Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-19 17:24 UTC

ml4t / data

Market data acquisition, storage, and update workflows for machine learning for trading.

PythonMIT★ 49 stars⑂ 24 forkssince Nov 2025View on GitHub ↗

KindCommand-line toolLibraryhow this is determined

#algorithmic-trading #data-engineering #finance #financial-data #machine-learning-for-trading #market-data #ml4t #parquet #polars #python #quantitative-finance #trading

ml4t/data holds a health index of 91 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (92/100) and lowest on Community & Adoption (59/100). It was last updated today. A single contributor accounts for most of its recent work.

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.

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)

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 77 is calibrated to 91 on the published index scale (record calibration 2026-08-02).

Ownership

ML for TradingOrganization

131 followers12 public repossince Mar 2017

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

Package ecosystems

Registry	Package	Version	Downloads / mo	Versions	Last publish	Tags
PyPI	ml4t-data	0.1.7	7,655	37	0 days ago	algorithmic-trading backtesting binance cryptocompare databento finance market-data oanda parquet polars quantitative-finance trading yahoo-finance

Metrics by category

Vitality

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

85Excellent · 21% of overall

Development activity

81Excellent

How it's scored

36/36	Push recency — last push 0 days ago
17.3/36	Commit cadence — 25/52 weeks with commits
18/18	Commit volume — 137 commits in the last year
10/10	OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10

Inputs used

commits_last_year	137
human_commit_share	0.96
days_since_last_push	0
active_weeks_last_year	25

Release discipline

90Excellent

How it's scored

27/27	Ships releases — 28 releases published
36/36	Release recency — latest release 0 days ago
27/27	Release cadence — a release every ~5.6 days
0/10	OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.

Inputs used

releases_count	28
latest_release_tag	v0.1.7
releases_from_tags	no
days_since_latest_release	0
mean_days_between_releases	5.6

Community & Adoption

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

59Moderate · 17% of overall

Popularity & adoption

39Weak

How it's scored

27.3/60	Stars — 49 stars
11.3/25	Forks — 24 forks
0/15	Watchers — 0 watchers

Inputs used

forks	24
stars	49
watchers	0
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history

Community health

77Good

How it's scored

22.5/22.5	README
22.5/22.5	License — recognized license (MIT)
18/18	CONTRIBUTING guide
0/13.5	Code of conduct
0/7.2	Issue template
6.3/6.3	PR template

Inputs used

has_readme	yes
has_license	yes
readme_badges	3
has_contributing	yes
has_issue_template	no
has_code_of_conduct	no
readme_badge_services	shields.io
has_pull_request_template	yes

Ecosystem adoption (downloads)

65Good

How it's scored

51.8/80	Monthly downloads — 7,655 downloads/month across pypi
0/20	Registry dependents — not reported by this ecosystem

Inputs used

packages	ml4t-data
dependents	—
ecosystems	pypi
total_downloads	—
monthly_downloads	7,655
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?

65Good · 23% of overall

Maintainer resilience (bus factor)

31At Risk

How it's scored

9/54	Bus factor — 1 contributor(s) cover half of all commits
9/22.5	Commit distribution — top contributor authored 60% of commits
6.8/13.5	Contributor breadth — 5 contributors
6/10	OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6

Inputs used

bus_factor	1
contributors_sampled	5
top_contributor_share	0.6

Issue & PR responsiveness

77Good

How it's scored

38.2/42	Issue resolution — 91% of issues closed
24.1/30	PR acceptance — 37/46 decided PRs merged
13/13	Newcomer PR acceptance — 2/2 first-time contributors' PRs merged in 30d
1.5/15	OpenSSF Scorecard: Code-Review — Found 2/19 approved changesets -- score normalized to 1

Inputs used

merged_prs	37
open_issues	1
closed_issues	10
prs_merged_7d	3
prs_decided_7d	3
prs_merged_30d	9
prs_decided_30d	9
issue_closed_ratio	0.909
closed_unmerged_prs	9
first_time_authors_30d	2
first_time_prs_merged_30d	2
first_time_prs_decided_30d	2

Ownership & stewardship

65Good

How it's scored

30/30	Ownership backing — organization-owned
0/20	Verified domain
15.2/25	Owner reach — 131 followers of ml4t
20.1/25	Track record — 12 public repos, account ~9 yr old

Inputs used

followers	131
owner_type	Organization
is_verified	no
owner_login	ml4t
public_repos	12
account_age_days	3,487

