collateral-backed system that incorporated an algorithmic peg stability module (PSM) for its DAI stablecoin.

• Mechanism: The PSM allowed 1:1, fee-based swaps between pre-approved, highly liquid collateral (like USDC) and DAI. This created a hard arbitrage ceiling very close to $1.
• Role: It acted as a safety valve, not the primary stability mechanism (which was overcollateralized debt positions). It algorithmically provided infinite liquidity at a specific price point to defend the peg.

Controllers rely on external price oracles (e.g., Chainlink, Uniswap TWAP) to determine the target asset's market price. An attacker who can manipulate this feed can trick the controller into issuing incorrect monetary policy, such as minting or burning tokens at the wrong time. This is a primary attack vector for de-pegging.

• Example: An attacker uses a flash loan to skew a DEX pool's price, causing the oracle to report a false value, triggering a flawed contraction or expansion cycle.

Many controllers have upgradeable proxy contracts or parameters controlled by a decentralized governance token. This creates critical risks:

• Malicious Upgrades: A compromised governance process (e.g., via a whale attack or voter apathy) can approve a malicious contract upgrade.
• Admin Key Risk: If the system retains privileged functions (e.g., an emergency pause), the security of the private keys controlling them becomes a single point of failure.

The stability mechanism itself can become unstable under extreme market stress, leading to a death spiral or hyperinflation. This is a systemic risk inherent to the design.

• Death Spiral: If the peg breaks below target, contraction (burning) reduces supply but may not restore confidence, causing further sell pressure.
• Ponzi Dynamics: If expansion (minting) rewards are the primary incentive for early holders, the system may rely on continuous new capital inflow to maintain the peg.

The controller's logic is encoded in smart contracts on-chain. Bugs in this code are immutable and can be exploited.

• Reentrancy Attacks: Improper state handling during mint/burn functions.
• Logic Errors: Flaws in the bonding curve calculation, rebase mechanism, or fee distribution.
• Integration Risks: Vulnerabilities in integrated liquidity pools or staking contracts can compromise the entire system. Rigorous audits and formal verification are essential but not guarantees.

A stable peg requires deep, resilient liquidity. Controllers are vulnerable to liquidity attacks.

• Liquidity Drain: A sudden, large withdrawal from the primary liquidity pool can widen spreads and break the peg.
• Front-Running: Bots can anticipate and profit from public rebase or expansion transactions, extracting value from regular users.
• Concentrated Liquidity Risks: If liquidity is concentrated in a few pools or on a single DEX, it becomes a target for manipulation.

Algorithmic stablecoins often operate in a regulatory gray area. Key considerations include:

• Security Classification: Regulators (e.g., SEC) may deem the governance token or the stablecoin itself a security.
• Banking Regulations: Mimicking monetary policy may attract scrutiny from financial authorities.
• Enforcement Actions: Sudden regulatory action against a project can trigger a loss of confidence and a bank run on the protocol.