Pattern Effects Labs had built a prototype for predicting movements of stock indices using proprietary technology which had to be moved into production. The primary issues were around the efficiency of the algorithms which lead to scaling issues as well long testing and tuning cycles due to long turnaround times to check results from parameter modifications. Honey Badger Labs implemented vectorised versions of the algorithms using Python Pandas to cut down computation times for the core algorithm from 12 minutes to 40 milliseconds. The core components were separated out into smaller units communicating with each other over message queues. Custom visualisations were implemented for the user dashboard as well as for the validation interface to show index movement in real time along with predictions and recommendations. Technical analysis indications were also implemented using custom graphs in D3. The real time nature of the project required using a NodeJS to handle web socket channels to update user dashboards with low latency.