  Minimizing execution costs for large orders is a fundamental challenge in
finance. Firms often depend on brokers to manage their trades due to limited
internal resources for optimizing trading strategies. This paper presents a
methodology for evaluating the effectiveness of broker execution algorithms
using trading data. We focus on two primary cost components: a linear cost that
quantifies short-term execution quality and a quadratic cost associated with
the price impact of trades. Using a model with transient price impact, we
derive analytical formulas for estimating these costs. Furthermore, we enhance
estimation accuracy by introducing novel methods such as weighting price
changes based on their expected impact content. Our results demonstrate
substantial improvements in estimating both linear and impact costs, providing
a robust and efficient framework for selecting the most cost-effective brokers.
