def test_get_one_fund(self):
tspdata = tsp.TSPReader(start="2015-11-2",
end="2015-11-2",
symbols=("I Fund", )).read()
assert len(tspdata) == 1
assert tspdata.columns.values.tolist() == ["I Fund"]
def fetch(self):
import pandas_datareader as pdr
import pandas_datareader.data as web
from pandas_datareader import wb
#self.prog_bar.pb_start()
self.applyOptions()
source = self.kwds['source']
dataset = self.kwds['dataset']
url = self.kwds['url']
st = self.kwds['start']
e = self.kwds['end']
dateformat = self.kwds['dateformat']
start = pd.to_datetime(st, format=dateformat, errors='ignore')
end = pd.to_datetime(e, format=dateformat, errors='ignore')
if url != '':
df = self.fetch_url(url)
if df is None:
return
label = os.path.basename(url)
self.parent.load_dataframe(df, label, True)
return
if source == 'Yahoo Finance':
df = web.DataReader("F", 'yahoo', start, end)
elif source == 'Google Finance':
df = web.DataReader("F", 'google', start, end)
elif source == 'FRED':
df = web.DataReader("GDP", "fred", start, end)
elif source == 'OECD':
df = web.DataReader('UN_DEN', 'oecd', end=end)
elif source == 'World Bank':
df = wb.download(indicator='NY.GDP.PCAP.KD',
country=['US', 'CA', 'MX'],
start=2005,
end=2008)
elif source == 'Eurostat':
df = web.DataReader("tran_sf_railac", 'eurostat')
elif source == 'TSP':
import pandas_datareader.tsp as tsp
tspreader = tsp.TSPReader(start=start, end=end)
df = tspreader.read()
elif source == 'FAMA/French':
ds = web.DataReader(dataset, "famafrench")
df = ds[0]
label = source + '_' + dataset
df = df.reset_index()
self.parent.load_dataframe(df, label, True)
#self.prog_bar.pb_stop()
return
def test_get_allfunds(self):
tspdata = tsp.TSPReader(start='2015-11-2', end='2015-11-2').read()
assert len(tspdata == 1)
assert round(tspdata['I Fund'][dt.date(2015, 11, 2)], 5) == 25.0058
def test_get_allfunds(self):
tspdata = tsp.TSPReader(start="2015-11-2", end="2015-11-2").read()
assert len(tspdata) == 1
assert round(tspdata["I Fund"][dt.datetime(2015, 11, 2)], 5) == 25.0058
end = datetime(2018, 1, 1)
gdp = web.DataReader('GDP', 'fred', start, end)
#crea la columna growth con los calculos
gdp['growth'] = gdp['GDP'] - gdp['GDP'].shift(1)
print(gdp)
#----------------------------------------------------------------------------------------------------------------
import pandas_datareader.tsp as tsp
from datetime import datetime
start = datetime(2009, 1, 1)
end = datetime(2019, 1, 1)
tsp_data = tsp.TSPReader(start,end).read()
print(tsp_data)
#prints the variance of the values
print(tsp_data.var())
#prints the covariance of the values
#describes the relationship between the returns on two different investments over a period of time,
#and can be used to help balance a portfolio.
print(tsp_data.cov())
# Python is able to import financial data from csv files as well as public financial APIs.
# The pandas read_csv function can be used to import data from a csv file into a pandas dataframe.
# Pandas-datareader makes it easy to import data from public financial APIs.
# Python’s datetime function can be used to create datetime objects which are often used to specify time ranges for financial data.
# API keys are unique identifiers required for some APIs in order to access data.
# Sometimes APIs can be flaky. To mitigate the damage this might cause it’s best to test your code often and keep up to date with the pandas-datareader documentation and GitHub page.