Modeling Crypto Trade Volume and Price Impact for Limit Placement

Summary

This study uses one day of aggregated HOOK/USDT trade data to examine buy volume over different time intervals, trade frequency, and price impact. It argues that interval-level volume distributions can be approximated by scaling a model of single-trade sizes, with a correction that may become unnecessary as intervals combine more trades. It also compares observed volume distributions with fitted curves and notes that estimated execution probabilities are not the same as actual fill probabilities at a given order-book depth, because the book changes while orders wait.

The analysis measures price changes within individual trade timestamps and fixed intervals, then relates those changes to traded volume. Based on these estimates, it sketches an expected-return model for a limit order and identifies a candidate placement around 2.5 times the average volume. That estimate relies on strong assumptions, including price reversion after impact, stable volume and arrival patterns, and simplified trade sequencing. The author calls the model preliminary and notes that it lacks depth data; its findings are specific to one trading pair and sample day.

Key ideas

• The article models interval-level cumulative volume by adapting a distribution fitted to individual trade sizes.
• Estimated volume exceedance probabilities do not directly equal limit-order fill probabilities because order-book conditions change over time.
• The sample shows that measured price impact tends to rise with trade volume, though the relationship is approximate.
• A preliminary limit-placement model combines estimated execution likelihood with expected price impact.
• The candidate placement depends on simplifying assumptions and a single day of data from one crypto pair.

Tags

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