Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-16 03:01 UTC

quantmind / quantflow

Quantitative finance and derivative pricing

PythonBSD-3-Clause★ 56 stars⑂ 13 forkssince Jan 2014View on GitHub ↗
KindNetwork servicehow this is determined
#finance#fourier-transform#option-pricing#stochastic-processes#cox-ingersoll-ross#heston-model#stochastic-volatility-models#quantitative-finance#ai-tools#mcp

quantmind/quantflow holds a health index of 78 out of 100, placing it in the Good band. It scores highest on Engineering Quality (85/100) and lowest on Security (46/100). It was last updated 60 days ago. A single contributor accounts for most of its recent work.

78
overall / 100
Good

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.

78
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 67 is calibrated to 78 on the published index scale (record calibration 2026-08-02).

Ownership

QuantmindOrganization · verified domain
120 followers48 public repossince Oct 2009

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIquantflow1.2.01,0783072 days agofinanceoptionspricingquantitativestochasticvolatility

Metrics by category

Vitality

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

74Good · 21% of overall

Development activity

56Moderate
How it's scored
18/36Push recency — last push 60 days ago
12.5/36Commit cadence — 18/52 weeks with commits
18/18Commit volume — 111 commits in the last year
7/10OpenSSF Scorecard: Maintained — 9 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 7
Inputs used
commits_last_year111
human_commit_share1
days_since_last_push60
active_weeks_last_year18

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 24 releases published
36/36Release recency — latest release 72 days ago
27/27Release cadence — a release every ~17.4 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count24
latest_release_tagv1.2.0
releases_from_tagsno
days_since_latest_release72
mean_days_between_releases17.4
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

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

57Moderate · 17% of overall

Popularity & adoption

37Weak
How it's scored
28.2/60Stars — 56 stars
9/25Forks — 13 forks
0/15Watchers — 0 watchers
Inputs used
forks13
stars56
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (BSD-3-Clause)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges4
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesbadge.fury.io, codecov.io, github.com, shields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

50Moderate
How it's scored
40.4/80Monthly downloads — 1,078 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesquantflow
dependents—
ecosystemspypi
total_downloads—
monthly_downloads1,078
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?

67Good · 23% of overall

Maintainer resilience (bus factor)

13Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0/22.5Commit distribution — top contributor authored 100% of commits
1.4/13.5Contributor breadth — 1 contributors
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled1
top_contributor_share1

Issue & PR responsiveness

82Excellent
How it's scored
42/42Issue resolution — 100% of issues closed
29.7/30PR acceptance — 92/93 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/29 approved changesets -- score normalized to 0
Inputs used
merged_prs92
open_issues0
closed_issues1
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio1
closed_unmerged_prs1
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

89Excellent
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
15/25Owner reach — 120 followers of quantmind
24.3/25Track record — 48 public repos, account ~16 yr old
Inputs used
followers120
owner_typeOrganization
is_verifiedyes
owner_loginquantmind
public_repos48
account_age_days6,200

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 72 days ago
20/20Version history — 30 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesquantflow
ecosystemspypi
any_deprecatedno
min_days_since_publish72

Engineering Quality

Are baseline engineering and documentation practices in place?

85Excellent · 19% of overall

Engineering practices

82Excellent
How it's scored
24/24CI workflows — 6 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
0/9.6Pre-commit hooks
0/6.4.editorconfig
18/20OpenSSF Scorecard: CI-Tests — 25 out of 26 merged PRs checked by a CI test -- score normalized to 9
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://quantflow.quantmind.com
10/10Repository description
10/10Topics — 10 topics
0/10Wiki
Inputs used
topicsfinance, fourier-transform, option-pricing, stochastic-processes, cox-ingersoll-ross, heston-model, stochastic-volatility-models, quantitative-finance, ai-tools, mcp
has_wikino
homepagehttps://quantflow.quantmind.com
docs_sitehttps://quantflow.quantmind.com
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

46Weak · 16% of overall

Security posture

33At Risk
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2.2/2.5CI-Tests — 25 out of 26 merged PRs checked by a CI test -- score normalized to 9
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/29 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
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
5.2/7.5Maintained — 9 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 7
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 38 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate3.3
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages15
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:quantflow@1.2.0 runtime dependency closure — what installing the published package pulls in — 15 packages. 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.

79Good · 4% of overall

Agent context & guidance

92Excellent
How it's scored
45/45Agent instructions — .github/copilot-instructions.md, .github/instructions/bibliography.instructions.md, .github/instructions/makefile.instructions.md, .github/instructions/observable.instructions.md, .github/instructions/release.instructions.md, .github/instructions/tutorial.instructions.md, CLAUDE.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.pydantic.dev/llms.txt)
31.5/40Legible commit history — 59 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.pydantic.dev/llms.txt
legible_history_share0.59
agent_instruction_files.github/copilot-instructions.md, .github/instructions/bibliography.instructions.md, .github/instructions/makefile.instructions.md, .github/instructions/observable.instructions.md, .github/instructions/release.instructions.md, .github/instructions/tutorial.instructions.md, CLAUDE.md
agent_instruction_max_bytes5,469

Verify loop (build / test / typecheck)

82Excellent
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
11/11Static type checking — quantflow/py.typed
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 5 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilespackage-lock.json, uv.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configsquantflow/py.typed
agent_commit_share0.05
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (quantflow/py.typed)
54.7/55Manageable file sizes — 1/165 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes64,739
source_files_sampled165
oversized_source_files1

Machine-readable interfaces

40Weak
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — examples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_files—
interfaces_expected_ofnetwork-service

Key facts

56GitHub stars
1contributors
111commits, last 12 months
60days since last push
24releases
1bus factor
0open issues
npm, PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 13 ⇿
0Stars
13Forks
12Releases

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.

