Optimal Client Order Management for Internal Liquidity Pools

A model to optimally manage clients’ orders to internal liquidity pools has been presented by researchers from HSBC and Imperial College London, offering a structured approach for market makers navigating evolving foreign exchange trading dynamics.

The research, authored by Alexander Barzykin and Robert Boyce of HSBC and Eyal Neuman of Imperial College London, focuses on how market makers can balance risk management and pricing strategies when clients access internal liquidity through passive orders. These orders, increasingly offered by institutions, allow client algorithms to interact with liquidity pools using limit or pegged orders tied to an internally maintained fair reference price.

As outlined in the study published on April 17, 2026, market makers act as principals when handling these internal orders, choosing either to fill them as part of their risk management or to adjust pricing relative to their external over-the-counter franchise to improve matching efficiency. The model addresses the uncertainty around how internal liquidity strategies should respond to market conditions, risk appetite, and the specific algorithms used by participating clients.

The authors note that while internalisation has long reduced market impact in FX trading by enabling client-to-client matching through intermediaries, such opportunities remain scarce in over-the-counter markets. This has driven interest in Internalisation Type B, defined by the Foreign Exchange Professionals Association as the offsetting of commercial flow by a liquidity provider, which relies more heavily on interaction with OTC liquidity.

The research contributes to ongoing efforts to refine algorithmic execution in FX markets, where reducing visibility and minimizing transaction costs remain key objectives. By framing internal liquidity management as a multi-objective optimisation problem, the model provides qualitative insights for institutions seeking to align passive order handling with both their own risk profiles and client expectations around execution speed.