from datetime import date
# Time period of import, start and end dates
start = date(2017, 10, 01)
end = date(2017, 11, 06)
# DataReader is a function to import, there are different sources available to import data
# such as ggogle fin, yahoo fin,fred, Oanda(for exchange rates)
# for eg Importing FB data from goolge
stockFb = DataReader('fb', 'google', start, end)
type(stockFb)
# DataReader returns a pandas data frame object
stockFb.head()
stockFb.info()
# from yahoo
stockApl = DataReader('AAPL', 'yahoo', start, end)
stockApl.head()
stockApl.info()
#plotting
stockApl['Close'].plot(title='APPLE')
plt.show()
#sp500 from fred up to now
sp500 = DataReader('SP500', 'fred', start)
#note sys date is deafult for end argument
sp500.tail()
sp500.plot(title='SP500')
stock_data = DataReader(ticker, data_source, start, end) In the first chapter, you learned that a stock ticker is the unique symbol needed to get stock information for a certain company. In this exercise, you will practice importing the 2016 data for Apple, with ticker 'AAPL'. ''' # Import DataReader from pandas_datareader.data import DataReader # Import date from datetime import date # Set start and end dates start = date(2016, 1, 1) end = date(2016, 12, 31) # Set the ticker ticker = 'AAPL' # Set the data source data_source = 'google' # Import the stock prices stock_prices = DataReader(ticker, data_source, start, end) # Display and inspect the result print(stock_prices.head()) stock_prices.info()
import pandas as pd
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
from datetime import date
start = date(1900,1,1) # default Jan 1, 2010
series_code = 'DGS10' # 10-year Treasury Rate
data_source = 'fred' # FED Economic Data Service
data = DataReader(series_code, data_source, start)
data.info()
pd.concat([data.head(3), data.tail(3)])
series_name = '10-year Treasury'
data = data.rename(columns={series_code: series_name})
data.plot(title=series_name)
plt.show()
# Set start and end dates start = date(2016, 1, 1) end = date(2016, 12, 31) # Set the ticker ticker = 'AAPL' # Set the data source data_source = 'iex' # Import the stock prices stock_prices = DataReader(ticker, data_source, start, end) # Display and inspect the result print(stock_prices.head()) stock_prices.info() ### Visualization # Import matplotlib.pyplot import matplotlib.pyplot as plt # Set start and end dates start = date(2016, 1, 1) end = date(2016, 12, 31) # Set the ticker and data_source ticker = 'FB' data_source = 'iex' # Import the data using DataReader
""" from pandas_datareader.data import DataReader from datetime import date #Date & time functionality import matplotlib.pyplot as plt import pandas as pd start = date(2015, 1, 1) end = date(2016, 12, 31) ticker = 'GOOG' data_source = 'google' stock_data = DataReader(ticker, data_source, start, end) (stock_data.info()) stock_data['Close'].plot(title=ticker) plt.show() #Economic data from the Federal Reserve, FRED series_code = 'DGS10' #10-year Treasury Rate data_source = 'fred' #FED Economic Data Service start = date(1962, 1, 1) #start date from earliest available, skip end date data = DataReader(series_code, data_source, start) data.info()
AMZN = DataReader('AMZN', 'google', start, end)
MSFT = DataReader('MSFT', 'google', start, end)
GE = DataReader('GE', 'google', start, end)
#print(AAPL)
# change the Timestamp index in the four data frames to Date
tech_list = [AAPL, GOOG, AMZN, MSFT,GE]
for stock in tech_list:
stock["Date"] = stock.index.date
for stock in tech_list:
stock.set_index("Date", drop=True, inplace=True)
#plotly.offline.plot(ff.create_table(GOOG.describe().round(2), index = True))
# General Info on Google data
print(GOOG.info())
### Change in Price of GOOGLE Stock over time
data = [go.Scatter(x=GOOG.index, y=GOOG.High)]
layout = go.Layout(title = 'GOOGLE Closing Price')
fig = go.Figure(data=data,layout=layout)
plotly.offline.plot(fig,filename='google-closing-price')
#print(GOOG)
## Adj Close column does not exist in Google Finance datasets
### To get better overview of price variations, we plot MOVING AVERAGES
# Moving Average is a constantly updated average price for a stock over a specified period
# Ex. 10-day MA presents its first data point as the avg prices from Day1-10
# Next data point is avg of prices from Day2-Day11 and so on
from pandas_datareader.data import DataReader
import pandas as pd
from datetime import date
import matplotlib.pyplot as plt
import seaborn as sns
series_code = 'WTISPLC' #Chicago Fed National Financial Conditions Index
data_source = 'fred' #FED economic data
start = date(2005, 1, 1)
data = DataReader(series_code, data_source, start)
print(data.info())
series_code2 = 'OIH' #S&P500
data_source2 = 'yahoo' #FED economic data
data2 = DataReader(series_code2, data_source2, start)
print(data2.info())
combined_df = pd.concat([data, data2], axis=1)
print(type(combined_df))
print(combined_df.info())
series_name = str(series_code) + 'vs' + str(series_code2)
#data = data.rename(columns={series_code: series_name})
#combined_df.plot(title=series_name)
#plt.figure(figsize=(12,5))
ax1 = data.iloc[:, 0].plot(color='blue', grid=True, label=series_code)
ax2 = data2['Adj Close'].plot(color='red',
grid=True,
secondary_y=True,
from datetime import date import pandas as pd import matplotlib.pyplot as plt # Set start date start = date(1968, 1, 1) # Set series code series = 'GOLDAMGBD228NLBM' data_source = 'fred' # Import the data gold_price = DataReader(series, data_source, start) # Inspect the price of gold gold_price.info() # Plot the price of gold gold_price.plot(title='Gold Price') # Show the plot plt.show() # Set the start date start = date(1950, 1, 1) # Define the series codes series = ['UNRATE', 'CIVPART'] data_source = 'fred' # Import the data
