Estimating Trade-Size Distributions for Maker Strategy Execution

Summary

This article introduces the modeling questions behind high-frequency maker strategies, focusing on where to place limit orders and how to control inventory. It argues that maker returns in oscillating markets may combine spread capture and exchange rebates, while trend moves can create losses. The analysis uses exchange aggregated-trade data, separates buyer- and seller-initiated activity, and regroups observations by timestamp to estimate trade-size behavior.

Trade sizes exhibit a long tail in the examined sample. The author compares empirical exceedance probabilities with a power-law approximation and finds that the initial fit deviates materially for small sizes. A modified formula is reported to reduce maximum deviation in the plotted comparison, though its derivation is omitted. These probabilities are conditional estimates that can help approximate execution likelihood at different order depths under idealized conditions. They do not account for queue position, additions, cancellations, or other order-book effects, and the article is an initial installment rather than a complete placement or inventory-control model.

Key ideas

• Maker strategies must balance order proximity, execution likelihood, fill profitability, and inventory exposure.
• The article analyzes timestamp-grouped trade quantities to estimate how often trades exceed a chosen size.
• The observed trade-size distribution has a long tail, and a basic power-law fit misses some empirical probabilities.
• A modified distribution formula improves the plotted fit, but its derivation is not fully documented.
• Estimated depth probabilities are conditional and omit queueing, cancellations, and order additions.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.