Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-17 18:28 UTC

ml4t / specs

Serializable, runtime-neutral contracts for market data, artifacts, strategy lifecycles, and execution across ML4T libraries.

PythonMIT★ 3 stars⑂ 8 forkssince Apr 2026View on GitHub ↗

ml4t/specs holds a health index of 84 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (92/100) and lowest on Community & Adoption (49/100). It was last updated today. A single contributor accounts for most of its recent work.

84
overall / 100
Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

84
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 71 is calibrated to 84 on the published index scale (record calibration 2026-08-02).

Ownership

ML for TradingOrganization
156 followers12 public repossince Mar 2017

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

76Good · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
6.9/36Commit cadence — 10/52 weeks with commits
14/18Commit volume — 35 commits in the last year
10/10OpenSSF Scorecard: Maintained — 29 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year35
human_commit_share0.806
days_since_last_push0
active_weeks_last_year10
How it's scored
27/27Ships releases — 8 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~28.4 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count8
latest_release_tagv0.2.0
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases28.4

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

49Weak · 17% of overall
How it's scored
4.9/60Stars — 3 stars
7/25Forks — 8 forks
0/15Watchers — 0 watchers
Inputs used
forks8
stars3
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateyes
How it's scored
56.1/80Monthly downloads — 16,124 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesml4t-specs
dependents—
ecosystemspypi
total_downloads—
monthly_downloads16,124
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

62Moderate · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.8/22.5Commit distribution — top contributor authored 97% of commits
2.7/13.5Contributor breadth — 2 contributors
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.966
How it's scored
42/42Issue resolution — 100% of issues closed
26.8/30PR acceptance — 25/28 decided PRs merged
13/13Newcomer PR acceptance — 1/1 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — Found 1/23 approved changesets -- score normalized to 0
Inputs used
merged_prs25
open_issues0
closed_issues8
prs_merged_7d4
prs_decided_7d4
prs_merged_30d10
prs_decided_30d10
issue_closed_ratio1
closed_unmerged_prs3
first_time_authors_30d1
first_time_prs_merged_30d1
first_time_prs_decided_30d1
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
15.8/25Owner reach — 156 followers of ml4t
20.1/25Track record — 12 public repos, account ~9 yr old
Inputs used
followers156
owner_typeOrganization
is_verifiedno
owner_loginml4t
public_repos12
account_age_days3,515

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 8 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesml4t-specs
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall
How it's scored
24/24CI workflows — 4 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 24 out of 24 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://www.ml4trading.io/docs/specs/
10/10Repository description
10/10Topics — 12 topics
0/10Wiki
Inputs used
topicsalgorithmic-trading, backtesting, data-contracts, finance, live-trading, machine-learning-for-trading, market-data, ml4t, python, quantitative-finance, schemas, serialization
has_wikino
homepagehttps://www.ml4trading.io/docs/specs/
docs_sitehttps://www.ml4trading.io/docs/specs/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

76Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2.5/2.5CI-Tests — 24 out of 24 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 1/23 approved changesets -- score normalized to 0
0.8/2.5Contributors — project has 1 contributing companies or organizations -- score normalized to 3
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 29 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
5/5Pinned-Dependencies — all dependencies are pinned
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
7.5/7.5Token-Permissions — GitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate7
Excluded from scoring (no data or not applicable): Branch-Protection. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages85
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 85 resolved dependencies against OSV. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

82Excellent · 4% of overall
How it's scored
45/45Agent instructions — AGENTS.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 29 of 29 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes2,207
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — src/ml4t/specs/py.typed
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 2 of the last 36 commits agent-authored or agent-credited
8/8Automated maintenance — 7 of the last 36 commits are automated dependency updates
10/10OpenSSF Scorecard: Pinned-Dependencies — all dependencies are pinned
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configssrc/ml4t/specs/py.typed
agent_commit_share0.056
toolchain_manifests—
dependency_bot_commit_share0.194
How it's scored
27/45Type-checkable code — Python with type-check config (src/ml4t/specs/py.typed)
49.8/55Manageable file sizes — 2/21 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes78,202
source_files_sampled21
oversized_source_files2

Key facts

3GitHub stars
2contributors
35commits, last 12 months
0days since last push
8releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • deps.dev does not index pypi:ml4t-specs@0.2.0; advisories assessed against the repository dependency graph instead

