All my open source projects are available on my Github Account.
Autodesk Connector for ArcGIS
- Role: Contractor - Programmer
- Client: Autodesk & Brockwell IT Consulting
- Product: https://www.autodesk.com/campaigns/bim-gis
- Date: November 2017 - Ongoing (As of December, 2018)
As part of a new partnership between ESRI and Autodesk the Autodesk Connector for ArcGIS was created. The connector is designed for seamless integration between the Autodesk suite of products and ArcGIS Online data.
The initial target product for integration was Autodesk Infraworks. Since Infraworks is a Qt-based product the library was built using Qt to allow for a more seamless integration. There is ongoing work to integrate the connector into other Autodesk products such as Map3D and Civil3D. The C++ library manages authentication, data and the embedded web view for displaying the UI.
Initially I was the sole developer on this product for a 6-8 month period. However, as the product reached production more developers from the Autodesk side started to contribute. The product was slowly transitioned to Autodesk for ongoing maintenance and product integration.
- Role: Consultant - Programmer
- Client: NatAlliance Securities
- Technologies: Python, Django, Excel, Bloomberg, Bloomberg TOMS
- Date: September 2017 - March 2019
NatAlliance is a mid-sized regional broker-dealer based out of Austin, Texas with a strong focus in fixed-income trading and sales.
Bloomberg Trade Order Management System (TOMS) is an integral part of NatAlliance's front-office. The majority of fixed-income trading/sales staff use the system to book their trades. As a result NatAlliance had developed a back-office system to process the data from TOMS and generate monthly P&L and commissions reports.
Unfortunately the system was not well maintained and contained several defects and issues. The back-office staff had to perform several manual steps every month to work around the problems: manual P&I updates, manual override of unsupported/bugged ticket types and incorrect output. I was contracted to develop a new system that would improve the output accuracy, eliminate the current inefficiencies and automate the workflow.
The core of my solution was a new algorithm that could calculate P&L and commissions from raw TOMS data without requiring a secondary manual input of extra data (e.g. coupon payments, mortgage payments, calls, etc.). This greatly reduced the complexity of the process and simiplified the back-office work flow. Another strong feature of the algorithm is that it can "show its work" as it tracks all the matched tickets for every realized P&L event. This allows for transparent, detailed P&L reports where the final calculation is readily auditable.
The algorithm is driven by a django application for multi-tenancy and easier deployment. It supports basic features such as parallel report generation, user management, data feeds and report/upload history. The entire system requires no intervention except for a regular upload process to update the internal dataset. As a result, the previously manual monthly process for building P&L reports is now completely automated