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2233admin / machine-learning-for-trading
Does the health score find the bugs?
Repowise ranks every file by predicted defect risk. Of the 20 files it flags as riskiest, 2 (10%) actually needed a bug fix in the last 6 months — 7.1× better than picking files at random.
Languages
- python79.2%
- markdown11.5%
- yaml6.8%
- toml1.1%
- dockerfile1.1%
- shell0.3%
Explore 2233admin/machine-learning-for-trading
Key modules2
- dockerfile
benchmark
envs/benchmark
- dockerfile
py312
envs/py312
Code health
Full reportHow 2233admin/machine-learning-for-trading works
This page is an auto-generated, always-fresh map of the 2233admin/machine-learning-for-trading repository, written primarily in python. Repowise indexes the source, parses every symbol, computes a dependency graph, scores per-file code health from complexity, duplication, test coverage and churn, mines git history for hotspots and ownership, and lifts the resulting architectural decisions into a wiki you can read or query through MCP.
The codebase has 355 source files, 7,147 symbols, and 6 languages, structured as a monorepo with 2 packages. Git churn analysis flags 1 high-frequency files as hotspots — places where bugs, rewrites, and code review tend to concentrate. The dependency graph clusters into 99 tightly-coupled module communities.
Use the panels above to open the interactive dashboards, or connect this repo to your editor via the Repowise MCP server for grounded answers inside Claude, Cursor, or VS Code.