Please describe the key functional components of Citi Velocity Dynamic Algos.
The Citi Velocity Dynamic Algo platform is underpinned by our belief that the best way an algo execution strategy can achieve low market impact is through internalisation against liquidity sourced from Citi’s extensive FX franchise. The new platform will deliver increased opportunity for internalisation via three distinct methods: access to internal principal liquidity, algo to algo matching and a discretionary internalization model.
The new algos will be able to source both deliverable and NDF liquidity from a configurable set of liquidity pools: Citi only, a hybrid of Citi & external liquidity and external liquidity only. As part of the planned product evolution, we are adding additional liquidity sources such as Curex and dark pools.
The strategies incorporate a new layer of dynamic control allowing users, via engine or discretion modes, to adapt order placement as market conditions change. Users can also choose whether an order should aggress at the end of the period to fill the remaining balance, extend the duration, or simply expire in-line with the initial working period instruction.
We have also increased the number of benchmark algos available by introducing a new Implementation Shortfall algo named ‘Arrival’ which is benchmarked against arrival price and uses adaptive techniques to forecast market dynamics and manage the trade-off between market impact and price risk.
Additionally, we added a VWAP strategy for clients looking to execute in line with a historical volume curve. These two new algos compliment the TWAP and Volume Tracker (Historical/live POV) strategies to provide clients with the flexibility to tailor their execution style according to their objectives.
Our dynamically controlled execution logic, which is common across all our benchmark tracking strategies, continuously recomputes an optimal execution policy using a variety of quantitative methods. The core algorithmic logic calculates the marginal impact on expected slippage of every potential order placement and compares it to the increase or reduction in the risk of missing the target benchmark rate and profile. It manages the trade-off between reduced slippage and increased benchmark risk according to the client’s stated discretion level. In determining that optimal action, it considers prevailing market conditions by using metrics including future expected spreads, market impact, market volatility, potential passive fills, and historical market volume profiles. It produces a trading policy that is optimal in both timing and rung placement.
How much choice and flexibility do clients with different trading styles have when using Citi Velocity Dynamic Algos?
At a strategy level the Citi Velocity Dynamic Algo platform offers a wide range of execution styles from the passive Peg strategy, multiple benchmark referencing strategies through to the liquidity seeking Sweep / Post and Sweep strategies. Within the strategies themselves we have increased the flexibility and control a user has over an in-flight order. For the Arrival & Peg strategies, the engine mode allows users to dictate the level of prioritization the strategy gives to clearing risk quickly with less regard for market impact, versus a slower execution with higher probability of passive fills and lower market impact.
For the TWAP, VWAP & Volume Tracker strategies users will be able to select from three levels of discretion mode which will instruct the algo on how closely it should track the benchmark, with the low mode designed to track the benchmark very closely and high mode allowing the strategy increased discretion to react to favourable price conditions and maximize passive fill rates.
What range of strategies are now available with Citi Velocity Dynamic Algo’s?
We categorize the Citi Velocity Dynamic Algo’s into three groups, Benchmark, Limit and Float:
Dynamic TWAP – Our dynamic TWAP algo 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, allowing clients to pursue optimal execution with minimal market impact.
Dynamic VWAP – Our dynamic VWAP algo 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.
Arrival – our new implementation shortfall algo aims 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.
Volume Tracker – Our new Volume Tracker algo 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.
Sweep – Our Sweep algo strategy 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 – Our Post & Sweep algo strategy 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).
Peg – Our Peg algo strategy 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.
What functionality does your Algo Dashboard provide to help clients monitor the performance and progress of their algo orders?
Post-trade specific execution reports and live charts are available to enhance levels of transparency and visibility, alongside an automated data-feed to independent third post-trade analysis. We also provide clients with insight into their real-time execution footprint as they execute, where a client running their execution via Velocity can see their fills plot graphically in real time against a market neutral price.