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Recent Projects
Distributed web automation system for public sector ecommerce
System: Distributed web-automation application simplifying tasks on a public sector ecommerce portal
Client: MePATool
Technologies: Perl, Javascript, Mojolicious, Hypnotoad, SQL
Problem: The workflow required for public sector ecommerce in a particular jurisdiction can slow down business.
Solution: 7 subsystems have been created and are maintained by Virtual Blue, including client executables, API, Web Admin and web-automation processes. Multithreaded headless Chrome instances are controlled via a custom process management system. Rendering browsers tend to be memory hungry which is one reason the system is distributed across several servers, in order to cope with traffic spikes. Recently very low error rates have been achieved.
Assembler's Factory Floor Time-Tracking App
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.
Stock Price History Pattern Matching Utility
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.
Management
Tom Gracey
Director and CTO
Physics BSc. 1st Class Hons; systems, algorithms and web languages polyglot
Amman Qadir
Business Director
Computer Information Systems BSc.; analysis, big picture ideas and Software Development Lifecycle barista