Becoming a Quant

Throughout my hypothetical journey as a quant in a hedge fund, I’ve evolved my role from handling and managing data to analysing and interpreting it, then to creating features and finally developing full-scale trading strategies. This journey has spanned several years and allowed me to develop a deep understanding of financial markets, data science, statistical modelling, machine learning, and the business of hedge funds.

Data Management:

My journey started with managing data streams, which was about maintaining the integrity, accuracy, timeliness and relevance of the data flowing into the hedge fund. It was critical to set up a reliable data infrastructure that could handle a massive volume of real-time data. During this stage, I learned the importance of data in quantitative finance, which is often quoted as “Data is the new oil”. Clean, high-quality, and up-to-date data is a prerequisite for any kind of data analysis or predictive modelling. The learning here was how to manage and maintain massive data pipelines, understand different data types (structured, unstructured, time-series, etc.) and formats, and how to store and retrieve them efficiently.

Data Analysis:

Once the data management systems were in place, I moved on to data analysis. This stage was all about understanding the data, identifying patterns, making preliminary forecasts, and discovering insights. I learned to apply statistical methods, generate descriptive and inferential statistics, create data visualizations, and more. This stage taught me to ask the right questions and interpret the data's answers.

Feature Creation:

The next step was feature creation, which is an essential part of the predictive modelling process. This stage involved engineering new features from existing data to better capture the underlying patterns that could predict market movements. For instance, I may have created rolling window features (like moving averages), interaction features, and complex derived features. This stage taught me that creative and smart feature engineering often leads to better model performance. The key was to keep the business problem in mind and create features that made sense in the context of financial markets.

Trading Strategies:

The final stage was developing full-blown trading strategies. This required a synthesis of all the skills learned so far, along with a deep understanding of financial markets, risk management, and the business objectives of the hedge fund. Here, I would have leveraged advanced predictive models, like machine learning algorithms, to forecast market movements. I would have also implemented backtesting to assess the strategy’s performance historically. Additionally, I would have had to understand how to implement the strategy in the live market, which involves understanding trade execution, transaction costs, liquidity, slippage, and more.

The biggest takeaway from this stage was that developing a good trading strategy involves a good prediction model and an understanding of risk management and trading logistics. Having a holistic view of the process is important, from data management to prediction to execution.

Throughout the journey, I learned the importance of constant learning and staying up-to-date with the latest research in finance, machine learning, and data science. I also understood that while quantitative analysis is crucial, understanding the business context and having a broader perspective on market movements is equally important. It’s the combination of these skills that makes a successful quant in a hedge fund. Finally, finding mentors, listening and asking questions is very helpful.

--h, Henry Carstens , PM and Head of Research, Aargo Trade