Package maintenance

100Exceptional

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
35/35	Publish recency — latest publish 0 days ago
20/20	Version history — 37 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	ml4t-data
ecosystems	pypi
any_deprecated	no
min_days_since_publish	0

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional

How it's scored

24/24	CI workflows — 7 workflow(s)
24/24	Tests present
16/16	Linter config — pyproject.toml ([tool.ruff])
9.6/9.6	Pre-commit hooks
0/6.4	.editorconfig
20/20	OpenSSF Scorecard: CI-Tests — 19 out of 19 merged PRs checked by a CI test -- score normalized to 10

Inputs used

has_ci	yes
has_tests	yes
has_editorconfig	no
has_linter_config	yes
has_precommit_config	yes

Documentation

90Excellent

How it's scored

30/30	README
25/25	Documentation directory
15/15	Documentation / homepage site — https://www.ml4trading.io/docs/data/
10/10	Repository description
10/10	Topics — 12 topics
0/10	Wiki

Inputs used

topics	algorithmic-trading, data-engineering, finance, financial-data, machine-learning-for-trading, market-data, ml4t, parquet, polars, python, quantitative-finance, trading
has_wiki	no
homepage	https://www.ml4trading.io/docs/data/
docs_site	https://www.ml4trading.io/docs/data/
has_readme	yes
has_docs_dir	yes
has_description	yes

Security

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

80Excellent · 16% of overall

Security posture

75Good

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
0/7.5	Branch-Protection — no data
2.5/2.5	CI-Tests — 19 out of 19 merged PRs checked by a CI test -- score normalized to 10
0/2.5	CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0.8/7.5	Code-Review — Found 2/19 approved changesets -- score normalized to 1
1.5/2.5	Contributors — project has 2 contributing companies or organizations -- score normalized to 6
10/10	Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5	Dependency-Update-Tool — update tool detected
0/5	Fuzzing — project is not fuzzed
2.5/2.5	License — license file detected
7.5/7.5	Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5	Packaging — packaging workflow detected
5/5	Pinned-Dependencies — all dependencies are pinned
3.5/5	SAST — SAST tool detected but not run on all commits
5/5	Security-Policy — security policy file detected
0/7.5	Signed-Releases — Project has not signed or included provenance with any releases.
7.5/7.5	Token-Permissions — GitHub workflow tokens follow principle of least privilege
7.5/7.5	Vulnerabilities — 0 existing vulnerabilities detected

Inputs used

source	openssf_scorecard
checks_evaluated	17
scorecard_version	v5.5.0
checks_inconclusive	1
scorecard_aggregate	7.5

Excluded from scoring (no data or not applicable): Branch-Protection. Remaining weights renormalized.

Dependency advisories

100Exceptional

How it's scored

35/35	Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25	Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40	No advisories left outstanding — no advisory carries a publication date

Inputs used

source	osv
advisories	0
affected_packages	0
assessed_packages	165
unassessed_packages	0
affected_by_severity	none
direct_affected_packages	0

Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 165 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.

86Excellent · 4% of overall

Agent context & guidance

85Excellent

How it's scored

45/45	Agent instructions — AGENTS.md, src/ml4t/data/AGENTS.md, src/ml4t/data/futures/AGENTS.md, src/ml4t/data/providers/AGENTS.md, src/ml4t/data/storage/AGENTS.md
0/15	Machine-readable docs (llms.txt)
40/40	Legible commit history — 87 of 96 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	no
llms_txt_url	—
legible_history_share	0.906
agent_instruction_files	AGENTS.md, src/ml4t/data/AGENTS.md, src/ml4t/data/futures/AGENTS.md, src/ml4t/data/providers/AGENTS.md, src/ml4t/data/storage/AGENTS.md
agent_instruction_max_bytes	3,323

Verify loop (build / test / typecheck)

82Excellent

How it's scored

0/18	One-command bootstrap
22/22	Automated tests
11/11	Lint / format config — pyproject.toml ([tool.ruff])
11/11	Static type checking — src/ml4t/data/py.typed
10/10	Reproducible environment — lockfile
10/10	Demonstrated agent practice — 5 of the last 100 commits agent-authored or agent-credited
8/8	Automated maintenance — 4 of the last 100 commits are automated dependency updates
10/10	OpenSSF Scorecard: Pinned-Dependencies — all dependencies are pinned

