Alphavantage

Alpha Vantage provides enterprise-grade financial market data through a set of powerful and developer-friendly APIs. To set up this environment you will need to have an API key, it can be straightly taken from the documentation here.

Note

Refer to Alphavantage Jupyter Notebook for more details.

Table of Contents

• Installation

• Usage

• Symbol Search

• Historical Price and Volume for 1 Stock

• Adding Time Periods

• Frequency Setting

• Stock Splits and Dividends

• Foreign Exchange

• Cryptocurrencies

• Mutual Funds

• Treasury Rates

• Stock Fundamentals

• Financials

• Stream Realtime Data

• Economic Indicators

• Technical Indicators

• Sector Performance

Installation

Note

Before working with this API, you will need to obtain a key from AlphaVantage’s Website

To install the package use:

pip install alpha_vantage

Or install with pandas support

pip install alpha_vantage pandas

Or install from the source

git clone https://github.com/RomelTorres/alpha_vantage.git
pip install -e alpha_vantage

Usage

Import all necessary libraries:

from alpha_vantage.timeseries import TimeSeries
import pandas as pd
import time
import requests
from io import BytesIO

key = 'insert your unique key here'

Symbol Search

For checking to see if the equity, commodity, mutual fund, etc. you want is available on Alphavantage:

Note

This example, and the following, also demonstrate how to convert an Alphavantage dictionary into a Pandas DataFrame for easier data analysis.

symbol_to_search = 'TSLA'
url = 'https://www.alphavantage.co/query?function=SYMBOL_SEARCH&keywords='+symbol_to_search+'&apikey={key}'
r = requests.get(url)
data = r.json()
data = pd.DataFrame(data['bestMatches'])

Historical Price and Volume for 1 Stock

Note

See the data dictionary for adjustments to time frame. Daily, weekly, and monthly time frames are available for equities.

data = {
    "function": "TIME_SERIES_DAILY", # WEEKLY, MONTHLY possible
    "symbol": "TSLA",
    "apikey": key
}
r = requests.get(url, params=data)
data = r.json()
data = pd.DataFrame(data['Time Series (Daily)']).T

Adding Time Periods

Shown below are the adjusted dictionaries for weekly and monthly time frames.

weekly = {
    "function": "DIGITAL_CURRENCY_WEEKLY",
    "symbol": "ETH",
    "market": 'CNY',
    "apikey": key
}

monthly = {
    "function": "DIGITAL_CURRENCY_MONTHLY",
    "symbol": "ETH",
    "market": 'CNY',
    "apikey": key
}

Frequency Setting

Outputs a similar Pandas DataFrame that breaks the OHLCV down into 1 minute intervals.

ticker = 'TSLA'
interval = '1min'
api_key = key

api_url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={ticker}&interval={interval}&apikey={api_key}'
raw_df = requests.get(api_url).json()
df = pd.DataFrame(raw_df[f'Time Series ({interval})']).T
df = df.rename(columns = {'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'close', '5. volume': 'volume'})
for i in df.columns:
    df[i] = df[i].astype(float)
df.index = pd.to_datetime(df.index)
df = df.iloc[::-1]
df.tail()

Stock Splits and Dividends

Outputs a Pandas DataFrame with the DPS, Yield, Dividend Date and ExDate for the given ticker.

ticker = "IBM"
url = 'https://www.alphavantage.co/query?function=OVERVIEW&symbol='+ticker+'&apikey={key}'
r = requests.get(url)
data = r.json()
dividends = pd.DataFrame(data, index = ['Values'])
dividends = dividends[['DividendPerShare', 'DividendYield', 'DividendDate', 'ExDividendDate']].T

Financial Indices

Note

This feature requires a premium subscription.

index = "DJI" # FCHI, IXIC, ...
url = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol='+index+'&outputsize=full&apikey={key}'
r = requests.get(url)
data = r.json()

Foreign Exchange

Outputs a dictionary with the exchange rate’s OHLC values on the given time interval.

# Currency list: https://www.alphavantage.co/physical_currency_list/
currency_a = 'EUR'
currency_b = 'USD'
interval = '5min' # 1min, 5min, 15min, 30min, 60min
url = 'https://www.alphavantage.co/query?function=FX_INTRADAY&from_symbol=EUR&to_symbol=USD&interval=5min&apikey=demo'
r = requests.get(url)
data = r.json()

Alternatively, you can use the ForeignExchange library.

from alpha_vantage.foreignexchange import ForeignExchange
from pprint import pprint
cc = ForeignExchange(key='YOUR_API_KEY')
# There is no metadata in this call
data, _ = cc.get_currency_exchange_rate(from_currency='BTC',to_currency='USD')
pprint(data)

Cryptocurrencies

There are multiple ways to view data on cryptocurrencies.

