Quant Simulation Toolkit

7 runnable Monte Carlo simulation tools extracted from a viral quant article. Importance sampling, particle filters, copulas, agent-based markets, variance r...

Practical next steps before installing or running this skill: - Review the code locally: skim the eight Python files for any network, subprocess, or filesystem operations (search for imports/uses of requests, urllib, socket, subprocess, os.system, open(..., 'w'), shutil, tempfile, ftplib, paramiko, smtplib). The provided snippets show only numeric computation, but five files were omitted in the listing — inspect them too. - Check SKILL.md for hidden/control characters and remove them. The pre-scan found unicode control characters that could be used to confuse LLMs or hide content; open the file in a hex/text editor or run a sanitizer to reveal/remove non-printable characters. - Run in a sandboxed environment: create a fresh virtualenv or a disposable VM/container and install numpy/scipy there (pip install -r requirements.txt). Execute scripts only after inspection. - Least-privilege execution: run as an unprivileged user and avoid mounting sensitive directories. The scripts appear self-contained and do not need secrets; do not run them on machines containing sensitive data without review. - If you plan to use results in production or trade real money, treat this as educational prototype code: test thoroughly, validate assumptions (margins, measures, numeric stability), and consider code review by a domain expert. - Copyright/attribution note: the skill bundles material derived from a viral social-media thread. Ensure you are comfortable with any licensing or attribution implications before redistribution. If you want, I can scan the omitted files for network/subprocess calls and summarize exact lines that warrant attention.

Type: OpenClaw Skill Name: quant-sim-toolkit Version: 1.0.0 The skill bundle contains Python scripts for quantitative finance simulations, a shell script to run them, and documentation. All code uses standard libraries (`numpy`, `scipy`) for numerical computation, performs no external network calls, file system modifications beyond standard output, or execution of arbitrary commands. The `SKILL.md` and other markdown files are descriptive and do not contain any prompt injection attempts or instructions for malicious behavior. The `requirements.txt` specifies benign, widely-used dependencies. The entire package aligns with its stated purpose of providing a 'Quant Simulation Toolkit' without any high-risk behaviors.