advanced-algorithmic-trading

56
5.5 Full Code
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm
import seaborn as sns
sns.set(style="darkgrid", palette="muted")
def simulate_linear_data(N, beta_0, beta_1, eps_sigma_sq):
"""
Simulate a random dataset using a noisy
linear process.
N: Number of data points to simulate
beta_0: Intercept
beta_1: Slope of univariate predictor, X
"""
# Create a pandas DataFrame with column ’x’ containing
# N uniformly sampled values between 0.0 and 1.0
df = pd.DataFrame(
{"x":
np.random.RandomState(42).choice(
map(
lambda x: float(x)/100.0,
np.arange(N)
), N, replace=False
)
}
)
# Use a linear model (y ~ beta_0 + beta_1*x + epsilon) to
# generate a column ’y’ of responses based on ’x’
eps_mean = 0.0
df["y"] = beta_0 + beta_1*df["x"] + np.random.RandomState(42).normal(
eps_mean, eps_sigma_sq, N
)
return df
def glm_mcmc_inference(df, iterations=5000):