Online Archive of University of Virginia Scholarship
RuleEngine AutoPilotRule Matching Speedup: Tree-Based Rule Indexing for Low-Latency Order Routing; Dark Systems, Tech-Metal Commodity Chains, and the Recursive Abstraction of Price in Algorithmic Finance13 views
Author
Vaddi, Suraj, School of Engineering and Applied Science, University of Virginia
Advisors
Carrigan, Coleen, EN-Engineering and Society, University of Virginia
Vrugtman, Rosanne, EN-Comp Science Dept, University of Virginia
Morrison, Briana, EN-Comp Science Dept, University of Virginia
Abstract
This thesis examines algorithmic finance as both a technical engineering problem and a sociotechnical system with hidden material dependencies. The technical project addresses latency in electronic order routing by replacing brute-force AutoPilotRule scanning with a tree-based indexing structure that precomputes rule paths, expands aliases, handles wildcard conditions, and performs targeted traversal at runtime. Tested against historical order windows, the system reduced matching latency substantially in some production-adjacent settings, showing how algorithmic restructuring can improve the speed and scalability of financial infrastructure. The STS research paper situates this kind of optimization within a broader system of algorithmic finance, commodity extraction, and value abstraction. Focusing on copper, lithium, and cobalt, it argues that the metals enabling computational infrastructure are also financialized commodities whose extraction costs are often obscured by market prices, supply-chain distance, and corporate accounting structures. Using Minati’s theory of dark systems, the thesis connects low-latency financial engineering to the material and ethical conditions that support it. Together, the technical and STS components show that faster financial systems are not purely digital or immaterial. They depend on physical infrastructure, mined resources, labor, energy, and political-economic arrangements that must be made visible when evaluating the social responsibility of financial technology.
Degree
BS (Bachelor of Science)
Keywords
algorithmic finance; low-latency order routing; dark systems; high-frequency trading; tech-metal commodity chains
Notes
School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Suraj Vaddi
STS Advisor: Coleen Carrigan
Technical Team Members: Rosanne Vrugtman, Briana Morrison
Vaddi, Suraj. RuleEngine AutoPilotRule Matching Speedup: Tree-Based Rule Indexing for Low-Latency Order Routing; Dark Systems, Tech-Metal Commodity Chains, and the Recursive Abstraction of Price in Algorithmic Finance. University of Virginia, School of Engineering and Applied Science, BS (Bachelor of Science), 2026-05-08, https://doi.org/10.18130/sc44-d583.
