Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 13:13 UTC

ml4t / backtest

Event-driven backtesting for quantitative strategies with configurable execution, accounting, risk, and framework-parity validation.

PythonMIT★ 26 stars⑂ 21 forkssince Nov 2025View on GitHub ↗
KindLibraryhow this is determined
#algorithmic-trading#backtesting#event-driven#execution-modeling#finance#machine-learning-for-trading#ml4t#portfolio#python#quantitative-finance#risk-management#trading

ml4t/backtest holds a health index of 87 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (92/100) and lowest on Community & Adoption (51/100). It was last updated 1 day ago. A single contributor accounts for most of its recent work.

87
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.

87
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 73 is calibrated to 87 on the published index scale (record calibration 2026-08-02).

Ownership

ML for TradingOrganization
130 followers11 public repossince Mar 2017

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIml4t-backtest0.1.711,128395 days agoalgorithmic-tradingbacktestingevent-drivenexecutionfinancepolarsportfolioquantitative-financerisk-managementsimulationtrading

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
36/36Push recency — last push 1 days ago
17.3/36Commit cadence — 25/52 weeks with commits
18/18Commit volume — 174 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year174
human_commit_share0.97
days_since_last_push1
active_weeks_last_year25

Release discipline

90Excellent
How it's scored
27/27Ships releases — 33 releases published
36/36Release recency — latest release 5 days ago
27/27Release cadence — a release every ~13.1 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count33
latest_release_tagv0.1.7
releases_from_tagsno
days_since_latest_release5
mean_days_between_releases13.1

Community & Adoption

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

51Moderate · 17% of overall

Popularity & adoption

34At Risk
How it's scored
22.7/60Stars — 26 stars
10.8/25Forks — 21 forks
0/15Watchers — 1 watchers
Inputs used
forks21
stars26
watchers1
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

57Moderate
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateyes

Ecosystem adoption (downloads)

68Good
How it's scored
54/80Monthly downloads — 11,128 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesml4t-backtest
dependents—
ecosystemspypi
total_downloads—
monthly_downloads11,128
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?

59Moderate · 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

76Good
How it's scored
40.7/42Issue resolution — 97% of issues closed
25.7/30PR acceptance — 54/63 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/28 approved changesets -- score normalized to 0
Inputs used
merged_prs54
open_issues1
closed_issues32
prs_merged_7d3
prs_decided_7d4
prs_merged_30d10
prs_decided_30d12
issue_closed_ratio0.97
closed_unmerged_prs9
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

65Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
15.2/25Owner reach — 130 followers of ml4t
19.9/25Track record — 11 public repos, account ~9 yr old
Inputs used
followers130
owner_typeOrganization
is_verifiedno
owner_loginml4t
public_repos11
account_age_days3,484

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 5 days ago
20/20Version history — 39 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesml4t-backtest
ecosystemspypi
any_deprecatedno
min_days_since_publish5

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 7 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 — 30 out of 30 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/backtest/
10/10Repository description
10/10Topics — 12 topics
0/10Wiki
Inputs used
topicsalgorithmic-trading, backtesting, event-driven, execution-modeling, finance, machine-learning-for-trading, ml4t, portfolio, python, quantitative-finance, risk-management, trading
has_wikino
homepagehttps://www.ml4trading.io/docs/backtest/
docs_sitehttps://www.ml4trading.io/docs/backtest/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

77Good · 16% of overall

Security posture

71Good
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 — 30 out of 30 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 0/28 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 — 30 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
1/5SAST — SAST tool is not run on all commits -- score normalized to 2
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_aggregate7.1
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
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_packages14
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:ml4t-backtest@0.1.7 runtime dependency closure — what installing the published package pulls in — 14 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.

