Signal Structure
Detected analysis stack (Python, Docker, GitHub Actions) shows where signals can be translated into traceable outputs.
Analysis
Short-term traders need fast, repeatable analysis for identifying scalping opportunities without relying on instinct alone.
Analysis delivery story for ken4ward/smt-scalping.
Framework capabilities
Detected analysis stack (Python, Docker, GitHub Actions) shows where signals can be translated into traceable outputs.
Repository evidence supports reviewability of assumptions, source transformations, and decision checkpoints.
File and manifest signals indicate structure suitable for repeatable analysis or execution pipelines.
Short-term traders need fast, repeatable analysis for identifying scalping opportunities without relying on instinct alone.
It frames the work as an analysis system, using Python, Docker, GitHub Actions, Python pytest to organize data, signals, calculations, or research workflows. The detected manifests show concrete implementation structure such as .github/workflows/scalping.yml, .github/workflows/smt.yml.
It frames the work as an analysis system, using Python, Docker, GitHub Actions, Python pytest to organize data, signals, calculations, or research workflows. The detected manifests show concrete implementation structure such as .github/workflows/scalping.yml, .github/workflows/smt.yml.
Shows ability to structure data-heavy thinking into repeatable tools, experiments, and decision support.
SMT Scalping Analysis shows implementation through Python, Docker, GitHub Actions, Python pytest, supporting a clearer reviewable scope.
Workflow or manifest signals are present (2 manifest signals), which supports repeatable build and validation behavior.
SMT Scalping Analysis includes structured signals for execution and workflow visibility, useful for portfolio-level quality discussion.
2 implementation signals were detected, with 1 top-level folder driving structure.
.github/workflows/scalping.yml.github/workflows/smt.ymlNext step
Use this repository story as a starting point for a scoped software, QA, data, or AI delivery conversation.