The first is using Alphavantage’s API request which returns the OHLCV for the given crypto:

ticker = 'ETH'
url = 'https://www.alphavantage.co/query?function=CRYPTO_INTRADAY&symbol='+ticker+'&market=USD&interval=5min&apikey={key}'
r = requests.get(url)
data = r.json()

Another way is to import the CryptoCurrencies library, which allows for easy plotting:

from alpha_vantage.cryptocurrencies import CryptoCurrencies
import matplotlib.pyplot as plt

cc = CryptoCurrencies(key='YOUR_API_KEY', output_format='pandas')
data, meta_data = cc.get_digital_currency_daily(symbol='BTC', market='CNY')
data['4b. close (USD)'].plot()
plt.tight_layout()
plt.title('Daily close value for bitcoin (BTC)')
plt.grid()
plt.show()

Lastly, we can view the excahnge rates for cryptos:

data = {
"function": "CURRENCY_EXCHANGE_RATE", # WEEKLY, MONTHLY possible
"from_currency": "ETH",
"to_currency": 'USD',
"apikey": key
}
r = requests.get(url, params=data)
data = r.json()

Mutual Funds

Outputs a dictionary of the OHLCV values for the given mutual fund.

ticker = 'OMOIX'
url = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol='+ticker+'&apikey={key}'
r = requests.get(url)
data = r.json()

Treasury Rates

Outputs a dictionary of the daily, weekly, or monthly treasury rate.

treasury_yield = {
    "function": "TREASURY_YIELD",
    "interval": "weekly", # daily, monthly
    "maturity": "3month", # OPTIONAL 5year, 10year, 30year
    "apikey": key
}
r = requests.get(url, params=treasury_yield)
data = r.json()

Stock Fundamentals

Outputs a dictionary of various stock data, including: AssetType, Description, Sector, Address, Market Cap, EBITDA, PE, EPS, RPS, Profit Margin, Moving Averages, Revenue, and Beta.

ticker = "IBM"
url = 'https://www.alphavantage.co/query?function=OVERVIEW&symbol='+ticker+'&apikey={key}'
r = requests.get(url)
data = r.json()

Financials

Outputs a dictionary containing the information for a company’s balance sheet, cash flows, or income statement.

document = 'INCOME_STATEMENT' # BALANCE_SHEET, CASH_FLOW
url = 'https://www.alphavantage.co/query?function='+document+'&symbol=IBM&apikey=demo'
r = requests.get(url)
data = r.json()

Stream Realtime Data

Each invocation of the below function will produce the most up-to-date data on the given symbol.

def get_live_updates(symbol):
    api_key = key
    api_url = f'https://www.alphavantage.co/query?function=GLOBAL_QUOTE&symbol={symbol}&apikey={api_key}'
    raw_df = requests.get(api_url).json()
    attributes = {'attributes':['symbol', 'open', 'high', 'low', 'price', 'volume', 'latest trading day', 'previous close', 'change', 'change percent']}
    attributes_df = pd.DataFrame(attributes)
    values = []
    for i in list(raw_df['Global Quote']):
        values.append(raw_df['Global Quote'][i])
    values_dict = {'values':values}
    values_df = pd.DataFrame(values).rename(columns = {0:'values'})
    frames = [attributes_df, values_df]
    df = pd.concat(frames, axis = 1, join = 'inner').set_index('attributes')
    return df

ibm_updates = get_live_updates('IBM')
ibm_updates

Economic Indicators

Below are a few dictionaries that contain different economic indicators that can be plugged into the JSON request at the very bottom.

gdp = {
    "function": "REAL_GDP",
    "interval": "annual", # quarterly
    "apikey": key
}
treasury_yield = {
    "function": "TREASURY_YIELD",
    "interval": "weekly", # daily, monthly
    "maturity": "3month", # OPTIONAL 5year, 10year, 30year
    "apikey": key
}
federal_funds_rate = {
    "function": "FEDERAL_FUNDS_RATE",
    "interval": "weekly", # daily, monthly
    "apikey": key
}
cpi = {
    "function": "CPI",
    "interval": "weekly", # daily, monthly
    "apikey": key
}
inflation = {
    "function": "INFLATION",
    "interval": "weekly", # daily, monthly
    "apikey": key
}
consumer_sentiment = {
    "function": "CONSUMER_SENTIMENT",
    "apikey": key
}
unemployment = {
    "function": "UNEMPLOYMENT",
    "apikey": key
}

Below is the aforementioned JSON request, where you will replace the params variable.

r = requests.get(url, params=unemployment) # REPLACE 'params' with desired dict
data = r.json()
df = pd.DataFrame(data['data'])
df = crypto_df.set_index("date")

Technical Indicators

Below is the JSON request approach to getting data on various technical indicators.

popular_ti = {
    "function": "ADX", # REPLACE: EMA, RSI, ADX, SMA
    "symbol": "IBM",
    "interval": "weekly",
    "time_period": "10",
    "series_type": "open",
    "apikey": key
}

r = requests.get(url, params=popular_ti)
data = r.json()

Alternatively, you can use the TechIndicators library to achieve similar results.

from alpha_vantage.techindicators import TechIndicators
import matplotlib.pyplot as plt

ti = TechIndicators(key='YOUR_API_KEY', output_format='pandas')
data, meta_data = ti.get_bbands(symbol='MSFT', interval='60min', time_period=60)
data.plot()
plt.title('BBbands indicator for  MSFT stock (60 min)')
plt.show()

Sector Performance

Lastly, Alphavantage allows you to use the SectorPerformances library to view the realtime performance, by sector:

from alpha_vantage.sectorperformance import SectorPerformances
import matplotlib.pyplot as plt

sp = SectorPerformances(key='YOUR_API_KEY', output_format='pandas')
data, meta_data = sp.get_sector()
data['Rank A: Real-Time Performance'].plot(kind='bar')
plt.title('Real Time Performance (%) per Sector')
plt.tight_layout()
plt.grid()
plt.show()