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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.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/mkurman/zorai --skill pymc-markets명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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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.
SOC 직업 분류 기준
SKILL.md 표시 중
| 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)