83Excellent · 4% of overall

Agent context & guidance

85Excellent
How it's scored
45/45Agent instructions — AGENTS.md, src/ml4t/backtest/AGENTS.md, src/ml4t/backtest/accounting/AGENTS.md, src/ml4t/backtest/analytics/AGENTS.md, src/ml4t/backtest/core/AGENTS.md, src/ml4t/backtest/execution/AGENTS.md, src/ml4t/backtest/risk/AGENTS.md, src/ml4t/backtest/risk/portfolio/AGENTS.md, src/ml4t/backtest/risk/position/AGENTS.md, src/ml4t/backtest/strategies/AGENTS.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 97 of 97 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, src/ml4t/backtest/AGENTS.md, src/ml4t/backtest/accounting/AGENTS.md, src/ml4t/backtest/analytics/AGENTS.md, src/ml4t/backtest/core/AGENTS.md, src/ml4t/backtest/execution/AGENTS.md, src/ml4t/backtest/risk/AGENTS.md, src/ml4t/backtest/risk/portfolio/AGENTS.md, src/ml4t/backtest/risk/position/AGENTS.md, src/ml4t/backtest/strategies/AGENTS.md
agent_instruction_max_bytes2,529

Verify loop (build / test / typecheck)

76Good
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/backtest/py.typed
10/10Reproducible environment — lockfile
4/10Demonstrated agent practice — 2 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 3 of the last 100 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/backtest/py.typed
agent_commit_share0.02
toolchain_manifests—
dependency_bot_commit_share0.03

Code legibility for models

81Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (src/ml4t/backtest/py.typed)
53.7/55Manageable file sizes — 6/257 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes133,459
source_files_sampled257
oversized_source_files6

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples, notebooks
Inputs used
example_dirsexamples, notebooks
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

26GitHub stars
1contributors
174commits, last 12 months
1days since last push
33releases
1bus factor
1open issues
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 ★ / 21 ⇿
0Stars
21Forks
13Releases

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.

048121620242132026-042026-062026-09
Major 0Minor 1Patch 4
OpenSSF Scorecard 7.1 / 10
7.1aggregate

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 · 2026-09-17 13:13 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-Tests30 out of 30 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 0/28 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
10Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
10Pinned-Dependenciesall dependencies are pinned
2SASTSAST tool is not run on all commits -- score normalized to 2
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 7
RegistryPackageVersion constraintManifest
PyPIml4t-specs>=0.1.4,<0.2pyproject.toml
PyPIpolars>=1.36.1pyproject.toml
PyPIpandas>=2.3.3pyproject.toml
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PyPIPyYAML>=6.0.3pyproject.toml
PyPIpandas-market-calendars>=5.4.0pyproject.toml
All dependencies 251

Full resolved dependency set from the GitHub dependency graph: 10 direct and 241 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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PyPItoolz1.1.0indirect
PyPItqdm4.70.0indirect
PyPItraitlets5.16.1indirect
PyPItwine7.0.0indirect
PyPIty0.0.18indirect
PyPItyper0.27.1indirect
PyPItyping-extensions4.16.0indirect
PyPItyping-inspection0.4.4indirect
PyPItzdata2026.3indirect
PyPItzlocal5.4.4indirect
PyPIurllib32.7.0indirect
PyPIvectorbt1.0.0indirect
PyPIvectorbt1.1.0indirect
PyPIvectorbtpro2025.12.31indirect
PyPIvectorbtpro2026.6.27indirect
PyPIvirtualenv21.7.8indirect
PyPIwatchdog6.0.0indirect
PyPIwcwidth0.8.2indirect
PyPIwcwidth0.8.3indirect
PyPIwebsocket-client1.9.0indirect
PyPIwerkzeug3.1.8indirect
PyPIwidgetsnbextension4.0.15indirect
PyPIwidgetsnbextension4.0.16indirect
PyPIwrapt2.4.0indirect
PyPIzipline-reloaded3.1.1indirect
PyPIzipp4.1.0indirect
Dependency advisories 0

Installing pypi:ml4t-backtest@0.1.7 pulls in 14 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.

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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.