AlphaVantageAPI

An Opinionated AlphaVantage API Wrapper in Python 3.9

Description

This API has been designed to simplify the process of aquiring financial data from AlphaVantage and only depends on the requests and Pandas packages. This package can clean and export data into a variety of file formats such as: csv (default), json, pkl, html, and txt with assistance of Pandas. If the openpyxl package is also installed, it can also be saved as an Excel file format: xlsx.

AlphaVantage Support and Contact

Email: [email protected]
Slack
Twitter: @alpha_vantage
Medium-AlphaVantage
Community events: https://alphavhack.devpost.com/

Contributing

Contributions are welcome and I am open to new ideas or implementations. Thank you!

Sciguymjm

Features

Access to AlphaVantage requires an API key. API Keys are free.
Built with Pandas for a simpler ETL pipeline.
Available export formats: csv (default), json, pkl, html, txt, and xlsx (if openpyxl package is installed)
Extended the Pandas DataFrame with extension 'av'. See the Extension Example below.
Like terse commands? The 'av' extension also includes their alias. For example: df.av.daily_adjusted('aapl') = df.av.DA('aapl'). See help('aliases') for more shorter length commands.
Simplifies column names i.e. "1. open" -> "open" when clean=True.
A help method to reduce looking up 'required' and 'optional' parameters for each function.
A call_history method to return all successful API calls.

What's New?

!!! Updated requirements.txt to fix urllib3 security vulnerability. !!!

Added Intraday Extended
There are some breaking changes! Please see the Classic Example and the DataFrame Extension Example below.
AlphaVantage has added Fundamental Data: Company Overview, Balance Sheet, Cash Flow, Income Statement, Earnings Calendar, IPO Calendar and Listing Status.

What's Gone?

AlphaVantage has depreciated batch, digital_intraday, and sectors.

Installation

Stable

The pip version is the last most stable release. Version: 1.0.30

$ pip install alphaVantage-api

Latest Version

Best choice! Version: 1.0.33

$ pip install -U git+https://github.com/twopirllc/AlphaVantageAPI

Excel Export

$ pip install openpyxl

Programming Conventions

There are two ways of utilizing AlphaVantageAPI. The Standard Class way or as a Pandas DataFrame Extension 'av'.

Standard

This is the Object/Class way. Instantiate a Class with predefined parameters and then make calls.

Parameter Defaults

api_key: str     = None
premium: bool    = False
output_size: str = 'compact'
datatype: str    = 'json'
export: bool     = False
export_path: str = '~/av_data'
output: str      = 'csv'
clean: bool      = False
proxy: dict      = {}

API Parameter Descriptions

api_key

If None, then do not forget to set your environment variable AV_API_KEY to API key. Otherwise set it in the class constructor. If you have a Premium API key, do not forget to set the premium property to True as well.

premium

*Got premium? Excellent! You do not need to wait 15.0001 seconds between each API call.

output_size

The other option is 'full'. See AlphaVantage API documentation for more details.

datatype

The preferred request type. See AlphaVantage API documentation for more details.

export

*Set it to True if you want to save the file locally according to the export_path property.

export_path

The path of where you want to save the data.

output

How to save/export the data locally. Other options are 'json', 'pkl', 'html', and 'txt'. If openpyxl is installed, then you can save as 'xlsz'.

clean

Simplifies the column header names for instance: "1. open" -> "open".

proxy

See requests API documentation for more details.

Example: Class(ic) Behavior

Initialization

from alphaVantageAPI import AlphaVantage

# Initialize the AlphaVantage Class with default values
av = AlphaVantage(
        api_key=None,
        premium=False,
        output_size="compact",
        datatype='json',
        export=False,
        export_path="~/av_data",
        output="csv",
        clean=False,
        proxy={}
    )

Help!

