It's about time

Currently financial institutions face a changing environment when it comes to quantitative methods.

• Classical valuation libraries are often “grown up” beasts and it is observed that they are not flexible nor scalable and adapting it is cumbersome, eg.
  • Pricing and managing hybrids and exposure
  • Implementing new programming paradigms and techniques (eg. AD, ML applications)
• We observe that classical quantitative methods need to be combined with statistical learning techniques which put pressure on restructuring units (combine quant with statistical learning/data science, learning new technique, using different tools)
• There is no open source solution that provides this unique combination, eg. the classic QuantLib library is based on technology of the late 90s and needs to be tweaked for any new issues. It does not provide any statistical learning methods at all. It is over-engineered!
• There is no mature solution in the area of statistical learning that can be directly applied to solve quantitative finance problems.
• Scripting should be considered on an efficiently large scale (not only Payoff descriptions!)

• QuantLab – ML can change that by creating the tool!

It's about time

Currently financial institutions face a changing environment when it comes to quantitative methods.

• Classical valuation libraries are often “grown up” beasts and it is observed that they are not flexible nor scalable and adapting it is cumbersome, eg.
  • Pricing and managing hybrids and exposure
  • Implementing new programming paradigms and techniques (eg. AD, ML applications)
• We observe that classical quantitative methods need to be combined with statistical learning techniques which put pressure on restructuring units (combine quant with statistical learning/data science, learning new technique, using different tools)
• There is no open source solution that provides this unique combination, eg. the classic QuantLib library is based on technology of the late 90s and needs to be tweaked for any new issues. It does not provide any statistical learning methods at all. It is over-engineered!
• There is no mature solution in the area of statistical learning that can be directly applied to solve quantitative finance problems.
• Scripting should be considered on an efficiently large scale (not only Payoff descriptions!)

• QuantLab – ML can change that by creating the tool!

It’s about time

Currently financial institutions face a changing environment when it comes to quantitative methods.

• Classical valuation libraries are often “grown up” beasts and it is observed that they are not flexible nor scalable and adapting it is cumbersome, eg.
  • Pricing and managing hybrids and exposure
  • Implementing new programming paradigms and techniques (eg. AD, ML applications)
• We observe that classical quantitative methods need to be combined with statistical learning techniques which put pressure on restructuring units (combine quant with statistical learning/data science, learning new technique, using different tools)
• There is no open source solution that provides this unique combination, eg. the classic QuantLib library is based on technology of the late 90s and needs to be tweaked for any new issues. It does not provide any statistical learning methods at all. It is over-engineered!
• There is no mature solution in the area of statistical learning that can be directly applied to solve quantitative finance problems.
• Scripting should be considered on an efficiently large scale (not only Payoff descriptions!)

QuantLab – ML can change that by creating the tool!