Inputs used

has_nix	no
has_tests	yes
lockfiles	uv.lock
has_dockerfile	no
typed_language	no
bootstrap_files	—
has_devcontainer	no
has_linter_config	yes
typecheck_configs	src/ml4t/data/py.typed
agent_commit_share	0.05
toolchain_manifests	—
dependency_bot_commit_share	0.04

Code legibility for models

82Excellent

How it's scored

27/45	Type-checkable code — Python with type-check config (src/ml4t/data/py.typed)
54.7/55	Manageable file sizes — 2/369 source files over 60KB

Inputs used

primary_language	Python
largest_source_bytes	67,824
source_files_sampled	369
oversized_source_files	2

Machine-readable interfaces

100Exceptional

How it's scored

0/40	API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20	MCP server — not applicable to this kind of software
40/40	Runnable examples — examples

Inputs used

example_dirs	examples
has_mcp_signal	no
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

49GitHub stars

5contributors

137commits, last 12 months

0days since last push

28releases

1bus factor

1open 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-data@0.1.7; advisories assessed against the repository dependency graph instead

More detail

Star and fork history 0 ★ / 24 ⇿

0Stars

24Forks

27Releases

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.

Major 0Minor 1Patch 6

OpenSSF Scorecard 7.5 / 10

7.5aggregate

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 · 2026-09-19 17:24 UTC

10	Binary-Artifacts	no binaries found in the repo
n/a	Branch-Protection	internal 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
10	CI-Tests	19 out of 19 merged PRs checked by a CI test -- score normalized to 10
0	CII-Best-Practices	no effort to earn an OpenSSF best practices badge detected
1	Code-Review	Found 2/19 approved changesets -- score normalized to 1
6	Contributors	project has 2 contributing companies or organizations -- score normalized to 6
10	Dangerous-Workflow	no dangerous workflow patterns detected
10	Dependency-Update-Tool	update tool detected
0	Fuzzing	project is not fuzzed
10	License	license file detected
10	Maintained	30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10	Packaging	packaging workflow detected
10	Pinned-Dependencies	all dependencies are pinned
7	SAST	SAST tool detected but not run on all commits
10	Security-Policy	security policy file detected
0	Signed-Releases	Project has not signed or included provenance with any releases.
10	Token-Permissions	GitHub workflow tokens follow principle of least privilege
10	Vulnerabilities	0 existing vulnerabilities detected

Direct dependencies 18

Registry	Package	Version constraint	Manifest
PyPI	polars	`>=0.20.0`	pyproject.toml
PyPI	pandas	`>=2.0.0`	pyproject.toml
PyPI	numpy	`>=1.24.0`	pyproject.toml
PyPI	httpx	`>=0.25.0`	pyproject.toml
PyPI	tenacity	`>=8.0.0`	pyproject.toml
PyPI	pybreaker	`>=1.0.0`	pyproject.toml
PyPI	pyyaml	`>=6.0`	pyproject.toml
PyPI	click	`>=8.0.0`	pyproject.toml
PyPI	python-dotenv	`>=1.0.0`	pyproject.toml
PyPI	pydantic-settings	`>=2.0.0`	pyproject.toml
PyPI	pydantic	`>=2.12,<3`	pyproject.toml
PyPI	structlog	`>=23.0.0`	pyproject.toml
PyPI	platformdirs	`>=4.0.0`	pyproject.toml
PyPI	filelock	`>=3.19.1`	pyproject.toml
PyPI	rich	`>=13.0.0`	pyproject.toml
PyPI	aiofiles	`>=23.0.0`	pyproject.toml
PyPI	pandas-market-calendars	`>=4.3.0`	pyproject.toml
PyPI	openpyxl	`>=3.1.5`	pyproject.toml