# Help: lists all the functions and indicators AlphaVantage API supports
av.help()

# Print 'function' aliases
av.help("aliases")

# Help with a specific API function
av.help("TIME_SERIES_DAILY")

# Help with an indicator
av.help("BBANDS")

Class Usage

import pandas as pd
import alphaVantageAPI as AV

# Initialize, clean and save "full" locally in "csv" to "~/av_data"
av = AV.AlphaVantage(api_key="demo", export=True, clean=True, output_size="full")

# Check Settings
print(av)

# Parameters
query = "AA"
ticker, crypto = "MSFT", "BTC"
base_fx, to_fx = "USD", "CAD"

# Symbol Search
found_symbols = av.search(query)

# Global Quote
quote_df = av.quote(ticker)


# Earnings Calendar
# The "horizon" is how many months out to look for Earnings. Options include:
#   "3month", "6month", "12month". Default: "3month"
# Optionally can use "symbol".
#   Default: Returns all symbols with Earnings based on the "horizon".

 # All symbols with Earnings in 3 months
earnings_df = av.earnings()
# All symbols with Earnings in 6 months
earnings6mo_df = av.earnings(horizon="6month")

# Earnings Calendar in 3 months for 'symbol'
earnings_ticker_df = av.earnings(symbol=ticker)
# Earnings Calendar in 6 months for 'symbol'
earnings6mo_ticker_df = av.earnings(symbol=ticker, horizon="6month")

# IPO Calendar
ipos_df = av.ipos()


# Listing Status
# Optionally can use "state". Options: "active" or "delisted".
#   Default: "active".
# Optionally can use "date". Options: None or "YYYY-MM-DD".
#   Default: None
listed_df = av.listed()
listedon_df = av.listed(date="2013-08-03")

delisted_df = av.listed(state="delisted")
delistedon_df = av.listed(state="delisted", date="2009-02-14")


# Company Overview
overview_df = av.overview(ticker)

# Balance Sheet, Income Statement and Cash Flow each return a list or a dict (if
# as_dict=True). Default: List with two DataFrames: [Quarterlydf, Annuallydf]
balance_list = av.balance(ticker)   # Balance Sheet
QuarterlyBSdf = balance_list[0]
AnnuallyBSdf = balance_list[1]

cashflow_list = av.cashflow(ticker) # Cash Flow
income_list = av.income(ticker)     # Income Statement


# FX / Currency
fx_rate_df = av.fxrate(from_symbol=base_fx, to_symbol=to_fx) # Rate
fx_I5_df = av.fx(from_symbol=base_fx, to_symbol=to_fx, function="FXI", interval=5) # Intraday as int
fx_I60_df = av.fx(from_symbol=base_fx, to_symbol=to_fx, function="FXI", interval="60min") # Intraday as str
fx_D_df = av.fx(from_symbol=base_fx, to_symbol=to_fx, function="FXD") # Daily
fx_W_df = av.fx(from_symbol=base_fx, to_symbol=to_fx, function="FXW") # Weekly
fx_M_df = av.fx(from_symbol=base_fx, to_symbol=to_fx, function="FXM") # Monthly

# Crypto Rating
btc_rating_df = av.crypto_rating(symbol=crypto) # == ds.crypto_rating(crypto)

# Digital/Crypto
btc_usd_D_df = av.digital(symbol=crypto, market=base_fx, function="CD") # Daily
btc_usd_W_df = av.digital(symbol=crypto, market=base_fx, function="CW") # Weekly
btc_usd_M_df = av.digital(symbol=crypto, market=base_fx, function="CM") # Monthly

# Generic Equity/ETF calls
ticker_I5_df = av.intraday(symbol=ticker, interval=5) # Intraday as int
ticker_I60_df = av.intraday(symbol=ticker, interval="60min") # Intraday as str
ticker_IE5_df = av.intraday_extended(symbol=ticker, interval=5) # Adjusted Intraday Extended. Default slice: "year1month1"
ticker_IE30_df = av.intraday_extended(symbol=ticker, interval="30min", slice="year2month6") # Adjusted Intraday Extended with slice
ticker_IE60_R_df = av.intraday_extended(symbol=ticker, interval=60, slice="year2month1", adjusted=False) # Raw Intraday Extended with slice
ticker_D_df = av.data(symbol=ticker, function="D") # Daily
ticker_DA_df = av.data(symbol=ticker, function="DA") # Daily Adjusted
ticker_W_df = av.data(symbol=ticker, function="W") # Weekly
ticker_WA_df = av.data(symbol=ticker, function="WA") # Weekly Adjusted
ticker_M_df = av.data(symbol=ticker, function="M") # Monthly
ticker_MA_df = av.data(symbol=ticker, function="MA") # Monthly Adjusted


