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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.
Passed 16 of 23 test cases with the skill, and 14 of 23 without it.
2 fixed14 passed either way7 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
In PyMC MCMC estimation for stochastic volatility models, how many parallel sampling chains are configured in the standard sampling call?
| Checked | Without skill | With skill |
|---|---|---|
| Sets the chains argument to 4 in pm.sample. | Missed | Passed |
The instructions your model reads when the skill loads.
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)
test cases passed, vs 14 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.