Star200+

Backtesting.Py

STRONG ยท 83
Community indexed

๐Ÿ”Ž ๐Ÿ“ˆ ๐Ÿ ๐Ÿ’ฐ Backtest trading strategies in Python.

Downloads0
Stars8.5K
Version1.0.0
Quality97/100 ยท Excellent
Trust83/100 ยท Review then install
Audit87/100 ยท Safe to try

Supply asset profile

Finance and quant workflows

Market data, SEC filings, portfolio analysis, quant research, backtesting, and risk workflows.

Browse track

Scenario

Finance and quant

I need my agent to analyze markets, financial data, filings, portfolios, and quant strategies.

Agent fit

Claude Code + CLI + Codex

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add kernc/backtesting.py

Maintenance

stable

7mo since push

Risk

Safe to try

No major risk signals from available metadata

GitHub quality

8.5K

97/100 Quality ยท 86/100 Trust

Coverage tags

FinanceFinance and quanttradingautomationalgo-trading

Review notes

No major risk signals from available metadata

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Excellent
97

High-confidence pick with strong adoption and healthy maintenance signals.

Trust

Review then install
83

Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.

Audit

Safe to try
87

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Agent install candidate

Use as the primary candidate after human or sandbox review.

PythonTradingCodexClaude CodeCursor

Stars

8.5K GitHub stars

Repo activity

8.5K stars, 1.5K forks

Maintenance

7mo since push

License

AGPL-3.0

Install

npx skills add kernc/backtesting.py

Install safety

standard package or runtime install path

Permission surface

filesystem or document access

Agent outcomes

No agent outcome data yet

Docs

Usable metadata, review docs

Risk summary

Low metadata risk

  • No major trust warnings detected from available metadata

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is declared
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

Open JSON

Suited tasks

  • Finance and quant workflows
  • Claude Code teams
  • teams that value GitHub adoption signals
  • Retrieve market data

Suited agents

PythonTradingCodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add kernc/backtesting.py
Policy
allow
Human review
no

Trust and risk

Trust
83/100
Audit
87/100
Risk level
Safe to try

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add kernc/backtesting.py
Public auditEval reportResolve APIInstall handoff

Do not use when

  • teams that need a vendor-supported SLA
  • high-compliance environments without internal security review
  • No major risk signals from current metadata
  • No major trust warnings detected from available metadata
  • Production credentials, payments, or irreversible account changes without explicit human review

Alternative

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Alternative

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Alternative

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Agent safety v2

79/100 ยท Safe to install with normal review

Reviewedallow

Good audit and safety signals with no high-risk permission hints in public metadata.

Review the audit page, then allow agent install in a sandboxed workflow.

Resolve via API

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

Install targets

Install this skill in your agent workflow

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

skill install

OpenAgentSkill CLI

Use the registry command when your workflow supports the OpenAgentSkill installer.

$ npx skills add kernc/backtesting.py

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Open JSON

/api/agent/resolve?task=Use%20Backtesting.Py%20for%20an%20agent%20workflow&agent=codex&max_risk=medium

Resolve text

/api/agent/resolve?task=Use%20Backtesting.Py%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text

Install handoff

/api/skills/kernc-backtesting-py/install

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use Backtesting.Py in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20Backtesting.Py%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/kernc-backtesting-py/install
Install command: npx skills add kernc/backtesting.py
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Install handoff

/api/skills/kernc-backtesting-py/install

LLM text format

/api/skills/kernc-backtesting-py/install?format=text

Find alternatives

/api/skills/search?q=Backtesting.Py&limit=3

Agent prompt

Use Backtesting.Py for this task. Review https://www.openagentskill.com/api/skills/kernc-backtesting-py/install, then install with: npx skills add kernc/backtesting.py

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Manifest

/api/registry/manifest/kernc-backtesting-py

LLM text

/api/registry/manifest/kernc-backtesting-py?format=text

Install alias

/api/registry/install/kernc-backtesting-py

Recommend

/api/registry/recommend?task=Use%20Backtesting.Py%20in%20an%20agent%20workflow&limit=3

Agent fit

100/100

Finance and quant

Use-case tags

Finance and quantCoding agentsGitHub automation

Platforms

Python, Trading, Claude Code

Audit report

Safe to try ยท 87/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Primary pick for Finance and quant

Use this as a leading candidate, then validate the README and install path in your own agent stack.

100
Readiness
Adopt
Stage

Role in stack

Primary pick

Primary fit

Finance and quant

Trust label

Production-ready

Install path

Command ready

Use when

  • Finance and quant workflows
  • Claude Code teams
  • teams that value GitHub adoption signals

Evidence

  • 8,529 GitHub stars
  • install command or GitHub repo available
  • 97/100 quality profile
  • 24 OpenAgentSkill engagement events

review first

  • No major risk signals from current metadata

Implementation path

  1. 1Install it in a sandbox agent and run one Finance and quant task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Review then install

Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.