More detail

Star and fork history 0 ★ / 8 ⇿
0Stars
8Forks
6Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

02468812026-052026-072026-10
Major 0Minor 1Patch 5
OpenSSF Scorecard 7.0 / 10
7.0aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-10-17 18:27 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
10CI-Tests24 out of 24 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 1/23 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained29 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
10Pinned-Dependenciesall dependencies are pinned
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 1
RegistryPackageVersion constraintManifest
PyPIpyyaml>=6.0pyproject.toml
All dependencies 85

Full resolved dependency set from the GitHub dependency graph: 1 direct and 84 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIpyyaml6.0.3direct
PyPIattrs26.1.0indirect
PyPIbabel2.18.0indirect
PyPIbackrefs8.0indirect
PyPIboolean-py5.0indirect
PyPIcachecontrol0.14.4indirect
PyPIcertifi2026.7.22indirect
PyPIcffi2.1.0indirect
PyPIcharset-normalizer3.4.9indirect
PyPIclick8.4.2indirect
PyPIcolorama0.4.6indirect
PyPIcoverage7.15.2indirect
PyPIcryptography50.0.0indirect
PyPIcyclonedx-python-lib11.11.0indirect
PyPIdefusedxml0.7.1indirect
PyPIdocutils0.23indirect
PyPIfilelock3.32.2indirect
PyPIghp-import2.1.0indirect
PyPIgriffelib2.1.0indirect
PyPIhypothesis6.165.2indirect
PyPIid1.6.1indirect
PyPIidna3.18indirect
PyPIiniconfig2.3.0indirect
PyPIjaraco-classes3.4.0indirect
PyPIjaraco-context6.1.2indirect
PyPIjaraco-functools4.6.0indirect
PyPIjeepney0.9.0indirect
PyPIjinja23.1.6indirect
PyPIjsonschema4.26.0indirect
PyPIjsonschema-specifications2025.9.1indirect
PyPIkeyring25.7.0indirect
PyPIlicense-expression30.4.4indirect
PyPImarkdown3.10.3indirect
PyPImarkdown-it-py4.2.0indirect
PyPImarkupsafe3.0.3indirect
PyPImdurl0.1.2indirect
PyPImergedeep1.3.4indirect
PyPImkdocs1.6.1indirect
PyPImkdocs-autorefs1.4.4indirect
PyPImkdocs-get-deps0.2.2indirect
PyPImkdocs-material9.7.7indirect
PyPImkdocs-material-extensions1.3.1indirect
PyPImkdocstrings0.30.1indirect
PyPImkdocstrings-python2.0.5indirect
PyPImore-itertools11.1.0indirect
PyPImsgpack1.2.1indirect
PyPInh30.3.6indirect
PyPIpackageurl-python0.17.6indirect
PyPIpackaging26.2indirect
PyPIpaginate0.5.7indirect
PyPIpathspec1.1.1indirect
PyPIpip26.2indirect
PyPIpip-api0.0.34indirect
PyPIpip-audit2.10.1indirect
PyPIpip-requirements-parser32.0.1indirect
PyPIplatformdirs4.11.0indirect
PyPIpluggy1.6.0indirect
PyPIpy-serializable2.1.0indirect
PyPIpycparser3.0indirect
PyPIpygments2.20.0indirect
PyPIpymdown-extensions11.0.1indirect
PyPIpyparsing3.3.2indirect
PyPIpytest9.1.1indirect
PyPIpytest-cov7.1.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpywin32-ctypes0.2.3indirect
PyPIpyyaml-env-tag1.1indirect
PyPIreadme-renderer45.0indirect
PyPIreferencing0.37.0indirect
PyPIrequests2.34.2indirect
PyPIrequests-toolbelt1.0.0indirect
PyPIrfc39862.0.0indirect
PyPIrich15.0.0indirect
PyPIrpds-py2026.6.3indirect
PyPIruff0.15.8indirect
PyPIsecretstorage3.5.0indirect
PyPIsix1.17.0indirect
PyPIsortedcontainers2.4.0indirect
PyPItomli2.4.1indirect
PyPItomli-w1.2.0indirect
PyPItwine7.0.0indirect
PyPIty0.0.27indirect
PyPItyping-extensions4.16.0indirect
PyPIurllib32.8.0indirect
PyPIwatchdog6.0.0indirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 85 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.