Backtesting Frameworks
Data and analytics skill, available on Zeplik
Backtesting Frameworks is a ready-to-run data and analytics skill on Zeplik. Not for general statistics (use statistical-analysis). Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
The Backtesting Frameworks skill loads automatically when your request matches it, or you can invoke it directly by typing /backtesting-frameworks in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.
What the Backtesting Frameworks skill can do
- Detect look-ahead, survivorship, and overfitting biases in backtests
- Structure train, validation, and test splits to prevent peeking
- Design walk-forward analysis windows for rolling out-of-sample tests
- Model realistic transaction costs and market impact in simulations
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Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.
How the Backtesting Frameworks skill works
/backtesting-frameworks
Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates. For computing the risk metrics themselves (VaR, Sharpe, drawdown), use risk-metrics-calculation.
When to Use
- 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
|
v
+-----------------------------------------+
| Training Set |
| (Strategy Development & Optimization) |
+-----------------------------------------+
|
v
+-----------------------------------------+
| Validation Set |
| (Parameter Selection, No Peeking) |
+-----------------------------------------+
|
v
+-----------------------------------------+
| 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]
-----> Time
Detailed 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 securities
- Don't use adjusted data carelessly -- understand adjustments
- Don't optimize on full history -- reserve a test set
- Don't ignore capacity -- market impact matters
Usage
/backtesting-frameworks $ARGUMENTS
How to use the Backtesting Frameworks skill
Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the Backtesting Frameworks skill right away.
Describe your data and analytics task
Ask in plain language, or type /backtesting-frameworks to invoke the skill directly. Zeplik recognizes the Backtesting Frameworks skill and applies its method.
Review and refine the result
Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.
Source and credit
- Author
- wshobson
- License
- MIT
Adapted from the open-source wshobson/agents project and tuned to run natively on Zeplik. View source on GitHub.
Frequently asked questions
- What is the Backtesting Frameworks skill?
- Backtesting Frameworks is a ready-to-run data and analytics skill on Zeplik. Not for general statistics (use statistical-analysis). Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
- How do I use Backtesting Frameworks on Zeplik?
- Sign in to Zeplik and ask in plain language, or type /backtesting-frameworks in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
- Which AI model does the Backtesting Frameworks skill use?
- Any model you choose. Zeplik works across every model in one chat, so the Backtesting Frameworks skill runs on your preferred model for the task.
- Where does the Backtesting Frameworks skill come from?
- The Backtesting Frameworks skill is adapted from the open-source wshobson/agents project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
- How much does the Backtesting Frameworks skill cost?
- Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.
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More on Zeplik
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