Proactive Market Making (PMM)

Unlike traditional AMMs that rely solely on their internal reserves, PMM uses an external price oracle (e.g., Chainlink, Pyth) as the primary reference for the true market price. The protocol's internal pricing curve is then actively managed relative to this oracle price, allowing it to proactively adjust liquidity depth.

PMM does not spread liquidity uniformly across a price range. Instead, it uses a concentration factor to algorithmically concentrate the majority of its liquidity within a tight band around the oracle price. This creates deeper liquidity where most trades occur, dramatically improving capital efficiency compared to constant product AMMs.

By aligning liquidity provision with the oracle price, PMM reduces divergence loss for LPs. Since the pool's reserves are managed to track the external market, the arbitrage gap between the pool price and the oracle price is minimized, leading to lower IL for LPs during normal market conditions.

Protocols can tune PMM behavior via key parameters:

• K (Spread Factor): Controls the steepness of the pricing curve and the spread between bid/ask.
• Mid Price & Price Gap: Defines the target price (often the oracle price) and the allowed deviation.
• Amplification Factor: Adjusts how aggressively liquidity is concentrated. These allow customization for different asset volatility profiles.

Many PMM implementations support single-sided liquidity deposits. LPs can deposit only one asset (e.g., only USDC), and the protocol's algorithm manages the counterparty asset exposure. This simplifies the LP experience and reduces the barrier to providing liquidity.

• vs. CPMM (Uniswap v2): PMM is not bound by x * y = k. It uses oracle data to break the constant product constraint, enabling better prices near the market rate.
• vs. CLMM (Uniswap v3): Both concentrate liquidity, but CLMM requires LPs to manually set price ranges. PMM automates this concentration dynamically around an oracle, reducing active management overhead.

PMM minimizes impermanent loss by dynamically adjusting the liquidity curve to follow a reference price from an oracle. Instead of providing liquidity across an infinite price range, capital is concentrated around the current market price, reducing exposure to adverse price divergence. This is a key improvement over standard Constant Product Market Makers (CPMM) like Uniswap V2.

By concentrating liquidity near the mark price, PMM protocols offer significantly lower slippage for trades of equivalent size compared to classic AMMs. This creates a trading experience closer to that of an order book, improving capital efficiency and attracting larger volume. The depth of the liquidity pool is effectively amplified around the fair market price.

PMM achieves higher capital efficiency by ensuring that a larger portion of the pooled assets is actively used for trading at relevant prices. This means Liquidity Providers (LPs) can achieve similar fee income with less capital at risk, or higher fee income with the same capital, compared to passive, wide-range liquidity provision.

The system uses a price oracle (e.g., from a centralized exchange or a decentralized price feed) to proactively set its curve's "anchor price." This alignment helps prevent arbitrageurs from easily draining the pool during normal market conditions, protecting LP capital and ensuring the quoted price reflects the broader market.

Many PMM implementations incorporate dynamic fee mechanisms based on market volatility or distance from the oracle price. This allows the protocol to automatically increase fees during periods of high arbitrage opportunity or price divergence, generating additional revenue for LPs to compensate for increased risk.

DODO is a pioneering protocol that popularized the Proactive Market Maker model with its PMM algorithm. Other platforms like Curve Finance employ a related concept of StableSwap and Crypto Pools, which are specialized PMM implementations optimized for stablecoin and pegged asset pairs, achieving extremely low slippage.

PMM algorithms depend on an external price oracle to set the target price for their bonding curve. A manipulated or stale price feed can cause the AMM to trade at a significant deviation from the true market price, leading to arbitrage losses for liquidity providers and potential permanent loss of funds. This risk is heightened during periods of low liquidity or high volatility on the reference market.

The bonding curve logic and oracle integration are implemented in smart contract code. Vulnerabilities such as reentrancy, integer overflows, or flawed access control can be exploited to drain funds. Rigorous audits and formal verification are critical, as the complexity of PMM logic increases the attack surface compared to simpler Constant Product Market Makers (CPMM).

By concentrating liquidity around a target price, PMM amplifies impermanent loss (divergence loss) if the market price moves outside the intended range. While this design aims for higher fee income, a sustained price move can leave LPs with a portfolio heavily skewed toward the depreciating asset, realizing significant losses upon withdrawal.

PMM performance is highly sensitive to its configuration parameters, including the amplification coefficient (k) and the price range width. Incorrectly set parameters can lead to:

• Excessive slippage if the curve is too steep.
• Insufficient fee capture if the range is too wide.
• Rapid depletion of reserves if the curve is too aggressive, making the pool vulnerable to large trades.

PMM implementations often integrate with broader DeFi ecosystems, creating dependency risks. This includes reliance on specific oracle providers (e.g., Chainlink), lending protocols for leveraged positions, or governance tokens for parameter updates. Failure or exploitation in any dependent protocol can cascade to the PMM pool.

The predictable adjustment of the PMM curve based on oracle updates creates opportunities for MEV. Searchers can front-run large trades that are expected to move the oracle price, or perform latency arbitrage between the oracle update and the PMM's price adjustment, extracting value from regular users and LPs.

A foundational decentralized exchange (DEX) protocol that uses a deterministic mathematical formula (e.g., x*y=k) to price assets and provide liquidity. Unlike PMM, traditional AMMs are reactive, relying solely on the ratio of assets in its pools. PMM builds upon AMM concepts by incorporating external price oracles to adjust its pricing curve proactively.

A critical data feed that provides external, real-world information to a blockchain. In PMM, price oracles (like Chainlink or Pyth) are essential. They supply the reference market price against which the PMM algorithm compares its internal pool price, triggering proactive adjustments to the bonding curve to align with the broader market.

A mathematical model that defines the relationship between an asset's price and its supply within a liquidity pool. PMM does not use a static curve like a constant product AMM. Instead, it dynamically shifts and shapes its bonding curve around the oracle price to concentrate liquidity where trading is most likely to occur, reducing slippage.

The potential loss experienced by liquidity providers when the price of deposited assets changes compared to simply holding them. PMM aims to mitigate impermanent loss by keeping the pool's asset ratios closer to the market price via its proactive adjustments. However, IL risk is not eliminated and can be amplified if oracle prices diverge from true market execution.

A measure of how effectively deployed capital generates trading volume and fees. By concentrating liquidity around the market price, PMM achieves significantly higher capital efficiency than traditional, spread-out AMM curves. This allows it to offer competitive slippage with a smaller total value locked (TVL), attracting both LPs and traders.

A fee mechanism that adjusts based on market conditions, such as volatility or pool imbalance. Advanced PMM implementations may incorporate dynamic fee models, increasing fees during high volatility to compensate LPs for greater risk or during large imbalances to incentivize arbitrageurs to rebalance the pool.