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Portfolio risk and optimization: mean-variance, risk parity, CVaR, CDaR, worst-case, and robust optimization. Factor models, Black-Litterman, NCO. Supports plotting and interactive dashboards.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mkurman/zorai --skill riskfolio-lib命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Use when provisioning Prime Intellect GPU compute, managing pods/disks/sandboxes, running hosted RL training via prime lab, installing or publishing RL environments, or exposing local services via Prime Tunnel. Covers the `prime` CLI (PyPI: prime) for all Prime Intellect platform operations.
Shared Python package for Science Skills, currently containing http_client -- a unified HTTP client with rate limiting, retries, and exponential backoff. Not a standalone agent skill. Do not invoke directly.
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
基于 SOC 职业分类
正在显示 SKILL.md
| name | riskfolio-lib |
| description | Portfolio risk and optimization: mean-variance, risk parity, CVaR, CDaR, worst-case, and robust optimization. Factor models, Black-Litterman, NCO. Supports plotting and interactive dashboards. |
| tags | ["portfolio-optimization","risk-parity","cvar","factor-models","risk-management","quant-finance","zorai"] |
Riskfolio-Lib provides portfolio optimization beyond mean-variance: risk parity, CVaR, CDaR, worst-case, robust optimization, NCO (Network Clustering), and hierarchical methods. Includes factor models, Black-Litterman, and built-in plotting for efficient frontiers.
uv pip install riskfolio-lib
import riskfolio as rp
import yfinance as yf
prices = yf.download(["AAPL", "MSFT", "GOOGL", "AMZN", "NVDA"], start="2022-01-01")["Close"]
returns = prices.pct_change().dropna()
port = rp.Portfolio(returns=returns)
port.assets_stats(method_mu="hist", method_cov="hist")
# Max Sharpe
w = port.optimization(model="Classic", rm="MV", obj="Sharpe", hist=True)
print("Optimal weights:", w.to_dict())
# Risk parity
w_rp = port.optimization(model="Classic", rm="MV", obj="MinRisk", hist=True)