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Five Pillars of Modern Electronic Trading
Traders Magazine Online News, April 5, 2019
The electronic trading business is highly competitive. In my view, to build a competitive low-touch offering, one needs to, at least, tackle the following five key areas.
TECHNOLOGY
Over the last 20 years, the electronic trading technology landscape has experienced dramatic changes. Innovations in hardware, networking, and software have had an immense impact on the current state of the art. To truly stand out from the rest of the competition, an electronic trading product must perform from a latency and throughput point of view and be resilient to failure without compromising capabilities and quality of execution.
There is a prevalence of cutting-edge open-source libraries for almost everything that needs to be built. A tremendous amount of attention should be paid to reliability and failover. A comprehensive suite of automated testing and a sophisticated simulation environment is essential to validate and guarantee the quality of a product.
Buy-side firms clearly demand better performance and predictability of execution results, without sacrificing the ability to source liquidity, transparency and control of their executions. Connecting to a wide range of liquidity sources, selecting venues to route algorithmic child orders based on advanced analytics, regularly reviewing venue selection, and providing transparency of order routing logic are all integral parts of best execution and drivers of quality execution performance.
QUANTITATIVE RESEARCH
Advanced quantitative models are the cornerstone of a competitive electronic trading offering. It is the “brain” that drives the algorithmic behavior. Taking into account both historical and real-time market data, a limit order placement model determines when, where and how much quantity to place at any point in time throughout the order lifecycle, a venue-ranking model makes informed stock-specific routing decisions, and a volume profile model dynamically tilts towards either a front-loaded or back-loaded distribution of volume.
One should choose the most suitable models for every situation. We treat our order placement problem as a Markov Decision Process solved by dynamic programming technique. This provides a mathematical framework that can naturally integrate various sub-models to address different aspects of security trading, in particular, market microstructure models, such as spread dynamics, fill probability and adverse selection, all modeled with thorough statistical analysis. Short-term signals can also be easily incorporated into the framework.
At every state, an optimal decision among crossing the spread, improving the best quote, joining the best quote, placing deeper in the order book, and staying away from the market will be selected in order to minimize trading cost.
The use of market microstructure models and dynamic programming technique makes a number of well-known optimal decision concepts become the natural outcome of an order placement model in a quantitative manner:
1. Opportunistically cross the spread when the spread is tight or the market is moving in the same direction as
the algo trading activity.
2. Remove the order from the market if there is significant adverse selection or the market is moving in the opposite direction as the algo trading activity.
3. Step into the spread to improve fill probability while still capturing the spread.
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