Scaling GLFT Grid Market Making Across Multiple Futures

Summary

This tutorial adapts a GLFT-based grid market-making backtest to multiple futures assets. It normalizes order size to a common notional amount, sets inventory limits in units of that order size, estimates trade-arrival intensity and price volatility from recent observations, and uses those estimates to set quote spread and inventory skew. The example refreshes quotes on a fixed interval and builds buy and sell grids around a reservation price. It then combines asset equity series to examine how portfolio statistics change as more markets are included.

The document points out practical limits: some assets trade mainly at the best quotes, which can leave too few observations for the intensity model; increasing the reaction interval may help but delays response. It also warns that some assets may violate the assumed Poisson arrival pattern, and its example filters assets using in-sample Sharpe statistics. Results depend on a stated futures market-maker rebate, and the tutorial notes an unresolved half-tick versus tick unit issue in the intensity output. The plotted backtest is educational and does not establish live performance.

Key ideas

• The example scales per-asset order quantity to a common notional value before combining backtests.
• A GLFT-style model estimates arrival intensity and volatility to adjust quote spreads and inventory skew.
• The strategy places layered limit orders and cancels orders that no longer fit the updated grid.
• Trade-arrival data may be inadequate for assets where executions occur mostly at the best bid or offer.
• Portfolio results depend on asset selection, a stated rebate assumption, and a noted tick-unit issue.

Tags

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