Algorithmic trading is an important step for FX but how dependent is the performance of these toolsets on the liquidity they interact with?
Liquidity is frequently a key factor in algo performance. Depending on the algo strategy, liquidity may supersede execution logic, as the most important determinant of execution performance, as who you’re matching with may significantly impact the outcome of the order. Clean liquidity with depth is something very few firms are able to offer.
Why is it important that buy-side firms have a greater understanding about the underlying liquidity that’s involved in algo FX trading and what steps can leading practitioners take to bring more transparency to the process?
It’s important to understand the underlying liquidity, as it may differ significantly depending on the provider you face. Buy-side firms need to know whether their order information is transmitted to the market, whether intentionally or not. Information leakage leads to market impact and that is what our clients want to avoid.
Leading providers need to provide clients with transparency into the composition of their liquidity pools. At BofA, we provide full details of the channel, order and client types an algo faces. We believe it’s essential that clients understand the liquidity their orders are interacting with.
In what ways can more effective liquidity management help to enhance the abilities of FX algos?
Proactive liquidity management often leads to better fills, which often results in better price / execution. However, sole focus on the tightest price in the liquidity pool misses the point. Clients benefit from a wider view of liquidity. Quantifying factors, such as fill probability and market impact, help determine the overall attractiveness of liquidity. BofA uses this approach seek better outcomes for our clients.
How much of a challenge is it for banks and clients to effectively manage and benchmark the various liquidity pools?
It is a constant challenge to manage and benchmark various liquidity pools. It takes a substantial investment in personnel and technology to create a purely data-driven approach, however, we view this as a BofA strength.
How can algo FX providers ensure the quality of the liquidity they provide to clients and how much of a challenge is keeping liquidity pools clean?
It is an ongoing process, as both market conditions and trading behaviors change over time. In order to successfully do this, it is critical to use an adaptive data-driven approach.
Optimal liquidity may differ depending on the client. This makes sharing the same philosophical approach of liquidity management with clients important. We believe minimizing information leakage is a top priority and BofA can be incredibly selective, due to the size of our franchise.
How is quantitative research helping providers to make more effective use of liquidity for algo FX trading?
Quantitative research is central to everything we do. Our decisions are data driven. The depth of our client base and our market reach provide us with scale. We leverage this scale to provide highly attractive liquidity to our algo FX trading clients.
What are the typical pros and cons of internal versus external liquidity for FX algo trading?
If you are working with a trusted counterparty, internal liquidity provides anonymity and certainty around who you’re facing. As a result the requirement to trade on secondary markets is reduced and thus results in lower market impact overall.
In what ways can leading FX providers leverage their liquidity as a key differentiator in this space?
FX providers can showcase their franchise on pass-through internal fills. BofA runs a pass-through liquidity model which allows algo execution to benefit from our client flow, including all “clicking” channels. These fills are passive and not market impactful. Clients know that large non-directional flows only come from a few providers. BofA, with its large payment business and global client franchise, is one of them.
What can we expect to see with next generation FX algos that will make them smarter in accessing hidden liquidity?
There has been tremendous innovation in this space. We are starting to see a proliferation of venues with a slightly different flavor of hidden liquidity. Although it’s positive for the market place, it makes the role of Smart Order Routers more complicated.
Order Routers will have to be more dynamic to maximize liquidity capture across both hidden and lit venues. Constant recalibration, using real-time data and signals, will govern where and why clients post larger order sizes. We expect to see a new generation of liquidity seeking algos that are richer in terms of the signals they consume.