Performance analysis of the parallel code execution for an algorithmic trading system, generated from UML models by end users
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we describe practical results of an algorithmic trading prototype and performance optimization related experiments for end-user code generation from customized UML models. Our prototype includes high-performance computing solutions for algorithmic trading systems. The performance prediction feature can help the traders to understand how powerful the machine they need when they have a very diverse portfolio or help hem to define the max size of their portfolio for a given machine. The traders can use our Watch Monitor for supervising the PNL (Profit and Loss) of the portfolio and other information so far. A portfolio management module could be added later for aggregating all strategies information together in order to maintain the risk level of the portfolio automatically. The prototype can be modified by end-users on the UML model level and then used with automatic Java code generation and execution within the Eclipse IDE. An advanced coding environment was developed for providing a visual and declarative approach to trading algorithms development. We learned exact and quantitative conditions under which the system can adapt to varying data and hardware parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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