All dependencies 165

Full resolved dependency set from the GitHub dependency graph: 19 direct and 146 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	aiofiles	`25.1.0`	direct
PyPI	click	`8.4.2`	direct
PyPI	filelock	`3.32.2`	direct
PyPI	httpx	`0.28.1`	direct
PyPI	numpy	`2.5.2`	direct
PyPI	openpyxl	`3.1.5`	direct
PyPI	pandas	`3.0.5`	direct
PyPI	pandas-market-calendars	`5.4.0`	direct
PyPI	platformdirs	`4.11.0`	direct
PyPI	polars	`1.43.2`	direct
PyPI	pybreaker	`1.4.1`	direct
PyPI	pydantic	`2.13.4`	direct
PyPI	pydantic	`2.14.0b1`	direct
PyPI	pydantic-settings	`2.15.0`	direct
PyPI	python-dotenv	`1.2.2`	direct
PyPI	pyyaml	`6.0.3`	direct
PyPI	rich	`15.0.0`	direct
PyPI	structlog	`26.1.0`	direct
PyPI	tenacity	`9.1.4`	direct
PyPI	aiohappyeyeballs	`2.7.1`	indirect
PyPI	aiohttp	`3.14.3`	indirect
PyPI	aiosignal	`1.4.0`	indirect
PyPI	annotated-types	`0.8.0`	indirect
PyPI	anyio	`4.14.2`	indirect
PyPI	asttokens	`3.0.2`	indirect
PyPI	attrs	`26.1.0`	indirect
PyPI	babel	`2.18.0`	indirect
PyPI	backrefs	`8.0`	indirect
PyPI	beautifulsoup4	`4.15.0`	indirect
PyPI	boolean-py	`5.0`	indirect
PyPI	cachecontrol	`0.14.4`	indirect
PyPI	certifi	`2026.7.22`	indirect
PyPI	cffi	`2.1.1`	indirect
PyPI	cfgv	`3.5.0`	indirect
PyPI	charset-normalizer	`3.4.9`	indirect
PyPI	colorama	`0.4.6`	indirect
PyPI	cot-reports	`0.1.3`	indirect
PyPI	coverage	`7.15.4`	indirect
PyPI	cryptography	`50.0.0`	indirect
PyPI	curl-cffi	`0.16.0`	indirect
PyPI	cyclonedx-python-lib	`11.11.0`	indirect
PyPI	databento	`0.83.0`	indirect
PyPI	databento-dbn	`0.65.0`	indirect
PyPI	decorator	`5.3.1`	indirect
PyPI	defusedxml	`0.7.1`	indirect
PyPI	distlib	`0.4.3`	indirect
PyPI	docutils	`0.23`	indirect
PyPI	et-xmlfile	`2.0.0`	indirect
PyPI	exchange-calendars	`4.13.2`	indirect
PyPI	execnet	`2.1.2`	indirect
PyPI	executing	`2.2.1`	indirect
PyPI	frozenlist	`1.8.0`	indirect
PyPI	ghp-import	`2.1.0`	indirect
PyPI	gitdb	`4.0.12`	indirect
PyPI	gitpython	`3.1.62`	indirect
PyPI	griffelib	`2.1.0`	indirect
PyPI	h11	`0.16.0`	indirect
PyPI	httpcore	`1.0.9`	indirect
PyPI	hypothesis	`6.165.2`	indirect
PyPI	id	`1.6.1`	indirect
PyPI	identify	`2.6.19`	indirect
PyPI	idna	`3.18`	indirect
PyPI	iniconfig	`2.3.0`	indirect
PyPI	ipdb	`0.13.13`	indirect
PyPI	ipython	`9.16.1`	indirect
PyPI	ipython-pygments-lexers	`1.1.1`	indirect
PyPI	jaraco-classes	`3.4.0`	indirect
PyPI	jaraco-context	`6.1.2`	indirect
PyPI	jaraco-functools	`4.6.0`	indirect
PyPI	jedi	`0.20.0`	indirect
PyPI	jeepney	`0.9.0`	indirect
PyPI	jinja2	`3.1.6`	indirect
PyPI	keyring	`25.7.0`	indirect
PyPI	korean-lunar-calendar	`0.4.0`	indirect
PyPI	license-expression	`30.4.4`	indirect
PyPI	markdown	`3.10.3`	indirect
PyPI	markdown-it-py	`4.2.0`	indirect
PyPI	markupsafe	`3.0.3`	indirect
PyPI	matplotlib-inline	`0.2.2`	indirect
PyPI	mdurl	`0.1.2`	indirect
PyPI	mergedeep	`1.3.4`	indirect
PyPI	mkdocs	`1.6.1`	indirect
PyPI	mkdocs-autorefs	`1.4.4`	indirect
PyPI	mkdocs-get-deps	`0.2.2`	indirect
PyPI	mkdocs-git-revision-date-localized-plugin	`1.5.3`	indirect