83
OpenAgentSkill Trust Score

GitHub adoption

PASS

8.5K GitHub stars

Stars/forks activity

PASS

8.5K stars, 1.5K forks; issue activity unavailable in current metadata

Recent maintenance

INFO

7mo since push

License clarity

PASS

AGPL-3.0

Good signals

  • Manually verified listing
  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Large GitHub adoption signal
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Use as the primary candidate after human or sandbox review.

Quality profile

Excellent candidate for agent workflows

High-confidence pick with strong adoption and healthy maintenance signals.

97
GitHub stars
8.5K
Freshness
7mo ago
Install ready
Yes
License
AGPL-3.0

Workflow fit

Use this skill in these scenarios

Analyze markets

Finance and quant

I need my agent to analyze markets, financial data, filings, portfolios, and quant strategies.

Build and ship code

Coding agents

I need a coding agent that can understand a repository, edit code, and review pull requests.

Manage repositories

GitHub automation

I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.

Workflow fit

Add it to a complete workflow

Inspect, patch, and verify code

Coding review agent

A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.

Turn skills into distribution

Content growth agent

A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.

Ingest, retrieve, and cite

RAG knowledge base

A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.

Alternative shortlist

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Overview

๐Ÿ”Ž ๐Ÿ“ˆ ๐Ÿ ๐Ÿ’ฐ Backtest trading strategies in Python.

Imported by the skill-only GitHub discovery pipeline because it matches agent skill, automation, domain workflow, RAG, document-processing, data, finance, security, or developer-tool signals. Protocol-server projects are excluded from automated imports.

Platform compatibility

pythonFULL
tradingFULL

Technical details

Version
1.0.0
License
AGPL-3.0
Last updated
Jun 18, 2026
Published
Jun 12, 2026

Frameworks & tools

PythonTrading

Decision snapshot

Primary pick

100
Ready
Adopt
Stage

8,529 GitHub stars

Audit

Install review

Install and adoption review

87
Safe to try
Security
88/100
Maintenance
62/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
โ€”
Recent failure
โ€”
Outcomes
0
Output quality
โ€”
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Agent-Proven rankingOutcome contract

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Compare AlternativesAuto-resolve PlanView on GitHubDocumentation

Growth loop

Share kit

X

Scenario-led draft for Backtesting.Py, ready for a manual X post.

Curator note
A practical pick for market research:

Backtesting.Py: ๐Ÿ”Ž ๐Ÿ“ˆ ๐Ÿ ๐Ÿ’ฐ Backtest trading strategies in Python.

8.5K stars

https://www.openagentskill.com/skills/kernc-backtesting-py?ref=x
Open X draft
Optional reply with install command
Listing + install path for Backtesting.Py:
https://www.openagentskill.com/skills/kernc-backtesting-py?ref=x

Install: npx skills add kernc/backtesting.py
Open reply draft

Listing source

Community indexed

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Creator
kernc
Indexed by
OpenAgentSkill community index

Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.

Claim this skill

Owner claim

Claim this skill listing

This Community indexed listing is attributed to kernc but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

Creator backlink kit

Add the evidence badges to your README

Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/kernc-backtesting-py?metric=listed&label=Listed)](https://www.openagentskill.com/skills/kernc-backtesting-py)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/kernc-backtesting-py?metric=trust&label=Trust)](https://www.openagentskill.com/skills/kernc-backtesting-py)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/kernc-backtesting-py?metric=audit&label=Audit)](https://www.openagentskill.com/skills/kernc-backtesting-py/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/kernc-backtesting-py?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/kernc-backtesting-py)
Preview badge Open audit Creator kit

Author

K

kerncโœ“

@kernc

Tags

financetradingautomationalgo-tradingalgorithmic-tradingbacktestingbacktesting-enginebacktesting-frameworksbacktesting-trading-strategiesfinancial-markets

Platform fit

Claude Code

Health signals

GitHub stars
8.5K
Quality score
62/100
Last GitHub push
Dec 20, 2025
Framework hints
2
OpenAgentSkill views
6
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Review then install

83
  • GitHub adoption8.5K GitHub starsPASS
  • Stars/forks activity8.5K stars, 1.5K forks; issue activity unavailable in current metadataPASS
  • Recent maintenance7mo since pushINFO
  • License clarityAGPL-3.0PASS
  • README/SKILL.md completenessPublic metadata needs stronger README/SKILL.md contextINFO
  • Dependency/runtime riskno major dependency risk hints in public metadataPASS

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