Fill Rate

The most critical factor. Fill rate is directly proportional to the available liquidity at or near the requested price. Deeper order books with more limit orders provide more volume to fill against. Low-liquidity pools or assets often result in partial fills or high slippage.

• Example: An order for 100 ETH will fill completely against a DEX pool with 500 ETH in reserve, but may only partially fill against a pool with 50 ETH.

A user-defined parameter that sets the maximum acceptable price movement for an order. A tight slippage tolerance (e.g., 0.1%) restricts the price range the router can explore for liquidity, often reducing fill rate. A wider tolerance allows the router to tap into more liquidity pools across a broader price range, increasing the chance of a full fill.

• Trade-off: Higher potential fill rate vs. accepting a potentially worse execution price.

The router's ability to split an order across multiple liquidity sources (e.g., different DEXs, AMM pools, private market makers) is key. Sophisticated routing algorithms perform path finding to aggregate fragmented liquidity.

• Direct vs. Split Routing: A single-chain swap might route through Uniswap, Curve, and Balancer pools to achieve a better fill than using one pool alone.

Larger order sizes relative to available liquidity reduce fill rate. This is due to the price impact of moving through an order book or AMM curve. Algorithms may break large orders into smaller batches over time (Time-Weighted Average Price strategies) to improve fill rates and minimize market impact.

• Rule of Thumb: Order size should be a small percentage of the pool's total value locked (TVL) for optimal fill.

Blockchain network conditions affect execution. High gas fees and network congestion can cause transaction delays, allowing prices to move before settlement. In a fast-moving market, this can cause orders to revert or fill at a worse rate. MEV searchers may also front-run or sandwich large orders, negatively impacting the fill price for the user.

Highly volatile assets experience rapid price changes, making it harder to fill an order at a stable price point. During periods of extreme volatility, liquidity often dries up (as market makers widen spreads), and fill rates plummet. Stablecoin pairs or assets with high liquidity provider incentives typically exhibit more stable fill rates.

Fill Rate is the percentage of a user's requested trade size that is successfully executed by a protocol or exchange. It is calculated as (Filled Amount / Requested Amount) * 100. A 100% fill rate means the entire order was executed, while a partial fill results in a lower percentage. This metric is a direct indicator of liquidity depth and execution quality.

High fill rates are essential for user experience and capital efficiency in decentralized exchanges (DEXs) and aggregators. They minimize slippage and ensure users get the expected output for their trades. Protocols compete on fill rate as it signals:

• Superior liquidity sourcing from multiple pools.
• Efficient routing algorithms that split orders.
• Effective use of on-chain liquidity and off-chain sources like RFQ systems.

Several on-chain and protocol-specific factors determine the achievable fill rate:

• Liquidity Depth: The total value available in a pool at a given price point.
• Transaction Ordering: Competition with other transactions (MEV) can cause front-running or failed fills.
• Slippage Tolerance: User-set limits that cancel the trade if the price moves beyond a threshold.
• Protocol Design: Aggregators that split orders across multiple DEXs (e.g., 1inch, 0x) typically achieve higher fill rates than single-DEX swaps.

These are distinct but related metrics. Fill Rate measures the portion of an order filled. Success Rate measures the frequency with which transactions are included on-chain without reverting. A transaction can have a 100% success rate (it didn't revert) but a 0% fill rate if the market moved and no liquidity was available at the user's specified price.

A user wants to swap 100 ETH for DAI on a DEX aggregator. The aggregator's routing engine finds:

• 60 ETH of liquidity in Uniswap v3 at the best price.
• 40 ETH of liquidity in a Curve pool. It executes two swaps, filling the entire 100 ETH order. Fill Rate = 100%. If it could only source 75 ETH across all venues, the fill rate would be 75%, and the remaining 25 ETH would be returned to the user.

Fill Rate quantifies execution efficiency by measuring the proportion of an order that is matched with liquidity. It is calculated as:

Fill Rate = (Filled Amount / Order Amount) * 100%

A 100% fill rate indicates the entire order was executed, while a lower rate shows partial fills, which can lead to slippage and higher effective costs as the remaining order may be filled at worse prices.

Low fill rates directly correlate with high slippage. When an order is partially filled, the remaining portion must be routed elsewhere, often at less favorable prices. This is a key economic consideration for traders.

• Example: A $100k market buy with an 80% fill rate means $80k executes at the target price, but the final $20k may incur significant price impact, raising the overall average execution price.

Fill rate is a direct function of liquidity depth at a specific price point. Deeper liquidity pools and concentrated liquidity positions enable higher fill rates for large orders.

Protocols like Uniswap V3 with concentrated liquidity allow liquidity providers (LPs) to target specific price ranges, creating denser liquidity and improving fill rates for trades within those ranges compared to constant product AMMs.

Low fill rates expose users to Maximal Extractable Value (MEV) risks. Bots can exploit partially filled orders through tactics like sandwich attacks.

• Process: A bot detects a large, partially filled order in the mempool.
• Action: It front-runs the remaining portion, buying the asset to drive up the price.
• Result: The user's remaining fill executes at this inflated price, and the bot profits by selling back immediately. High fill rates mitigate this attack surface.

Advanced DEX aggregators and protocols implement Smart Order Routing (SOR) to maximize fill rates and minimize slippage. They split a single order across multiple liquidity sources (e.g., different AMM pools, RFQ systems) to achieve the best overall execution.

Key mechanisms include:

• Path Splitting: Dividing an order across multiple liquidity pools.
• RFQ Integration: Filling portions via professional market makers.
• Gas Optimization: Balancing fill rate against transaction cost.

Fill rate data influences liquidity provider (LP) behavior and protocol economics. High fill rates in a specific pool indicate efficient capital utilization, attracting more LPs and fees.

• Concentrated Liquidity: LPs earn fees only when the price is within their set range, incentivizing them to place liquidity where fill demand is highest.
• Fee Tiers: Protocols may adjust fee tiers based on historical fill rate performance to balance LP returns with trader costs.