API basics

Definition

An application programming interface is a way for two or more computer programs to communicate with each other. It can be useful to share large Datasets effectively.

Python example

You need to retrieve the Alpha Vantage API Key you received with your subscription to replace 'YOURAPIKEY'.

Import Python packages

import requests
import pandas as pd
import json
pd.set_option('display.max_columns', 500)
# Set your Alpha Vantage API Key
AV_API_Key='YOURAPIKEY'

Search a ticker by keyword

search_keyword='meta'
url = 'https://www.alphavantage.co/query?function=SYMBOL_SEARCH&keywords={searchKeyword}&apikey={apiKey}'.format(apiKey=AV_API_Key, searchKeyword=search_keyword)
r_search = requests.get(url)
js_search = r_search.json()
df_search=pd.DataFrame(js_search)
display(df_search)

Add information based on GAFAM ticker

ticker_information=pd.DataFrame()
ticker_list=['GOOGL','AAPL', 'META', 'AMZN', 'MSFT']
for ticker in ticker_list :
    r_overview = requests.get('https://www.alphavantage.co/query?function=OVERVIEW&symbol={ticker}&apikey={apiKey}'.format(apiKey=AV_API_Key, ticker=ticker))
    js_overview=r_overview.json()
    df_overview=pd.json_normalize(js_overview)
    ticker_information=pd.concat([ticker_information, df_overview])
    display(ticker_information.head())

Get GAFAM daily stock market

ticker_time_series=pd.DataFrame()
ticker_metadata=pd.DataFrame()
ticker_list=['GOOGL','AAPL', 'META', 'AMZN', 'MSFT']
for ticker in ticker_list :
    r_stock = requests.get('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&outputsize=full&symbol={ticker}&apikey={apiKey}'.format(apiKey=AV_API_Key, ticker=ticker))
    js_stock = r_stock.json()
    # Ticker time series to DataFrame
    df_time_series=pd.DataFrame(js_stock['Time Series (Daily)'])
    df_time_series['Ticker']=ticker
    ticker_time_series=pd.concat([ticker_time_series, df_time_series])
    # Ticker metadata to DataFrame
    df_metadata=pd.json_normalize(js_stock['Meta Data'])
    df_metadata['Ticker']=ticker
    ticker_metadata=pd.concat([ticker_metadata, df_metadata])
display(ticker_time_series.head())
display(ticker_metadata.head())

Reset index and melt DataFrame

ticker_time_series=ticker_time_series.reset_index().rename({'index':'Value_type'}, axis=1)
lg_ticker_ts = ticker_time_series.melt(id_vars=['Ticker','Value_type'])*
display(lg_ticker_ts.head())

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