Having looked at resolvable problems in the financial market, the focus was then on the machine learning toolkit and finding the right tool for the problem. The favoured school of thought here was to keep this simple, opting for the simplest tool first to see if this works before moving onto a more complicated one. When it came to the more complicated tools, the most widely used were Libraries and Python however neither of these tools appeared to offer much innovation. Ultimately, the innovation was in how the tool was being used rather than what tool was being used. This led to more cutting edge discussion, offering practical studies on the use of quantum computing and AI in the energy trading market.