Backtesting Frameworks
v1.0.0Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developin...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
The name, description, and SKILL.md all focus on building backtesting systems and the included Python examples match that purpose. Minor metadata mismatches: _meta.json lists Python package requirements (pandas, numpy, backtrader) and a different slug/displayName, but the skill has no install spec — this is a small packaging/information inconsistency, not evidence of malicious intent.
Instruction Scope
SKILL.md contains conceptual guidance and concrete Python implementation patterns (event-driven backtester, execution model, etc.). It does not instruct the agent to read unrelated system files, access credentials, or POST data to external endpoints. The provided code samples operate on in-memory data structures and expected market data inputs.
Install Mechanism
There is no install spec and no code files to execute on install. That minimizes risk — nothing is downloaded or written by an installer. Note: _meta.json lists Python package dependencies, but no automated install step is provided.
Credentials
The skill requests no environment variables, no credentials, and no config paths. This is proportional to the stated purpose of providing backtesting guidance and code examples.
Persistence & Privilege
The skill is not marked always:true and does not request system persistence or modify other skills. Default autonomous invocation is allowed by platform policy but is not combined with other risky behaviors here.
Assessment
This skill appears coherent and instruction-only: it provides design guidance and Python examples for backtesting and does not request credentials or install anything automatically. Consider these precautions before using: (1) review and run the example code in an isolated environment (e.g., a virtualenv or container) before using real funds or sensitive data; (2) install any dependencies (pandas, numpy, backtrader) from trusted sources (PyPI) yourself rather than running unverified install commands; (3) note the small metadata inconsistencies (different slug/displayName and listed requirements with no install script) — they look like packaging sloppiness, not malicious behavior; (4) if you plan to connect the backtester to live market data or brokerage APIs, expect to supply API keys — review those integration steps carefully and avoid sharing credentials with untrusted components.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.