Having built data sciences solutions for financial institutions, traders and capital markets clients, one of the challenges we have experienced during the past 12 months is extracting the tacit knowledge that quant teams possess and applying it from the technology side.
In a number of conversations in clients, where we have discussed data sciences or allied areas such as machine learning, we have seen that technology implementation is not what clients are really concerned about. What really matters to them is the modus operandi to integrate technology with the models that quant teams are building.
Who are quants and what do they do?
You find quants in investment banking, trading, risk management, and many other industries today. Thanks to the rise of “Big Data”, quants have suddenly become popular. You can see them working in structured credit or quantitative analysis but right now it is about the models.
Let’s take the “Trading industry” as an example.
Quantitative traders take a trading technique and create a model of it using mathematics, and then they develop a computer program that applies the model to historical market data. The model is then backtested and optimized. If favorable results are achieved, the system is then implemented in real-time markets with real capital.
In essence, Quantitative traders take advantage of modern technology, mathematics and the availability of comprehensive databases for making rational trading decisions.
For instance, one of our clients has their own quant team who work with Trading Team to enhance trading strategies and devises new strategies. The Tech Team at the client end designs and builds the technical infrastructure and software systems that make trading possible. There is a high degree of collaboration between the Trading, Tech, and Quant Teams.
How to bridge the gap?
In a Data Sciences assignment, how do we bridge this gap between quant and tech teams?
You will hear clients saying that it is impossible to take time out of quant teams to help tech teams given other priorities and prefer quant teams getting trained on technology, which is not easy.
On the other hand, you will see developers getting challenged with lateral learning requirements on areas such as Statistics and other math fundamentals. Without a good integration between these teams, how do we build solutions that do justice to the purpose for which they were built?
In our exploration and reading, we have seen Institutional Quant Platforms (IQP) as a solution to this problem. To quote Mark Higgins, Co-Founder and COO at Beacon, “IQP’s are designed to make it easy for quants to write, share and release code; access the data they need; and build and run the tools that feed the business.”
At Zuci, we built our Institutional Quant Platform.
What’s your strategy?
Sources: Zuci Clients
Zuci is revolutionizing the way software platforms are engineered with the help of patented AI and deep learning models. Learn more about Zuci at www.zucisystems.com
About the authors
The Data Science team at Zuci is a power-packed set of data geeks committed to innovation. They are relentlessly working towards providing unique solutions to client problems while being invested in exploring data stuff that is buzzing around. You can simply drop a mail to firstname.lastname@example.org to catch them in action.