03581013151322025-072026-012026-08
Major 1Minor 6Patch 5

Each point covers 2 days.

OpenSSF Scorecard 3.3 / 10
3.3aggregate

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 · 2026-10-16 03:01 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
9CI-Tests25 out of 26 merged PRs checked by a CI test -- score normalized to 9
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/29 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
7Maintained9 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 7
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities38 existing vulnerabilities detected
Direct dependencies 8
RegistryPackageVersion constraintManifest
npm@observablehq/framework^1.13.4frontend/package.json
npm@observablehq/plot^0.6.17frontend/package.json
npmd3^7.9.0frontend/package.json
PyPIscipy>=1.14.1pyproject.toml
PyPIpandas>=2.3.0pyproject.toml
PyPIpydantic>=2.0.2pyproject.toml
PyPIccy>=2.0.0pyproject.toml
PyPIstatsmodels>=0.14.6,<0.15.0pyproject.toml
All dependencies 540

Full resolved dependency set from the GitHub dependency graph: 9 direct and 531 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
npm@observablehq/framework1.13.4direct
npm@observablehq/plot0.6.17direct
npmd37.9.0direct
PyPIccy2.0.0direct
PyPIpandas3.0.5direct
PyPIpydantic2.13.4direct
PyPIscipy1.17.1direct
PyPIscipy1.18.0direct
PyPIstatsmodels0.14.6direct
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PyPIasync-timeout5.0.1indirect
PyPIattrs26.1.0indirect
PyPIbabel2.18.0indirect
PyPIbackrefs8.0indirect
PyPIblack26.5.1indirect
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PyPIcuda-bindings12.9.7indirect
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PyPIcuda-toolkit12.6.3indirect
PyPIdurationpy0.10indirect
PyPIexecuting2.2.1indirect
PyPIfastapi0.141.1indirect
PyPIfilelock3.32.2indirect
PyPIfrozenlist1.8.0indirect
PyPIfsspec2026.7.0indirect
PyPIghp-import2.1.0indirect
PyPIgriffe-pydantic1.3.1indirect
PyPIgriffe-typingdoc0.3.1indirect
PyPIgriffelib2.1.0indirect
PyPIh110.16.0indirect
PyPIhiredis3.4.0indirect
PyPIhjson3.1.0indirect
PyPIhttpcore22.9.1indirect
PyPIhttpx22.9.1indirect
PyPIhypothesis6.165.0indirect
PyPIidna3.18indirect
PyPIiniconfig2.3.0indirect
PyPIipython9.16.1indirect
PyPIipython-pygments-lexers1.1.1indirect
PyPIisort8.0.1indirect
PyPIjedi0.20.0indirect
PyPIjinja23.1.6indirect
PyPIkaleido1.3.0indirect
PyPIkubernetes36.0.3indirect
PyPIkubernetes-asyncio36.1.0indirect
PyPIlibrt0.13.0indirect
PyPIlogistro2.0.1indirect
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PyPImatplotlib-inline0.2.2indirect
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PyPImkdocs1.6.1indirect
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PyPImkdocs-get-deps0.2.2indirect
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PyPImkdocs-material9.7.7indirect
PyPImkdocs-material-extensions1.3.1indirect
PyPImkdocs-redirects1.2.3indirect
PyPImkdocstrings1.0.0indirect
PyPImkdocstrings-python2.0.5indirect
PyPImpmath1.3.0indirect
PyPImultidict6.7.1indirect
PyPImypy2.3.0indirect
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PyPInvidia-cublas-cu1212.6.4.1indirect
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PyPIoauthlib3.3.1indirect
PyPIorjson3.11.9indirect
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PyPIparso0.8.7indirect
PyPIpathspec1.1.1indirect
PyPIpatsy1.0.2indirect
PyPIpexpect4.9.0indirect
PyPIplatformdirs4.11.0indirect
PyPIplotly6.9.0indirect
PyPIpluggy1.6.0indirect
PyPIprometheus-client0.26.0indirect
PyPIprompt-toolkit3.0.53indirect
PyPIpropcache0.5.2indirect
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PyPIpydantic-core2.46.4indirect
PyPIpydantic-settings2.14.2indirect
PyPIpygments2.20.0indirect
PyPIpymdown-extensions11.0.1indirect
PyPIpytest9.1.1indirect
PyPIpytest-asyncio1.4.0indirect
PyPIpytest-cov7.1.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-dotenv1.2.2indirect
PyPIpython-json-logger4.1.0indirect
PyPIpython-slugify8.0.4indirect
PyPIpytokens0.4.1indirect
PyPIpyyaml6.0.3indirect
PyPIpyyaml-env-tag1.1indirect
PyPIquantflow1.2.0indirect
PyPIredis8.1.0indirect
PyPIrequests2.34.2indirect
PyPIrequests-oauthlib2.0.0indirect
PyPIruff0.16.1indirect
PyPIsetuptools83.0.0indirect
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PyPItermcolor3.3.0indirect
PyPItext-unidecode1.3indirect
PyPItomli2.4.1indirect
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PyPItyping-extensions4.16.0indirect
PyPItyping-inspection0.4.2indirect
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PyPIurllib32.7.0indirect
PyPIuvicorn0.52.1indirect
PyPIwatchdog6.0.0indirect
PyPIwcwidth0.8.2indirect
PyPIwebsocket-client1.9.0indirect
PyPIyarl1.24.5indirect
Dependency advisories 0

Installing pypi:quantflow@1.2.0 pulls in 15 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

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

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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.