Transaction cost analysis is more than box ticking

I used to go to the gym quite often for the purposes of box ticking. A few minutes on the rowing machine, then maybe 5 minutes on Twitter. Then perhaps a few minutes browsing through the music stored on my iPhone. Perhaps, a bit more time on the running machine, before going back to walking around aimlessly at the gym and so on. It certainly managed to tick that “did I go the gym box?”. Did it help me improve my fitness? Well, probably not, and it’s likely I’d have been better off just going for a brisk walk.


I’ve now stopped going the gym for “box ticking”. Instead I often go to gym classes, which enforces discipline. When I go on my own, I have a routine, rather than randomly trying machines, which invariably ends up getting broken up by sitting around doing nothing deciding which machine to use. There’s still a long way to go in terms of getting fitter, but hopefully I’ll get there! Possibly, eating a mixture of burgers, gelato and chocolate might be somewhat of an impediment for me.


Box ticking is not just a feature of going to the gym, it’s also unfortunately often a feature of financial markets. Let’s take the idea of doing best execution, which is now part of MiFID II regulations. The buy side needs to show that it has tried to get the best price from liquidity providers. Transaction cost analysis can be used to accomplish this. Basically it involves analysing your trade data to work out your cost of trading, to see how your various liquidity providers are charging you. It is tempting to see this as a box ticking exercise, to do the least work possible in TCA, trying to use the simplest/cheapest TCA solutions you can. 


However, in practice, TCA shouldn’t be done just to satisfy regulatory reasons. It should be done in order to reduce your transaction costs and increase the alpha delivered to your clients. This involves spending more time and effort to do TCA. It involves creating your own customised process for TCA, so the metrics/workflow you use are unique to you. It involves assessing your liquidity not only for a specific liquidity provider or venue, but across them all. 


Cuemacro’s tcapy Python based library gives you the power to do TCA on FX spot on your own terms. Clients purchasing the enterprise licence, get access to 100% source code so they can add their own metrics and benchmarks. It 100% transparent for clients, given access to the source code. At the same time, you get the library maintained and supported externally, rather than having the costly burden of internal maintenance. Clients using tcapy are also free to choose whatever market benchmark they want to use (and switch between them) not simply to use the market benchmark imposed on them.


Furthermore, because it is run locally your trade data is kept private behind your own firewall. Your entire trade history is a valuable resource after all, and shouldn’t be given away for free. If you have a lot of flow and a large footprint in the market, it is advantageous to keep that data in house. It also adds a compliance burden if you choose to send your entire trade history outside your firm. You need to know precisely how that data is stored, used, processed by a vendor, and you have to sign off on this. If the data is all kept internally, you can avoid this whole question and instead concentrate on the trading!


Transaction cost analysis shouldn’t be a box ticking exercise. It should be seen as an opportunity to generate alpha and reduce your trading costs. A fully customisable solution like tcapy lets you do this. If your firm trades FX, and you’d like to know more about tcapy let me know!