What Is a Quantitative Developer?
A quantitative developer, or quant dev, is an individual working at a private fund (often a hedge fund), an investment bank or other asset manager, employed to implement quantitative/financial models or quantitative trading infrastructure.
The term is extremely diverse and covers a wide spectrum of responsibilities.
At one end quant of the quant dev spectrum, developers will be tasked with taking a mathematical model already designed by a quantitative researcher and implementing, maintaining and optimising the model in production code.
At the other end, quant developers will be obtaining financial pricing data feeds, cleaning the data, preparing its storage and allowing access in a straightforward manner to other individuals who make require it.
A quantitative developer who implements financial models will need to be mathematically astute, and possess experience in scientific programming, usually in an object-oriented language such as C++, MATLAB, Python or R.
Their day to day responsibilities will revolve around the firms' quantitative library - maintenance, improvement, optimisation or adding new models.
Not only that, but the traders will want these models/prices exposed in other formats such as Excel.
It is the responsibility of the developer to construct these interfaces.
Other quant developers are more data-oriented.
They will be tasked with building systems to obtain various forms of financial data, such as pricing feeds (equities, forex, fixed income etc), macroeconomic data and internal/external trading signals.
They will then have the responsibility of storing this data, either in a RDBMS (relational database management system) such as SQL Server, Oracle or MySQL, or into a proprietary time-series database such as KDB+.
This data will need to be accessed in a timely fashion by multiple stakeholders.
It is also the quant dev's responsibility to ensure this happens in a reliable fashion.
So how should a prospective quant developer prepare for their career? The best approach is to begin working on large data sets requiring rapid data analysis.
This could be for a piece of hobby code, an open source project or a smaller consulting gig.
This will flex a prospective quant dev's statistical, computational and software developmental skills in a production environment and provide experience in handling financial data sets.
One of the best approaches is to begin implementing a few derivatives pricing or quantitative trading strategies in multiple languages, to get a feel for the advantages and disadvantages.
Quantitative developers are always in strong demand, as financial modelling and implementation requires a sophisticated computational skillset.
Expertise in algorithm implementation, database optimisation, API design, systems administration and specific languages such as C++, Java, MATLAB, R and Python will always be useful.
Needless to say, a career in quantitative development can be very lucrative and rewarding for those that enjoy utilising their computational skills.
The term is extremely diverse and covers a wide spectrum of responsibilities.
At one end quant of the quant dev spectrum, developers will be tasked with taking a mathematical model already designed by a quantitative researcher and implementing, maintaining and optimising the model in production code.
At the other end, quant developers will be obtaining financial pricing data feeds, cleaning the data, preparing its storage and allowing access in a straightforward manner to other individuals who make require it.
A quantitative developer who implements financial models will need to be mathematically astute, and possess experience in scientific programming, usually in an object-oriented language such as C++, MATLAB, Python or R.
Their day to day responsibilities will revolve around the firms' quantitative library - maintenance, improvement, optimisation or adding new models.
Not only that, but the traders will want these models/prices exposed in other formats such as Excel.
It is the responsibility of the developer to construct these interfaces.
Other quant developers are more data-oriented.
They will be tasked with building systems to obtain various forms of financial data, such as pricing feeds (equities, forex, fixed income etc), macroeconomic data and internal/external trading signals.
They will then have the responsibility of storing this data, either in a RDBMS (relational database management system) such as SQL Server, Oracle or MySQL, or into a proprietary time-series database such as KDB+.
This data will need to be accessed in a timely fashion by multiple stakeholders.
It is also the quant dev's responsibility to ensure this happens in a reliable fashion.
So how should a prospective quant developer prepare for their career? The best approach is to begin working on large data sets requiring rapid data analysis.
This could be for a piece of hobby code, an open source project or a smaller consulting gig.
This will flex a prospective quant dev's statistical, computational and software developmental skills in a production environment and provide experience in handling financial data sets.
One of the best approaches is to begin implementing a few derivatives pricing or quantitative trading strategies in multiple languages, to get a feel for the advantages and disadvantages.
Quantitative developers are always in strong demand, as financial modelling and implementation requires a sophisticated computational skillset.
Expertise in algorithm implementation, database optimisation, API design, systems administration and specific languages such as C++, Java, MATLAB, R and Python will always be useful.
Needless to say, a career in quantitative development can be very lucrative and rewarding for those that enjoy utilising their computational skills.
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