mkurman Overview
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.
Installation
uv pip install pymc arviz
Stochastic Volatility Model
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)