The TRADE 60 | Page 26

[ T H O U G H T L E A D E R S H I P | L I Q U I D M E T R I X ] Machine learning engineering for TCA Chris Sparrow, head of research, and Melinda Bui, director of trading analytics, at LiquidMetrix explain how applying domain knowledge to its database of client orders can create features that help get the most out of the machine learning algos. W hen we analyse our trading performance using transaction cost analysis (TCA), we need to realise there are things that are not in our control, like the liquidity available in the market, and things that are in our control, like how we inter- act with the market - our trading strategy. The trading strategy describes the prices and volumes we obtained in the market and include the venue distribution and the time distribution of volume executed passively and actively, lit and dark, etc. The point of TCA is to uncover insights in our execution data. These insights can shed light on how to best model market impact of our orders along with many other use cases. Other use cases include comparing how different algos trade different securities, how stable the trading strategies are and many others. We can use machine learning (ML) algorithms to help us extract 26 // TheTrade // Summer 2019 these insights from our historical set of order data. In this article we explain a key part of the process of applying ML to TCA - applying our domain knowledge to our database of client orders to create features that help us get the most out of the ML algos. The trading strategy is often dic- tated by the type of algo employed, along with specific parameters that modify the default strategy. Depending on our reasons for trading and the liquidity charac- teristics of the stock, we may want to modify our trading strategy. Since we measure the perfor- mance of each order using various benchmarks, we can analyse how performance varies with trading strategy. When we talk about a trading strategy, we generally mean the volume profile of our order as a function of time. There are other components related to venue selection (eg: whether dark pools are accessed, etc…) which can also be measured to understand the way our order interacted with the market. We can then take these measurements and create ana- lytics, or features, to describe the trading strategy. We are particularly interested in the trading strategy because that is how we interacted with the market and so we expect that for our pri- mary use case, measuring market impact, the degree of impact will depend in part on our trading strategy. To put this another way, we want to be able to do scenario analysis using our pre-trade market impact model, and we want to see how