The question I get asked most, usually revolves around burgers, a variation of where’s my favourite burger place, or what cheese works best on a burger etc. I am always happy to answer these crucial questions!
Another question which I get asked often, is how do I begin to use data in my trading? The question is kind of easy, right? However, the answer is pretty involved, and it really depends on how your firm. What might be appropriate for a quant firm, isn’t necessarily right for a discretionary firm, and you must tread carefully. Furthermore, everyone’s investment mandate is slightly different. In The Book of Alternative Data, Alexander Denev and I discuss how you can create teams to use alternative data. Below, I talk about some of these points from the book and also expand upon them to answer the more general question of how you can transform your trading firm to use data better.
If you’d be interested in having a chat with me about this let me know too, and Cuemacro can put together a customised plan for your trading firm to move into the data age!
Make sure you have the data to start!
At the very least, you’ll need to have a way to store, clean and manage data. Without having easy access to data you can’t do any further analysis. What type of technologies you use is dependent on your requirements. Storing daily data is going to be very different to handling large amounts of tick data. Data can be used at many levels, whether it’s improving execution through TCA or for longer term trading idea generation. You’ll also need to consider where to host your platform (cloud or locally?)
You’ll need to look for which datasets you’ll need for analysis, and these days, that often includes alternative data. Cuemacro can help you understand what datasets you might be able to use and also what data infrastructure you’ll need.
Upskilling folks to deal with data
Ok, Excel is pretty good for many things. However, for doing serious data work, Python (or R) are better starting points. I’m not saying everyone needs to be a Python whizz in your trading firm, but it’s good to offer your staff the ability to upskill to Python. That way working with data can be an integral part of your firm, rather than simply a random bolt on. Cuemacro has several training courses that we can teach in house for Python for finance, as well as Python with alt data/NLP and large datasets (partially based on The Book of Alternative Data).
Using lots more data will also mean hiring
Whilst upskilling staff is important part of a more data driven approach, so is hiring staff or at least repurposing staff from elsewhere in your firm. Again precisely who you hire depends on what you want to do. If you want to use large amounts of unstructured data, you’ll probably need a data engineer who has experience in managing data lakes etc. Cuemacro can help you with interviews to help hire the right data folks and put together a strategy for hiring to plan how to do it.
You need to ask the right questions for your data science team
Any attempt to introduce data into your process shouldn’t see it massively siloed. Even if you have a separate data science team, it needs to be an integral part of the business, working with portfolio managers, traders and research, with communication going both ways. The business needs to ask the right questions for data scientists to answer. If data scientists are answering the wrong questions, then it’s impossible to monetise what they do. There have been too many occasions where massive data science teams have been created in the wrong way, only to see them fail (often after spending millions of dollars). It has to be done the right way!
Cuemacro can help you formulate the types of questions you might want to ask. They could be anything from “how can I use news to help me forecast volatility?” to ” can social media help me forecast an earnings number?” to creating fully systematic trading models – which question you want to ask depends on your firm. A private equity firm is going to ask very different questions to a systematic fund, when it comes to data. Cuemacro can also be used as an external resource to help answer these questions.
Augmenting your processes with data, not replacing everything
If you want to become more data driven, it’s also important to understand what you’ve done right already. Take for example using alternative data. The idea isn’t to throw away all your existing models, it’s about adding alternative data to see how to improve them.
Conclusion
It is possible to make your trading firm more data driven! However, doing it effectively, requires a lot of planning and many steps. We can’t simply hire a bunch of folks, and say “do something with data”, and throw money at it. We need to work out what data they need, how they will process it, what questions they will need to answer etc. Cuemacro can help you put together a plan to do this, and we can also work on data projects too.