Financial Market Data Access in Python

Exploring the yahoofinancials Python library

The yahoofinancials Python library provides free access to the market data available from Yahoo Finance, whose provider is ICE Data Services.

It provides access to historical and, for most assets, also real-time pricing data for the following:

• Currencies
• Indexes
• Stocks
• Commodities
• ETFs
• Mutual funds
• US Treasuries
• Cryptocurrencies

pip install yahoofinancials

from yahoofinancials import YahooFinancials

Single-ticker retrieval

aapl = yf.Ticker("AAPL")

hist = aapl.get_historical_price_data('2020-01-01', '2020-12-31', 'daily')

hist = aapl.get_historical_price_data('2020-01-01', '2020-12-31', 'weekly')

hist = aapl.get_historical_price_data('2020-01-01', '2020-12-31', 'monthly'

Convert the nested JSON to a pandas’ DataFrame

import pandas as pd

hist_df = pd.DataFrame(hist['AAPL']['prices'].drop('date', axis=1).set_index('fromatted_date')

print(hist_df)

Real-time data retrieval

Get real-time stock price data:

print(aapl.get_stock_price_data())

Real-time data for free data sources is usually delayed by 10 to 30 minutes.

Retrieving a financial statement

statements = aapl.get_financial_stmts('quarterly', ['income', 'cash', 'balance'])
print(statements)

Summary data retrieval

print(aapl.get_summary_data())

Multiple-tickers retrieval

Multiple-tickers retrieval, also known as a bulk retrieval, is far more efficient and faster than single-ticker retrieval since most of the time associated with each download request is spent on establishing and closing the network connection.

Historical data retrieval

currencies = YahooFinancials(['EURCHF=X', 'USDEUR=X', 'GBPUSD=X'])
print(currencies.get_historical_price_data('2020-01-01', '2020-12-31', 'weekly')) 

List of methods:

• get_200day_moving_avg()
• get_50day_moving_avg()
• get_annual_avg_div_rate()
• get_annual_avg_div_yield()
• get_beta()
• get_book_value()
• get_cost_of_revenue()
• get_currency()
• get_current_change()
• get_current_percent_change()
• get_current_price()
• get_current_volume()
• get_daily_dividend_data(start_date, end_date)
• get_daily_high()
• get_daily_low()
• get_dividend_rate()
• get_dividend_yield()
• get_earnings_per_share()
• get_ebit()
• get_exdividend_date()
• get_financial_stmts(frequency, statement_type,
• reformat=True)
• get_five_yr_avg_div_yield()
• get_gross_profit()
• get_historical_price_data(start_date, end_date, time_
• interval)
• get_income_before_tax()
• get_income_tax_expense()
• get_interest_expense()
• get_key_statistics_data()
• get_market_cap()
• get_net_income()
• get_net_income_from_continuing_ops()
• get_num_shares_outstanding(price_type=’current’)
• get_open_price()
• get_operating_income()
• get_payout_ratio()
• get_pe_ratio()
• get_prev_close_price()
• get_price_to_sales()
• get_research_and_development()
• get_stock_earnings_data(reformat=True)
• get_stock_exchange()
• get_stock_price_data(reformat=True)
• get_stock_quote_type_data()
• get_summary_data(reformat=True)
• get_ten_day_avg_daily_volume()
• get_three_month_avg_daily_volume()
• get_total_operating_expense()
• get_total_revenue()
• get_yearly_high()
• get_yearly_low()

Exploring the pandas_datareader Python library

Some of the data sources supported are as follows:

• Yahoo Finance
• The Federal Reserve Bank of St Louis’ FRED
• IEX
• Quandl
• Kenneth French’s data library
• World Bank
• OECD
• Eurostat
• Econdb
• Nasdaq Trader symbol definitions

pip install pandas-datareader

Set up the basic data retrieval parameters:

from pandas_datareader import data
start_date = '2010-01-01'
end_date = '2020-12-31'

Access to Yahoo Finance

aapl = data.DataReader('AAPL', 'yahoo', start_date, end_date)

Access to EconDB

oilprodus = data.DataReader('ticker=OILPRODUS', 'econdb', start_date, end_date)

Access to the Federal Reserve Bank of St Louis’ FRED

import pandas as pd
pd.set_option('display.max_rows', 2)

gdp = data.DataReader('GDP', 'fred', start_date, end_date)

Cashing queries

One of the key advantages of the library is its implementation of caching the results of queries, thereby saving bandwidth, speeding up code execution, and preventing the banning of IPs due to the overuse of APIs.

import datetime
import requests_cache

session = requests_cache.CacheSession(cache_name='cache', backend='sqlite', expire_after=datetime.timedelta(days=7))

aapl_full_history = data.DataReader("AAPL", 'yahoo', datetime.datetime(1980, 1, 1), datetime.datetime(2020, 12, 31), session=session)

Access jest one data point:

aapl_full_history.loc['2013-01-07']

Exploring the Quandl data source

Quandl is one of the largest repositories of economic/financial data on the internet. Its data sources can be accessed free of charge. It also offers premium data sources, for which there is a charge.

pip install quandl

import quandl
quandl.ApiConfig.api_key = 'XXXXXXXXXX'

Download the Monthly average consumer prices in metropolitan France – Apple (1 KG); EUR data

papple = quandl.get('ODA/PAPPLE_USD')

Download Apple’s fundamental data:

aapl_fundamental_data = quandl.get_table('ZACKS/FC', ticker='AAPL')

Exploring the IEX Cloud data source

IEX Cloud is one of the commercial offerings. It offers a plan for individuals at USD 9 per month. It also offers a free plan, with a limit of 50,000 API calls per month.

pip install iexfinance

from iexfinance.refdata import get_symbols
get_symbols(output_format='pandas', token="XXXXXXXXXX")

Obtain Apple’s balance sheet

from iexfinance.stocks import Stock
aapl = Stock("aapl", token="XXXXXXXXX")
aapl.get_balance_sheet()

Get the current price

aapl.get_price()

Get the sector performance report

from iexfinance.stocks import get_sector_performance
get_sector_performance(output_format='pandas', token=token)

Get historical market data for Apple

from iexfinance.stocks import get_historical_data
get_historical_data("AAPL", start="20190101", end="20200101", output_format='pandas', token=token)

Exploring the MarketStack data source

MarketStack offers an extensive database of real-time, intra-day, and historical market data across major global stock exchanges. It offers free access for up to 1,000 monthly API requests.

While there is no official MarketStack Python library, the REST JSON API provides comfortable access to all its data in Python.

Download the adjusted close data for Apple

import requests

params = {
     'access_key': 'XXXXX'
}

api_result = requests.get('http://api.marketstack.com/v1/tickers/aapl/eod', params)

api_response = api_result.json()
print(f"Symbol = {api_response['data']['symbol']}")

for eod in api_response['data']['eod']:
    print(f"{eod['date']}:{eod['adj_close']}")

Download all tickers on the Nasdaq stock exchange:

api_result = requests.get('http://api.marketstack.com/v1/exchanges/XNAX/tickers', params)

api_response = api_result.json()

print(f"Exchange Name = {api_response['data']['name']}")

for ticker in api_response['data']['tickers']:
    print(f"{ticker['name']}:{ticker['symbol']}")