As traders, our dream is to buy securities at the lowest possible price and sell them at the absolute highest. While that may sound simple enough, the reality is that our job is far more complicated than that. So, when it comes to investment managers and data scientists who need to measure trading efficiency, the answer is not always clear-cut.
Even so, checking whether you are making efficient trades doesn’t have to be an arduous manual task, as there is software that can help you automate the process. Before we get into that, however, let’s look at how measuring trading efficiency works.
Every investment manager has their own rules to identify when to enter and exit a trade, with timing being a crucial factor in whether this turns out to be a successful one or not.
Intuitively, we can all agree that a truly successful trade is one which is bought at the bottom price and sold at the top, making it 100% efficient. On the other hand, an unsuccessful trade would be one that is bought at the top and sold at the bottom price, making it a whopping -100% efficient.
Of course, efficiency doesn’t always need to be at 100% and, in realistic terms, most investment managers will practically never buy securities at the relative bottom price and sell them at the relative top price. Instead, through your experience, you can predict the direction the market is moving in before deciding on whether a security is worth letting go, investing in, or just held.
Most of the time, your efficiency is going to fluctuate between -100% and 100% – and we’re here to explain how to measure it efficiently and effectively.
This should come as no surprise, but numbers are your friends here. You’ll need to work with mathematical formulas to measure the efficiency in all your trades. While doing this, keep in mind that market volume affects trading prices for large trades so, in this case, a formula adjustment could be needed to manage these effects.
To calculate efficiency, you’ll need to conduct an in-depth analysis of market movements and related volume. This requires you to work with proper data sources but, in this case, underestimating the trades size when compared to the market traded volume means underestimating slippage effects in the metrics. In addition, intraday data allows you to apply the correct calculations and avoid incorrect assumptions on daily data.
Improving your total trading efficiency always starts with an analysis of all the related components. In other words, you need to identify which type of investment signals and instruments are affecting the different levels of your efficiency.
Different levels of volatility may produce different behaviours in your strategy, as well as determine your related entry and exit prices. Properly managing the speed of the market helps you keep trading costs down and can be crucial to understanding your efficiency and related levels.
Finally, remember that all this analysis is there to help you improve your future trading efficiency. So, work with your team on understanding your newly-collected data and figure out a game plan to move forward.
In order to measure trading efficiency and better manage the volatility of the market, you’ll need to identify your metrics methodology and apply it. Our advice to investment managers and data scientists out there is to then use this data to help you understand what can be improved but, also, to simplify your life by allowing automation to run this whole process for you. This will save you time, help you stay ahead of the game, and improve your trading analysis overall.
So, what are you waiting for? Get in touch with us to find out how we could help you.