Guide · Agent skill
How to use the Backtesting Frameworks agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The backtesting-frameworks skill is a set of instructions that teaches an AI agent to design and review historical tests of trading strategies, the kind that check how an idea would have performed on past market data. It is written for quant developers, traders and students who want their backtests to avoid common errors such as look-ahead bias, survivorship bias and ignored transaction costs. After this guide you will know what the skill changes about the agent's behaviour, how to install it, and how to ask for it with natural requests.
Backtesting Frameworkswshobson/agents · MIT Download the skill Quick start
- 1Download the ZIP. Get the backtesting-frameworks ZIP file from this page.
- 2Unzip into a skills folder. Extract it into ~/.claude/skills/ for personal use, or into a project's .claude/skills/ folder.
- 3Or upload in the Claude apps. Go to Settings → Capabilities → Skills and upload the skill there.
- 4Ask in plain language. Request a backtest, a bias review or an out-of-sample check using everyday wording.
- 5Review the output. Check the data handling, splits and cost assumptions before trusting any result.
What does the skill make the agent do?
Once installed, the agent follows a more careful approach when you ask it to build or check a backtest. It uses only information that was available at each point in time, splits historical data into training, validation and test sets, and uses walk-forward analysis rather than a single train/test split. It also includes trading costs in the simulation.
The skill is guidance and structure, not a ready-made trading tool. It describes principles and patterns; it does not place trades, connect to brokers or markets, or tell you whether a strategy will make money.
- Point-in-time data to avoid look-ahead bias
- Delisted securities included to avoid survivorship bias
- Training, validation and test sets, with the test set reserved for final evaluation
- Walk-forward windows that repeatedly train on one period and test on the next
- Realistic transaction cost models
- Monte Carlo analysis to understand uncertainty
How do you install the backtesting-frameworks skill?
Download the ZIP file for the skill from this page. Unzip it into your personal skills folder, ~/.claude/skills/, so it is available across your projects. To limit it to one project, unzip it into that project's .claude/skills/ folder instead.
If you use the Claude apps, you can instead upload the skill under Settings → Capabilities → Skills.
- Personal install: ~/.claude/skills/
- Project install: the .claude/skills/ folder inside your project
- Claude apps: Settings → Capabilities → Skills, then upload
How do you trigger the skill with a natural request?
You do not need a special command. The skill's description covers building backtesting systems, validating strategies and building backtesting infrastructure, so ask for those things in plain language and the agent can use it. Mention the concern you want handled, such as bias, out-of-sample results or costs, to make the request specific.
- Build a backtest for a moving-average crossover strategy with walk-forward analysis.
- Review my backtest code for look-ahead bias and survivorship bias.
- Check whether these strategy results hold up out of sample.
- Add realistic transaction costs to my backtesting engine.
What do the core concepts mean?
The skill organises its guidance around three ideas. The first is a table of biases: look-ahead bias (using future information), survivorship bias (testing only on securities that survived), overfitting (curve-fitting to history), selection bias (cherry-picking strategies) and transaction bias (ignoring trading costs). Each has a suggested mitigation, such as point-in-time data or out-of-sample testing.
The second is the proper backtest structure: historical data flows into a training set for development, then a validation set for parameter selection, then a test set for final evaluation. The third is walk-forward analysis, where each window trains on earlier data and tests on the following period.
Window 1: [Train──────][Test]
Window 2: [Train──────][Test]
Window 3: [Train──────][Test]
Window 4: [Train──────][Test]
─────▶ TimeWhere can you find the detailed patterns?
The main instructions point to a second file, references/details.md, which contains detailed worked examples and implementation patterns, including an event-driven backtester structure with order, fill and position types. The agent reads this file when the summary is not enough. The skill's own files are guidance; check any code you adopt before relying on it.
What does the skill need, and is it safe to use?
The skill lists no requirements, does not run scripts and does not use the network. It consists of markdown instruction files. Because it contains no scripts, installing it does not execute code on your machine. Any code the agent writes while using it still runs under your own control, so review it before running it against real data or accounts.
- Requirements: none listed
- Runs scripts: no
- Uses network: no
- Files: SKILL.md and references/details.md
Tips for getting better backtests
Ask the agent to state which data was available at each decision point, and to show where the test set was kept separate. Limit the number of parameters you tune, because overfitting is one of the most common causes of disappointing live results. Reserve test data before you start optimising.
- Reserve the test set before optimising anything
- Keep parameters few and justified
- Include delisted securities where your data allows
- Ask for capacity and market impact to be considered
What people use it for
- Building a walk-forward backtest for a quant strategy
- Reviewing an existing backtest pipeline for look-ahead and survivorship bias
- Checking whether a strategy's results hold up on a held-out test period
- Teaching students why backtests often overstate performance
Frequently asked questions
Is the backtesting-frameworks skill free?
The skill is licensed under MIT, as listed on its source page. The source does not list pricing, so check the page for current terms.
What does the backtesting-frameworks skill require?
The skill lists no requirements, runs no scripts and uses no network access. It is made of markdown instruction files.
Where do I install the backtesting skill?
Unzip the downloaded ZIP into ~/.claude/skills/ for personal use or a project's .claude/skills/ folder. In the Claude apps, upload it under Settings → Capabilities → Skills.
Can this skill tell me whether a trading strategy will make money?
No. The skill describes testing methods and does not judge whether any strategy will be profitable. It is not investment advice.
Does the skill connect to my broker for live trading?
No. It covers historical simulation only and does not connect to brokers or markets.
What is the difference between walk-forward analysis and a simple train/test split?
A simple split tests once on one period. The skill's walk-forward approach repeats the process across several windows, training on earlier data and testing on the following period each time.
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Our verdict
Use this skill if you build or review backtesting pipelines, or want to check whether a strategy's results hold up out of sample. Pick something else if you need a finished backtesting library, live trading execution or investment advice, since this skill only provides testing principles and patterns.
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