Crypto Maker High-Frequency Trading and Trade-Size Modeling

Summary

This article introduces a crypto maker strategy that posts buy and sell orders during choppy markets. It identifies potential returns from capturing price movement and maker fee rebates, then describes two design challenges: choosing order distances that balance fill probability against profit per fill, and limiting accumulated inventory. It recommends modeling fill behavior and building a fast backtest system to reduce experimentation costs.

Using exchange aggregated trade data, the article groups trades by timestamp and examines the distribution of aggressive trade sizes. It compares observed tail probabilities with a Pareto-style model and proposes a modified formula that fits the displayed sample more closely. The article presents plots as evidence, including a reported maximum deviation below two percent for the revised fit. This is a sample-specific, conditional estimate: it does not account fully for order additions, cancellations, or queue priority, and it does not establish profitability or forecast market direction.

Key ideas

• Maker strategies may earn from price oscillations and maker fee rebates.
• Order distance trades off fill likelihood against potential profit per fill.
• Inventory limits can reduce the risk of accumulating excessive exposure.
• The example models aggressive trade sizes with a modified Pareto-style distribution.
• Estimated size probabilities are conditional and omit important order-book dynamics.

Tags

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