PyPI	mkdocs-material	`9.7.7`	indirect
PyPI	mkdocs-material-extensions	`1.3.1`	indirect
PyPI	mkdocstrings	`1.0.6`	indirect
PyPI	mkdocstrings-python	`2.0.5`	indirect
PyPI	more-itertools	`11.1.0`	indirect
PyPI	msgpack	`1.2.1`	indirect
PyPI	multidict	`6.7.1`	indirect
PyPI	multitasking	`0.0.13`	indirect
PyPI	nh3	`0.3.6`	indirect
PyPI	nodeenv	`1.10.0`	indirect
PyPI	oandapyv20	`0.7.2`	indirect
PyPI	packageurl-python	`0.17.6`	indirect
PyPI	packaging	`26.3`	indirect
PyPI	paginate	`0.5.7`	indirect
PyPI	parso	`0.8.7`	indirect
PyPI	pathspec	`1.1.1`	indirect
PyPI	peewee	`4.3.0`	indirect
PyPI	pexpect	`4.9.0`	indirect
PyPI	pip	`26.2.1`	indirect
PyPI	pip-api	`0.0.34`	indirect
PyPI	pip-audit	`2.10.1`	indirect
PyPI	pip-requirements-parser	`32.0.1`	indirect
PyPI	pip-system-certs	`5.3`	indirect
PyPI	pluggy	`1.6.0`	indirect
PyPI	polars-runtime-32	`1.43.2`	indirect
PyPI	pre-commit	`4.6.1`	indirect
PyPI	prompt-toolkit	`3.0.53`	indirect
PyPI	propcache	`0.5.2`	indirect
PyPI	protobuf	`7.35.1`	indirect
PyPI	psutil	`7.2.2`	indirect
PyPI	ptyprocess	`0.7.0`	indirect
PyPI	pure-eval	`0.2.3`	indirect
PyPI	py-serializable	`2.1.0`	indirect
PyPI	pyarrow	`25.0.0`	indirect
PyPI	pycparser	`3.0`	indirect
PyPI	pydantic-core	`2.46.4`	indirect
PyPI	pydantic-core	`2.48.0`	indirect
PyPI	pygments	`2.20.0`	indirect
PyPI	pyluach	`2.3.0`	indirect
PyPI	pymdown-extensions	`11.0.1`	indirect
PyPI	pyparsing	`3.3.2`	indirect
PyPI	pytest	`9.1.1`	indirect
PyPI	pytest-asyncio	`1.4.0`	indirect
PyPI	pytest-cov	`7.1.0`	indirect
PyPI	pytest-timeout	`2.4.0`	indirect
PyPI	pytest-xdist	`3.8.0`	indirect
PyPI	python-dateutil	`2.9.0.post0`	indirect
PyPI	python-discovery	`1.5.1`	indirect
PyPI	pytz	`2026.3.post1`	indirect
PyPI	pywin32-ctypes	`0.2.3`	indirect
PyPI	pyyaml-env-tag	`1.1`	indirect
PyPI	readme-renderer	`45.0`	indirect
PyPI	requests	`2.34.2`	indirect
PyPI	requests-toolbelt	`1.0.0`	indirect
PyPI	rfc3986	`2.0.0`	indirect
PyPI	ruff	`0.16.2`	indirect
PyPI	secretstorage	`3.5.0`	indirect
PyPI	six	`1.17.0`	indirect
PyPI	smmap	`5.0.3`	indirect
PyPI	sortedcontainers	`2.4.0`	indirect
PyPI	soupsieve	`2.9.2`	indirect
PyPI	stack-data	`0.6.3`	indirect
PyPI	tomli	`2.4.1`	indirect
PyPI	tomli-w	`1.2.0`	indirect
PyPI	toolz	`1.1.0`	indirect
PyPI	traitlets	`5.16.1`	indirect
PyPI	twine	`7.0.0`	indirect
PyPI	ty	`0.0.69`	indirect
PyPI	typing-extensions	`4.16.0`	indirect
PyPI	typing-inspection	`0.4.2`	indirect
PyPI	tzdata	`2026.3`	indirect
PyPI	urllib3	`2.7.0`	indirect
PyPI	virtualenv	`21.7.1`	indirect
PyPI	watchdog	`6.0.0`	indirect
PyPI	wcwidth	`0.8.2`	indirect
PyPI	websockets	`17.0.1`	indirect
PyPI	xlsxwriter	`3.2.9`	indirect
PyPI	yarl	`1.24.5`	indirect
PyPI	yfinance	`1.5.2`	indirect
PyPI	zstandard	`0.25.0`	indirect

Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 165 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.

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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.

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