What impact can the time of day have on FX liquidity and how much relevance does this have for algorithmic trading?
Time of day can have a significant impact of the performance of execution algorithms, the FX market is OTC and for the majority of algorithmically tradable currency pairs the market is open 24/5 so naturally liquidity varies through the day, these variations are normally the result of local markets opening or closing and spikes in liquidity around certain events such as popular fixing times, option expiry times or data releases. As you would expect in times of low liquidity market spreads tend to be wider and the probability of passive fills is reduced, increasing market impact and execution costs. As the universe of available Algo currencies increases, for example adoption of NDF algos. It’s becoming increasingly important for algo users to have access to robust liquidity tools which can help assess the optimal time to execute to meet their performance objectives.
What sort of work are the Quant and the algo execution teams of leading providers doing to better understand the FX liquidity landscape and its impact on algorithmic FX trading?
Market impact analysis should be a core component of any FX execution algorithm. Smart execution algorithms should start with an idea of how different execution speeds, choice of liquidity pools and timing around relevant volume events alter the overall cost of execution through market impact and other factors, then adapt accordingly. Quant teams should be doing work to create, benchmark and improve such techniques, and the algorithmic execution logic that uses them during an execution. Market impact and optimal execution models are abundant in market microstructure literature, but almost all of them make a strong assumption that there is an accurate measure of total traded market volumes available as an input. In the OTC world of FX this is a more challenging problem, there is no such definitive record of trading volume available in real time, the liquidity landscape is increasingly fragmented and contains varying types of liquidity, so having a bespoke volume estimation model is extremely important.
Why is their access to liquidity a key factor to consider when choosing a suitable provider of FX algo execution services to work with?
The single greatest factor in determining overall market impact is the quantity internalised by the executing bank, either by matching against opposing client interest, or by warehousing risk for longer than the execution time. This means that banks with larger algorithmic execution businesses and principal trading businesses are in the best position to provide low market impact executions. Users of execution algorithms should pay careful attention to this, as fills against internal liquidity may very quickly be hedged against the same external liquidity pools available to the client algo and cause market impact, even though in terms of categorisation they are now marked as ‘internal’, not all fills against internal liquidity are created equal!
Range of strategies now available with Citi Velocity Dynamic Algos
These are categorized into three groups, Benchmark, Limit and Float:
BENCHMARK
Arrival – An implementation shortfall algo which aims to minimize slippage to the arrival price benchmark while managing the trade-off between market impact and price risk in real time.
This strategy combines the power of advanced statistical techniques with microstructure theory to forecast market dynamics.
Dynamic TWAP – A dynamic TWAP algo which aims to minimize slippage to the time-weighted average price benchmark over a set time interval. This allows clients to manage the trade-off between tracking the execution schedule and expected slippage while continuously tailoring order placement to market conditions. Continuous time approach allows for sophisticated benchmark tracking, with optional discretion around this to benefit from favourable price conditions.
Dynamic VWAP – A dynamic VWAP algo which is engineered to intelligently track the historically observed volume distribution over the specified order duration. This allows clients to manage the trade-off between tracking the execution schedule and expected slippage, optimizing execution within client parameters while dynamically adapting to live market conditions.
Volume Tracker – A Volume Tracker algo which targets a configurable level of market volume participation, dynamically scaling passive and aggressive order placement based on real-time volumes and the target participation level. Market participation can be amended in-flight, allowing clients to adjust their market participation mid-execution
LIMIT
Sweep – A Sweep algo strategy which takes liquidity at or better than the limit price, without showing visible resting interest externally using a proprietary cost model. This allows client orders to sweep across the selected liquidity pools, posting into the primary market at the limit price to work the balance via further aggressive orders as liquidity becomes available within the price limit.
Post & Sweep – A Post & Sweep algo strategy which employs a proprietary cost model to optimize liquidity targeting across multiple venues at or better than the limit price, without showing visible resting interest externally. This allows client orders to sweep across the selected liquidity pools, posting into the primary market at the limit price to work the balance, taking liquidity that becomes available within the limit price (and discretion).
FLOAT
Peg – A Peg algo strategy which captures liquidity passively in line with market prices, posting resting interest at or near the top of book. Engine Mode gives user control over how urgently the strategy should treat the execution and adjusts its posting behavior accordingly.
