advanced-algorithmic-trading

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plt.xlabel(’Time’)
plt.ylabel(’Log Volatility’)
plt.show()
print("Plotting the absolute returns overlaid with vol...")
plt.plot(np.abs(np.exp(log_returns))-1.0, linewidth=0.5)
plt.plot(np.exp(trace[s][::k].T), ’r’, alpha=opacity)
plt.xlabel("Trading Days")
plt.ylabel("Absolute Returns/Volatility")
plt.show()
if __name__ == "__main__":
# State the starting and ending dates of the AMZN returns
start_date = datetime.datetime(2006, 1, 1)
end_date = datetime.datetime(2015, 12, 31)
# Obtain and plot the logarithmic returns of Amazon prices
amzn_df = obtain_plot_amazon_prices_dataframe(start_date, end_date)
log_returns = np.array(amzn_df["log_returns"])
# Configure the stochastic volatility model and carry out
# MCMC sampling using NUTS, plotting the trace
samples = 2000
configure_sample_stoch_vol_model(log_returns, samples)