Alpha Vantage demo code SAFE Research Data Center

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Information about the available content can be found in the documentation page ( https://www.alphavantage.co/documentation/).

Request

A request can be made using an API Key.

import requests

# Replace YOUR_API_KEY with your actual API key
api_key = 'YOUR_API_KEY'

# Specify the stock symbol for which you want to retrieve data
symbol = 'AAPL'

# Specify the type of data you want to retrieve (e.g., 'TIME_SERIES_DAILY_ADJUSTED')
function = 'TIME_SERIES_DAILY_ADJUSTED'


# Make the API request
url = f'https://www.alphavantage.co/query?function={function}&symbol={symbol}&apikey={api_key}'
response = requests.get(url)

# Print the response
print(response.json())

This can also be saved as a CSV.

from alpha_vantage.timeseries import TimeSeries
import csv

# Replace YOUR_API_KEY with your actual API key
api_key = 'YOUR_API_KEY'

# Create a TimeSeries object using your API key
ts = TimeSeries(key
=
api_key, output_format='csv')

# Specify the stock symbol for which you want to retrieve data
symbol = 'AAPL'

# Retrieve daily adjusted time series data for the symbol
data, meta_data = ts.get_daily_adjusted(symbol=symbol)

# Write the data to a CSV file
with open('stock_data.csv', 'w', newline='') as csvfile:
    writer = csv.writer(csvfile)
    writer.writerow(['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adjusted Close'])
    for row in data:
        writer.writerow(row)

or it can be stored to a Pandas data frame.

import pandas as pd
import requests

# Replace YOUR_API_KEY with your actual API key
api_key = 'YOUR_API_KEY'

# Specify the stock symbol for which you want to retrieve data
symbol = 'AAPL'

# Specify the type of data you want to retrieve (e.g., 'TIME_SERIES_DAILY_ADJUSTED')
function = 'TIME_SERIES_DAILY_ADJUSTED'

# Specify the size of the time series.
output_size = 'full'

# Make the API request
url = f'https://www.alphavantage.co/query?function={function}&symbol={symbol}&apikey={api_key}&outputsize={output_size}'
response = requests.get(url)

# Parse the response and extract the data
data = response.json()
stock_data = data['Time Series (Daily)']

# Convert the data to a pandas DataFrame
df=pd.DataFrame(stock_data).T
df.index.name = 'Date'

# Print the DataFrame
df

	1. open	2. high	3. low	4. close	5. adjusted close	6. volume	7. dividend amount	8. split coefficient
Date
2023-01-17	134.83	137.29	134.13	135.94	135.94	63646627	0.0000	1.0
2023-01-13	132.03	134.92	131.66	134.76	134.76	57809719	0.0000	1.0
2023-01-12	133.88	134.26	131.44	133.41	133.41	71379648	0.0000	1.0
2023-01-11	131.25	133.51	130.46	133.49	133.49	69458949	0.0000	1.0
2023-01-10	130.26	131.2636	128.12	130.73	130.73	63896155	0.0000	1.0
...	...	...	...	...	...	...	...	...
1999-11-05	84.62	88.37	84.0	88.31	0.671185647632425	3721500	0.0000	1.0
1999-11-04	82.06	85.37	80.62	83.62	0.635540073095045	3384700	0.0000	1.0
1999-11-03	81.62	83.25	81.0	81.5	0.619427361363862	2932700	0.0000	1.0
1999-11-02	78.0	81.69	77.31	80.25	0.609926941711042	3564600	0.0000	1.0
1999-11-01	80.0	80.69	77.37	77.62	0.589938058761509	2487300	0.0000	1.0

5840 rows × 8 columns