Performance analysis of the parallel code execution for an algorithmic trading system, generated from UML models by end users
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".