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Bayesian inference for financial markets using PyMC. Stochastic volatility models, regime-switching, Bayesian portfolio optimization, factor models, and Markov chain Monte Carlo for risk estimation.
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 pymc-markets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。
基于 SOC 职业分类
正在显示 SKILL.md
| name | pymc-markets |
| description | Bayesian inference for financial markets using PyMC. Stochastic volatility models, regime-switching, Bayesian portfolio optimization, factor models, and Markov chain Monte Carlo for risk estimation. |
| tags | ["bayesian","pymc","stochastic-volatility","portfolio-optimization","risk","markets","zorai"] |
PyMC provides Bayesian inference for financial modeling: stochastic volatility, regime-switching, Bayesian portfolio optimization, factor models, and MCMC risk estimation using the NUTS sampler. ArviZ provides diagnostics and visualization.
uv pip install pymc arviz
import pymc as pm
import numpy as np
import arviz as az
# Simulated daily returns
returns = np.random.randn(500) * 0.02
with pm.Model() as sv_model:
sigma = pm.InverseGamma("sigma", alpha=2, beta=1)
log_vol = pm.GaussianRandomWalk("log_vol", sigma=sigma, shape=len(returns))
obs = pm.Normal("returns", mu=0, sigma=pm.math.exp(log_vol / 2), observed=returns)
trace = pm.sample(1000, tune=1000, chains=4)
print(az.summary(trace, var_names=["sigma"]))
az.plot_trace(trace)