Generating a Real-Time Algorithmic Trading System Prototype from Customized UML Models (a case study)
Why this work is in the frame
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Bibliographic record
Abstract
Real-time algorithmic trading systems are widely used by pension funds, mutual funds, some hedge funds, market makers and other institutional traders, to manage market impact and risk, to provide liquidity to the market. The technologies of real-time information processing and high-performance computing, such as the parallel bridging model - SGL, are essential for such systems. However, many errors can be made with todays tools, for example, the distraction of developers because they must focus both on financial algorithms, parallel computing and coding, or compiler mis-optimization, etc. In this paper, we describe practical results with the software design of a real-time algorithmic trading prototype by undergraduate students within the CoSc 319 software engineering project course at the University of British Columbia's Okanagan campus (Canada) in collaboration with a PhD student from the University Paris-Est (France). The prototype can be modifi ed by end-users on the UML model level and then used with automatic Java code generation and execution within the Eclipse IDE. During the case study an advanced coding environment was developed for providing a visual and declarative approach to trading algorithms development so as to generate directly portable bitcode on Low-Level Virtual Machine (LLVM) from nancial speci cation of trading strategies. During the project, Canadian students collaborated with a research engineer from a hedge fund in Paris.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it