Most finance data is in the format of time-series, see the following several examples. The first one shows how to download historical, daily stock price data from Yahoo!Finance for a given ticker's beginning and ending dates:
from matplotlib.finance import quotes_historical_yahoo_ochl as getData x = getData("IBM",(2016,1,1),(2016,1,21),asobject=True, adjusted=True) print(x[0:4])
The output is shown here:
The type of the data is numpy.recarray
as the type(x)
would show. The second example prints the first several observations from two datasets called ffMonthly.pkl
and usGDPquarterly.pkl
, and both are available from the author's website, such as http://canisius.edu/~yany/python/ffMonthly.pkl:
import pandas as pd GDP=pd.read_pickle("c:/temp/usGDPquarterly.pkl") ff=pd.read_pickle("c:/temp/ffMonthly.pkl") print(GDP.head()) print(ff.head())
The related output is shown here:
There is one end of chapter problem which is designed to merge discrete data with the daily...