Skills backtesting-frameworks
📦

backtesting-frameworks

Content revision r2 Safe ⚡ Contains scripts⚙️ External commands

Build Reliable Strategy Backtests

Trading backtests often overstate performance because they ignore bias, costs, and uncertainty. This skill provides practical structures for realistic simulation and validation.

Supports: Claude Codex Code(CC)
🥉 78 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "backtesting-frameworks" from https://skillstore.io/skills/sickn33-backtesting-frameworks.md and its manifest at https://skillstore.io/api/skills/sickn33-backtesting-frameworks/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/sickn33-backtesting-frameworks.md Signed manifest GET /api/skills/sickn33-backtesting-frameworks/manifest Signed lockfile GET /api/skills/sickn33-backtesting-frameworks/lockfile

Test it

Using "backtesting-frameworks". Design a daily momentum backtest with realistic assumptions.

Expected outcome:

  • Use point-in-time daily bars and generate each signal after the close.
  • Execute on the next bar and model commission, slippage, and position limits.
  • Reserve the final period for testing and report drawdown with risk-adjusted returns.

Using "backtesting-frameworks". How should I validate three strategy parameters?

Expected outcome:

Use rolling walk-forward windows. Select parameters only on each training window, evaluate the next unseen period, and combine only out-of-sample results.

Using "backtesting-frameworks". Which uncertainty checks should accompany the Sharpe ratio?

Expected outcome:

Report maximum drawdown, downside risk, trade count, and parameter stability. Bootstrap returns to estimate loss probability and plausible drawdown ranges.

Security Audit

Safe
v5 • 8/14/2026 Open versioned report

All 11 static findings are false positives caused by Python type annotations, dictionary methods, ordinary prose, and Markdown code formatting. No executable scripts, shell commands, credential access, reconnaissance behavior, or prompt injection were found.

2
Files scanned
696
Lines analyzed
0
Review items
0
False positives ignored

Risk Factors

⚡ Contains scripts (1)
resources/implementation-playbook.md:403
⚙️ External commands (2)
SKILL.md:33 SKILL.md:42
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
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Open versioned report
Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

sickn33. (2026). backtesting-frameworks security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-backtesting-frameworks/audits/5

BibTeX citation

@techreport{sickn33-sickn33-backtesting-frameworks-2026, author = {sickn33}, title = {backtesting-frameworks security audit report (audit version 5)}, institution = {Skillstore}, year = {2026}, number = {5}, url = {https://skillstore.io/skills/sickn33-backtesting-frameworks/audits/5}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "backtesting-frameworks security audit report (audit version 5)" version: "unspecified" type: report authors: - name: "sickn33" date-released: "2026-08-14" url: "https://skillstore.io/skills/sickn33-backtesting-frameworks/audits/5" identifiers: - type: other value: "skillstore:sickn33-backtesting-frameworks:audit:5" description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Higher Skillstore usage
wshobson Recommended

wshobson-backtesting-frameworks

Skillstore Score 78
Evidence Confidence High
Skillstore usage 23
Updated

2026-08-21

sickn33 Current

sickn33-backtesting-frameworks

Skillstore Score 78
Evidence Confidence High
Skillstore usage 13
Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High
55
Architecture
85
Maintainability
87
Content
69
Community
83
Spec Compliance

What You Can Build

Prototype a Research Backtest

Create an event-driven or vectorized design with realistic transaction costs and delayed signal execution.

Review Validation Methodology

Identify look-ahead, survivorship, selection, and overfitting risks before accepting reported strategy performance.

Plan a Backtesting Platform

Define portfolio, execution, optimization, and reporting components for a maintainable internal research system.

Try These Prompts

Define a Basic Backtest 
Design a backtest for [strategy] using [market] data from [period]. Include assumptions, signal timing, transaction costs, and evaluation metrics.
Choose a Simulation Architecture 
Compare event-driven and vectorized designs for [strategy]. Recommend one based on order complexity, data volume, execution realism, and maintenance needs.
Create a Walk-Forward Plan 
Create a walk-forward validation plan for [strategy]. Specify training, validation, and test windows. Explain parameter selection and leakage controls.
Audit an Advanced Methodology 
Audit this backtesting methodology: [details]. Assess bias, cost modeling, capacity, overfitting, regime sensitivity, Monte Carlo uncertainty, and reporting gaps. Prioritize corrections.

Best Practices

  • Use point-in-time data and delay signal execution to prevent look-ahead bias.
  • Model commissions, slippage, market impact, liquidity, and portfolio constraints.
  • Keep an untouched test period and report uncertainty alongside average performance.

Avoid

  • Optimizing parameters across the full history and reporting the same period.
  • Testing only surviving securities or using revised data unavailable at the decision time.
  • Presenting gross returns without costs, capacity limits, drawdowns, or uncertainty.

Frequently Asked Questions

Does this skill place trades?
No. It supports historical simulation design and does not provide broker connectivity or live execution.
Can it obtain market data?
No. You must provide suitable historical data and verify its quality, timestamps, adjustments, and point-in-time accuracy.
When should I use an event-driven backtester?
Use it when order lifecycle, multiple assets, execution timing, partial fills, or portfolio state require detailed simulation.
When is a vectorized design appropriate?
Use it for simple signal strategies where speed matters and execution behavior can be represented accurately with array operations.
Does walk-forward testing eliminate overfitting?
No. It reduces leakage and measures stability, but repeated experimentation can still overfit the research process.
Are the performance estimates financial advice?
No. Historical estimates are uncertain, depend on modeling assumptions, and do not guarantee future results.

Developer Details

Author

sickn33

License

MIT

Skillstore revision

r2

Version notice

The author did not declare a version.

Repository

https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/backtesting-frameworks

Ref

5a26d1d61d694db29af9b138c661c1981076d9df

Maintenance freshness

8/15/2026

Usage

12 downloads · 136 views

File structure

📁 resources/

📄 implementation-playbook.md

📄 SKILL.md