Volatility-Based Grid Market Making with Inventory Skew and Microprice
Summary
The document presents a simplified high-frequency grid market-making approach inspired by GLFT. Rather than dynamically estimating order-arrival intensity to set spreads and skew, it uses recent price volatility to determine quote distance. Inventory is normalized against a maximum notional position and shifts bid and ask depths in opposite directions. A microprice derived from best bid and ask prices and displayed quantities serves as the quote center. The strategy aligns prices to a minimum grid step, maintains multiple limit orders on each side, cancels orders outside the refreshed grid, and restricts new quoting as inventory approaches its bounds.
The accompanying examples run historical simulations on Binance and Bybit markets and show plotted outputs, but the document does not state numerical performance results in the provided text. It notes that large-tick assets may benefit particularly from microprice and that rebates and short-term mean reversion matter to the approach. Results depend on the assumed maker rebate, fees, latency, queue model, and backtest setup; the example is educational and does not establish live profitability. The simplified volatility method also bypasses explicit order-arrival modeling.
Key ideas
- The simplified strategy sets quote distance from recent volatility instead of a parametric order-arrival model.
- Inventory skew moves bid and ask quotes to manage directional exposure.
- A quantity-weighted microprice is used as the quote center, with potential value for large-tick pairs.
- Quotes are placed on a grid and refreshed as prices, volatility, and inventory change.
- Simulation assumptions such as rebates, fees, latency, and queue behavior affect how results should be interpreted.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.