スキル backtest-expert

📦

backtest-expert

Name: backtest-expert
Author: tradermonty

コンテンツリビジョン r1 安全 ⚙️ 外部コマンド

Evaluate Trading Backtests for Robustness

Backtest results can hide overfitting, weak samples, and unrealistic execution assumptions. This skill applies structured scoring, stress tests, and bias checks.

対応: Claude Codex Code(CC)

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自分のエージェントでインストール

このリクエストをエージェントにコピーしてください。正規の Skill ページとマニフェストが含まれています。

エージェントリクエスト

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

エージェントは引き続き計画を提示し、セキュリティポリシーで必要な確認を求める必要があります。

エージェントが読めるリソース

AI エージェント、クローラー、スクリプトがページ全体ではなく整理されたコンテキストを必要とする場合は、これらのリンクを使ってください。

Markdown詳細 GET /skills/tradermonty-backtest-expert.md 署名済み manifest GET /api/skills/tradermonty-backtest-expert/manifest 署名済み lockfile GET /api/skills/tradermonty-backtest-expert/lockfile

テストする

「backtest-expert」を使用しています。 Evaluate 150 trades, 62 percent wins, 1.8 percent average win, 1.2 percent average loss, 15 percent drawdown, eight years, three parameters, with slippage tested.

期待される結果:

Deploy, 81 out of 100. The sample, expectancy, drawdown, test duration, parameter count, and execution assumptions support further consideration. Independent validation remains necessary.

「backtest-expert」を使用しています。 Review 24 trades over two years with eight parameters and no slippage test.

期待される結果:

Verdict: Abandon.
The sample is below the minimum threshold.
The test period may omit important market regimes.
Eight parameters indicate substantial overfitting risk.
Execution costs have not been validated.

「backtest-expert」を使用しています。 Explain why a profitable strategy fails after realistic trading costs.

期待される結果:

The reported edge is smaller than expected slippage and commissions. Test conservative fills and nearby parameters before deciding whether the hypothesis remains viable.

セキュリティ監査

安全

v1 • 2026/9/27 バージョン別レポートを開く

All 17 static findings are false positives caused by trading prose, Python test identifiers, Markdown code fences, and inline code formatting. The skill performs local calculations and writes requested reports, with no network access, secret handling, prompt injection, or dynamic shell execution found.

スキャンされたファイル

1,839

解析済み行数

レビュー項目

誤検知を無視

リスク要因

⚙️ 外部コマンド (6)

SKILL.md:119-130 SKILL.md:130-181 SKILL.md:181-185 SKILL.md:185-186 SKILL.md:186-191 SKILL.md:191-204

最新の完了済み静的・セマンティック監査では、確認済みのセキュリティ検出事項は見つかりませんでした。これは、スキルに副作用がないことを証明するものではありません。

監査者: codex

このレポートを共有・引用

バージョン付き評価レポート、中立的なバッジ、埋め込みカード、引用を共有できます。Skillstore は証拠を報告しますが、この Skill が安全かどうかは判断しません。

バージョン別レポートを開く

レポートリンクをコピー

https://skillstore.io/skills/tradermonty-backtest-expert/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdownバッジ

[![Skillstore security assessment](https://skillstore.io/badges/skills/tradermonty-backtest-expert/security.svg)](https://skillstore.io/skills/tradermonty-backtest-expert?utm_source=security_passport_badge)

HTMLバッジ

<a href="https://skillstore.io/skills/tradermonty-backtest-expert?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/tradermonty-backtest-expert/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

埋め込みカード

<iframe src="https://skillstore.io/embed/skills/tradermonty-backtest-expert.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

学術引用 (APA · BibTeX · CFF)

APA形式の引用

tradermonty. (2026). backtest-expert security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/tradermonty-backtest-expert/audits/1

BibTeX形式の引用

@techreport{tradermonty-tradermonty-backtest-expert-2026,
  author = {tradermonty},
  title = {backtest-expert security audit report (audit version 1)},
  institution = {Skillstore},
  year = {2026},
  number = {1},
  url = {https://skillstore.io/skills/tradermonty-backtest-expert/audits/1},
  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: "backtest-expert security audit report (audit version 1)"
version: "unspecified"
type: report
authors:
  - name: "tradermonty"
date-released: "2026-09-27"
url: "https://skillstore.io/skills/tradermonty-backtest-expert/audits/1"
identifiers:
  - type: other
    value: "skillstore:tradermonty-backtest-expert:audit:1"
    description: "Skillstore immutable audit report identifier"

Skillstore スコア

このスコアの理由証拠の信頼度: 中

アーキテクチャ

保守性

コンテンツ

コミュニティ

仕様準拠

作成できるもの

Screen a New Strategy

Review early backtest metrics and identify the highest-priority validation gaps before further development.

Challenge Research Results

Apply bias checks, parameter stress tests, and walk-forward criteria during a formal research review.

Teach Robust Validation

Demonstrate why sample size, execution friction, market regimes, and out-of-sample testing affect confidence.

これらのプロンプトを試す

Review Basic Metrics

Evaluate this backtest: [trades], [win rate], [average win], [average loss], [drawdown], [years], [parameters], and [slippage tested]. Explain the verdict simply.

Build a Stress Test Plan

Create a stress test plan for this strategy: [rules]. Cover parameter ranges, slippage, commissions, fill quality, sample size, and market regimes.

Diagnose a Failed Backtest

Analyze these failed backtest results: [results]. Identify breaking points, likely biases, weak assumptions, and the next tests that could reject or refine the hypothesis.

Conduct an Adversarial Validation

Audit this strategy and evidence: [strategy], [metrics], [periods], [cost assumptions], [parameter tests], and [out-of-sample results]. Produce a prioritized robustness assessment and deployment conditions.

ベストプラクティス

Define entry, exit, sizing, filters, and the trading universe before testing.
Use multiple market regimes, out-of-sample periods, and conservative execution costs.
Prefer broad performance plateaus over isolated optimal parameter values.