advanced-algorithmic-trading

68
"""
Download, calculate and plot the AMZN logarithmic returns.
"""
print("Downloading and plotting AMZN log returns...")
amzn = pdr.get_data_yahoo("AMZN", start_date, end_date)
amzn["returns"] = amzn["Adj Close"]/amzn["Adj Close"].shift(1)
amzn.dropna(inplace=True)
amzn["log_returns"] = np.log(amzn["returns"])
amzn["log_returns"].plot(linewidth=0.5)
plt.ylabel("AMZN daily percentage returns")
plt.show()
return amzn
def configure_sample_stoch_vol_model(log_returns, samples):
"""
Configure the stochastic volatility model using PyMC3
in a ’with’ context. Then sample from the model using
the No-U-Turn-Sampler (NUTS).
Plot the logarithmic volatility process and then the
absolute returns overlaid with the estimated vol.
"""
print("Configuring stochastic volatility with PyMC3...")
model = pm.Model()
with model:
sigma = pm.Exponential(’sigma’, 50.0, testval=0.1)
nu = pm.Exponential(’nu’, 0.1)
s = GaussianRandomWalk(’s’, sigma**-2, shape=len(log_returns))
logrets = pm.StudentT(
’logrets’, nu,
lam=pm.math.exp(-2.0*s),
observed=log_returns
)
print("Fitting the stochastic volatility model...")
with model:
trace = pm.sample(samples)
pm.traceplot(trace, model.vars[:-1])
plt.show()
print("Plotting the log volatility...")
k = 10
opacity = 0.03
plt.plot(trace[s][::k].T, ’b’, alpha=opacity)