What “execution quality” means for a market maker
Execution quality describes how closely a trader’s orders are filled to the intended price and timing, after accounting for trading costs. For a market maker model, a common starting point is that the execution happens through a provider’s market-making process, which may quote prices and manage inventory risk. To assess execution quality, focus on measurable outcomes: the difference between the expected execution price (based on a reference) and the realized fill price, and the speed with which the order is filled.
A useful definition separates stable mechanics from variable conditions:
- Stable mechanics: how orders are handled (for example, whether fills reflect the provider’s available prices, and whether execution includes buffering or internal routing).
- Variable conditions: market volatility, liquidity, your order type, and transaction costs (spreads, commissions, fees).
Mechanism: what to measure and how to benchmark it
To evaluate execution quality for a market maker approach, build a simple scorecard from your own historical trade records. Even without live data, you can compare fills to a reference you can reconstruct from the moment you submitted or placed the order.
Consider these measurable factors:
- Slippage vs. a reference price: Choose a consistent reference for every test (for example, the last quoted price at order entry, or the mid-price at order entry). Slippage is the realized fill price minus the reference for buys (reverse the sign for sells). State your assumption explicitly: if you use “last quote,” your results reflect quote availability and not necessarily the best market-wide liquidity.
- Fill speed and partial fills: Measure time from order submission to first fill, and record how often orders are split into multiple fills. Fast, complete fills generally indicate better ability to execute at the intended level, but speed can also be linked to market conditions.
- Price improvement vs. the reference: For each trade, calculate whether the fill was better than the reference price. A consistent pattern of improvement can indicate effective execution, but it can also be driven by favorable short-term conditions.
Evidence example (with assumptions)
Assume you submit a buy order when the last available quote is 1.1000. Your fill is 1.0996. Using that reference, slippage is -0.0004 (a 4-point improvement relative to the quote for that trade). If you repeat this for many trades, you can compute average slippage and the distribution (for example, how often slippage is worse than a threshold). State the assumption: the quote you used is treated as the best available reference at that moment, even though it may not represent all venues.
Limitations and common failure modes
Several limitations can prevent you from attributing execution outcomes uniquely to the market maker model.
- Reference mismatch: If your benchmark is not a true “available at submission” market price, your slippage estimates mix execution quality with reference bias. Using mid-price versus last quote can materially change results.
- Hidden costs and spreads: Quoted spreads can differ from realized all-in costs once commissions and fees are included. If you only compare fill prices without cost accounting, you may misread execution quality.
- Regime dependence: In calm markets, many systems look similar; in fast markets, outcomes can diverge. Historical averages do not guarantee future behavior because volatility, liquidity, and order book dynamics change.
- Attribution ambiguity: Even with the same provider model, execution depends on your order type (market vs. limit), size, and timing. A poor outcome may reflect insufficient liquidity for your order size rather than the market maker’s execution process.
A material failure mode is treating one metric as sufficient. For example, focusing only on average slippage may hide that a provider fills quickly but with occasional large adverse fills, which can dominate risk.
Verification: a practical control point for independent checking
A strong way to verify claims about execution quality is to rely on internal, reproducible calculations from your own records and compare results under different, clearly documented conditions.
Use a control point approach:
- Keep your benchmarking method constant (same reference definition, same sign convention, same cost inclusion rule). - Compare across market regimes (for example, high vs. low volatility periods), and record whether the distribution changes. - Separate order types (market orders and limit orders behave differently).