backtesting-frameworks
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- 88 / 100 · community maintained
- Author / version / license
- @wshobson · no license declared
- Token usage
- Lean
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- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
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- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: backtesting-frameworks
description: Build robust backtesting systems for trading strategies with proper handling of look-ahead bias…
category: engineering
runtime: no special runtime
---
# backtesting-frameworks output preview
## PART A: Task fit
- Use case: Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to Use This Skill / Core Concepts / 1. Backtesting Biases” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.”.
- **02** When the source has headings, the agent prioritizes “When to Use This Skill / Core Concepts / 1. Backtesting Biases” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “When to Use This Skill / Core Concepts / 1. Backtesting Biases”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: backtesting-frameworks
description: Build robust backtesting systems for trading strategies with proper handling of look-ahead bias…
category: engineering
source: wshobson/agents
---
# backtesting-frameworks
## When to use
- Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, an…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “When to Use This Skill / Core Concepts / 1. Backtesting Biases” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "backtesting-frameworks" {
input -> user goal + target files + boundaries + acceptance criteria
context -> When to Use This Skill / Core Concepts / 1. Backtesting Biases
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}Backtesting Frameworks
Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.
When to Use This Skill
- Developing trading strategy backtests
- Building backtesting infrastructure
- Validating strategy performance
- Avoiding common backtesting biases
- Implementing walk-forward analysis
- Comparing strategy alternatives
Core Concepts
1. Backtesting Biases
| Bias | Description | Mitigation |
|---|---|---|
| Look-ahead | Using future information | Point-in-time data |
| Survivorship | Only testing on survivors | Use delisted securities |
| Overfitting | Curve-fitting to history | Out-of-sample testing |
| Selection | Cherry-picking strategies | Pre-registration |
| Transaction | Ignoring trading costs | Realistic cost models |
2. Proper Backtest Structure
Historical Data
│
▼
┌─────────────────────────────────────────┐
│ Training Set │
│ (Strategy Development & Optimization) │
└─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ Validation Set │
│ (Parameter Selection, No Peeking) │
└─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ Test Set │
│ (Final Performance Evaluation) │
└─────────────────────────────────────────┘3. Walk-Forward Analysis
Window 1: [Train──────][Test]
Window 2: [Train──────][Test]
Window 3: [Train──────][Test]
Window 4: [Train──────][Test]
─────▶ TimeDetailed worked examples and patterns
Detailed sections (starting with ## Implementation Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.
Best Practices
Do's
- Use point-in-time data - Avoid look-ahead bias
- Include transaction costs - Realistic estimates
- Test out-of-sample - Always reserve data
- Use walk-forward - Not just train/test
- Monte Carlo analysis - Understand uncertainty
Don'ts
- Don't overfit - Limit parameters
- Don't ignore survivorship - Include delisted
- Don't use adjusted data carelessly - Understand adjustments
- Don't optimize on full history - Reserve test set
- Don't ignore capacity - Market impact matters
Decide Fit First
Design Intent
How To Use It
Boundaries And Review