Company

System: The Golang-based application comprises core application GUI, which may be hosted separately, and is designed to operate in the low latency environment of a local network. Arbitrary event-driven show logic is supported via an internal scripting system. Rapidly changing data is cached externally (with Memcached) to prevent loss of state in the event the core application goes down.

Client: Technical Productions

Technologies: Golang, Echo, Javascript, MySQL, Memcached

Problem: A centralised piece of software is needed to coordinate sound, lighting and other IoT devices in interactive show venues.

Solution: Devices,  device commands, network setup, show "actions" and responses to events can all be defined by show operators to create arbitrary show scenarios. The software interfaces with popular software favoured for sound and lighting, and includes a versatile distributed dashboard scheme which allows custom dashboards to be created that can be hosted across multiple machines. 

System: Assembler's Factory Floor Time-Tracking App

Client: Whippendell Marine

Technologies: Raku, CRO, Template::Nest, jQuery, CSS, MySQL

Problem: Create an application that interfaces with the company's existing Teamwork-based project management system and enables assemblers to log time spent on jobs - with design according to client spec.

Solution: A set of "Element" modules were created in Raku which inherited from a base module responsible for making calls to the Teamwork API. Syncing of local application data was accomplished with an independent daemon looping over and caching necessary user and project information in a MySQL database. Server-side rendering created dynamic content with Template::Nest, and the front end assembled HTML chunks delivered from the backend with jQuery. The system was designed to be deployed to Android-based tablet as a PWA app.

System: Stock price history pattern matching utility

Technologies: Python (asyncio matplotlib mplfinance pandas...)

Problem: Build a system which identifies stocks that are currently in the process of repeating previously observed price history patterns, based on low-high and high-low trend time intervals.

Solution: Our solution involves iterative analysis of a large large database of stocks, gathered from web sources. Price histories are divided into progressively smaller time period bins, with duration labelled the "granularity" of the iteration. The pattern of trends across bins is compared with recent price movements to identify matching segments. We then used a visualisation system based on Japanese "renko" diagrams to depict matches in an easy-to-digest format.

Note this is a bespoke system created upon request; we provided no advice on stock selection methods.