Python script to generate the stock time series data specifically for Hidden Markov Model example

hmm_data_prep.py

import numpy as np
import pandas as pd
import pandas_datareader.data as web
import scipy.stats as scs

import matplotlib as mpl
from matplotlib import cm
import matplotlib.pyplot as plt
from matplotlib.dates import YearLocator, MonthLocator
import seaborn as sns
import os, gc, sys
import datetime

SEED = 515

mkt = 'GE'  # stock to watch
f1 = 'TEDRATE' # ted spread (ref: https://fred.stlouisfed.org/series/TEDRATE)
f2 = 'T10Y2Y' # constant maturity ten yer - 2 year
f3 = 'T10Y3M' # constant maturity 10yr - 3m
f4 = 'BAMLHYH0A0HYM2TRIV'  # ICE BofA US High Yield Index Total Return Index Value

start = datetime.date(2010, 1, 1)
end = datetime.date.today()

mkt_df = web.DataReader([mkt], 'yahoo', start, end)['Adj Close']\
    .rename(columns={mkt: mkt})\
    .assign(sret=lambda x: np.log(x[mkt] / x[mkt].shift(1)))\
    .dropna()

data_df = web.DataReader([f1, f2, f3, f4], 'fred', start, end)\
    .join(mkt_df, how='inner')\
    .dropna()