Public record
Software health reportschema 0.14.0 · metrics 2.10.0 · 2026-07-19 07:57 UTC

mehdizare / fmp-data

FMP Data: A Modern Python Client for Financial Modeling Prep API

PythonMIT★ 27 stars⑂ 5 forkssince Oct 2024View on GitHub ↗

mehdizare/fmp-data holds a health index of 71 out of 100, placing it in the Good band. It scores highest on Engineering Quality (90/100) and lowest on Security (40/100). It was last updated 6 days ago. A single contributor accounts for most of its recent work.

71
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.

71
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 63 is calibrated to 71 on the published index scale (record calibration 2026-08-02).

Ownership

MehdiPersonal account
4 followers20 public repossince Jul 2014

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIfmp-data2.4.0-276 days agoapifinancialfinancial-datafmpmarket-datastock-marketstocks

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 recencylast push 6 days ago
7.6/36Commit cadence11/52 weeks with commits
18/18Commit volume213 commits in the last year
5/10OpenSSF Scorecard: Maintained5 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 5
Inputs used
commits_last_year213
human_commit_share
days_since_last_push6
active_weeks_last_year11
How it's scored
27/27Ships releases26 releases published
36/36Release recencylatest release 6 days ago
27/27Release cadencea release every ~18.4 days
0/10OpenSSF Scorecard: Signed-ReleasesProject has not signed or included provenance with any releases.
Inputs used
releases_count26
latest_release_tagv2.4.0
releases_from_tagsno
days_since_latest_release6
mean_days_between_releases18.4

Community & Adoption

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

48Weak · 17% of overall
How it's scored
23/60Stars27 stars
5/25Forks5 forks
0/15Watchers1 watchers
Inputs used
forks5
stars27
watchers1
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services
has_pull_request_templateno

Sustainability & Governance

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

55Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
0.4/22.5Commit distributiontop contributor authored 98% of commits
4.1/13.5Contributor breadth3 contributors
0/10OpenSSF Scorecard: Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
Inputs used
bus_factor1
contributors_sampled3
top_contributor_share0.982
How it's scored
42/42Issue resolution100% of issues closed
23.7/30PR acceptance72/91 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 0/7 approved changesets -- score normalized to 0
Inputs used
merged_prs72
open_issues0
closed_issues15
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio1
closed_unmerged_prs19
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
5/25Owner reach4 followers of MehdiZare
21.6/25Track record20 public repos, account ~12 yr old
Inputs used
followers4
owner_typeUser
is_verified
owner_loginMehdiZare
public_repos20
account_age_days4,389
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 6 days ago
20/20Version history27 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesfmp-data
ecosystemspypi
any_deprecatedno
min_days_since_publish6

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 19% of overall
How it's scored
24/24CI workflows11 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests9 out of 9 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

85Excellent
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics3 topics
10/10Wiki
Inputs used
topicsfinance, financial-analysis, stock-market
has_wikiyes
homepage
docs_site
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

40Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0.8/7.5Branch-Protectionbranch protection is not maximal on development and all release branches
2.5/2.5CI-Tests9 out of 9 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 0/7 approved changesets -- score normalized to 0
0/2.5Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
0/10Dangerous-Workflowdangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
3.8/7.5Maintained5 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 5
5/5Packagingpackaging workflow detected
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
5/5SASTSAST tool is run on all commits
0/5Security-Policysecurity policy file not detected
0/7.5Signed-ReleasesProject has not signed or included provenance with any releases.
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated18
scorecard_versionv5.5.0
checks_inconclusive0
scorecard_aggregate4

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
How it's scored
45/45Agent instructionsAGENTS.md, CLAUDE.md
0/15Machine-readable docs (llms.txt)
0/40Legible commit historyno data
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share
agent_instruction_filesAGENTS.md, CLAUDE.md
agent_instruction_max_bytes8,424
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
18/18One-command bootstrapMakefile, noxfile.py
22/22Automated tests
11/11Lint / format config
11/11Static type checkingfmp_data/py.typed
0/10Reproducible environment
0/10Demonstrated agent practiceno data
5/8Automated maintenancedependency automation configured, none observed in the sampled commits
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, noxfile.py
has_devcontainerno
has_linter_configyes
typecheck_configsfmp_data/py.typed
agent_commit_share
toolchain_manifests
dependency_bot_commit_share0
Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.
How it's scored
27/45Type-checkable codePython with type-check config (fmp_data/py.typed)
54.2/55Manageable file sizes3/212 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes123,606
source_files_sampled212
oversized_source_files3
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_files
interfaces_expected_of
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

27GitHub stars
3contributors
213commits, last 12 months
6days since last push
26releases
1bus factor
0open issues
PyPIpackage ecosystems

More detail

OpenSSF Scorecard 4.0 / 10
4.0aggregate

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 · 2026-07-19 07:57 UTC

10Binary-Artifactsno binaries found in the repo
1Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests9 out of 9 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 0/7 approved changesets -- score normalized to 0
0Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
0Dangerous-Workflowdangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
5Maintained5 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 5
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
10SASTSAST tool is run on all commits
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 3
RegistryPackageVersion constraintManifest
PyPIhttpx>=0.28.1pyproject.toml
PyPIpydantic>=2.13.4pyproject.toml
PyPItenacity>=9.1.4pyproject.toml
All dependencies 36

Full resolved dependency set from the GitHub dependency graph: 3 direct and 33 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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PyPIpydanticdirect
PyPItenacitydirect
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PyPIfaiss-cpuindirect
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PyPIhatch-vcsindirect
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PyPIlangchain-coreindirect
PyPIlangchain-openaiindirect
PyPIlanggraphindirect
PyPImcpindirect
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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 v0.14.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.