John Ashley

Last fall at STAC, John argued that the future belongs to the trader who leverages the Python ecosystem, including ML/AI tools, to create smarter strategies faster. Part of this was using NLP to provide better contextual intelligence to trading and investing.

William Gibson famously said that "the future is already here--it's just not evenly distributed". In John's view, that maxim applies to trading desks as much as any other part of life.

The number of architectures capable of supporting compute-intensive analytics in finance has never been greater. We can choose from an ever-expanding range of CPUs, GPUs, FPGAs, and NPUs, which can in turn exchange data via a wide variety of interconnects.

Financial firms are increasingly using machine learning or deep learning (broadly, “AI”) to improve outcomes or automate functions. Like many other key components of a business plan, AI is a team sport.

Financial firms are increasingly using machine learning or deep learning (broadly, “AI”) to improve outcomes or automate functions. Like many other key components of a business plan, AI is a team sport.

Financial firms are increasingly using machine learning or deep learning (broadly, “AI”) to improve outcomes or automate functions. Like many other key components of a business plan, AI is a team sport.