IEX_data_collect.py

#import packages for use later in the HMM code

import pandas as pd
import sklearn.mixture as mix

import numpy as np
import scipy.stats as scs

import datetime as dt

import matplotlib as mpl
from matplotlib import cm
import matplotlib.pyplot as plt
from matplotlib.dates import YearLocator, MonthLocator
%matplotlib inline

import seaborn as sns

from iex import Stock

ticker = ["SPY"]
all_historic_data = pd.DataFrame()

for t in ticker:
    ticker_data = Stock(t).chart_table(range="5y")
    
    ticker_data_clean = ticker_data[["date", "close", "high", "low"]]
    ticker_data_clean["date"] = pd.to_datetime(ticker_data_clean["date"])
    ticker_data_clean.insert(1, "ticker", t)
    ticker_data_clean["return"] = ticker_data_clean["close"].pct_change()
    
    ticker_data_clean["range"] = (ticker_data_clean["high"]/ticker_data_clean["low"])-1
    del ticker_data_clean["high"]
    del ticker_data_clean["low"]
    ticker_data_clean.dropna(how="any", inplace=True)

    
    all_historic_data = pd.concat([all_historic_data, ticker_data_clean])

all_historic_data.head()