## Indicators
# SMA(close, 20)
ticker_SMA_20_df = av.data(symbol=ticker, function="SMA", series_type="close", time_period=20)

# STOCH(close)
ticker_STOCH_df = av.data("STOCH", symbols[2], interval="daily", series_type="close")

# List of symbols Daily
symbols = ["AAPL", "MSFT", "XLK"]
# returns dict of DataFrames: {"ABC": pd.DataFrame(), ..
techs = av.data(symbols, "D").}
[print(techs[s]) for s in symbols]

# History of Successful Calls to AlphaVantage
history = pd.DataFrame(av.call_history())
print(history)

Call History

# Returns all successfull calls to the API
history_list = av.call_history()

# Pretty display of Call History
history_df = pd.DataFrame(history_list)[["symbol", "function", "interval", "time_period"]]
print(history_df)

Example: DataFrame Extension 'av' Behavior

Initialization

For simplicity and protection of your AV API key, the extension uses the environment variable AV_API_KEY upon import of the module.

import pandas as pd
import alphaVantageAPI

Empty DataFrame: No current data, no problem!

Since 'av' is an extension of a Pandas DataFrame, we need a DataFrame to work from. Simply create an empty DataFrame, it's contents will be replaced anyhow.

df = pd.DataFrame()

Setting properties

Same as the Class properties above. Use the 'av' extension to change them prior to requesting data or adjusting on the fly

# Your API KEY. Default: None
df.av.api_key = "DEMO"

# Whether you have a Premium Account. Default: False
df.av.premium = False

# Simplify OHLCV columns. Default: False
df.av.clean = True

# Whether to export (save locally). Default: False
df.av.export = True

# Output Size: "compact" or "full". Default: "compact"
df.av.output_size = "full"

# Retrieval Format. Default: "json"
df.av.datatype = "json"

# Export Path to save output format. Default: "~/av_data"
df.av.export_path = "~/av_data"

# Final output format. Default: "csv"
df.av.output = "csv"

# Proxy to use. Default: {}
df.av.proxy = {}

Help!

# Help: lists all the functions and indicators AlphaVantage API supports
df.av.help()

# Print 'function' aliases
df.av.help("aliases")

# Help with a specific API function
df.av.help("TIME_SERIES_DAILY")

# Help with an indicator
df.av.help("BBANDS")

Data Acquisition Methods

import pandas as pd
import alphaVantageAPI

# Create an Empty DataFrame to store the results
df = pd.DataFrame()

# Apply AV settings to: Clean and save "full" locally in "csv" to "~/av_data"
df.av.api_key = "DEMO"
df.av.export = True
df.av.clean = True
df.av.output_size="full"

# Parameters
query = "AA"
ticker, crypto = "MSFT", "BTC"
base_fx, to_fx = "USD", "CAD"

found_symbols = df.av.search(query)

# Global Quote
quote_df = df.av.Q(ticker) # => df.av.quote(ticker)
quote_df.av.name # returns "MSFT"

# Company Information
ticker_overview = df.av.overview(ticker)

# Earnings Calendar
# The "horizon" is how many months out to look for Earnings. Options include:
#   "3month", "6month", "12month". Default: "3month"
# Optionally can use "symbol".
#   Default: Returns all symbols with Earnings based on the "horizon".
earnings_df = df.av.earnings()
earnings6month_df = df.av.earnings(horizon="6month")
earnings12month_ticker_df = df.av.earnings(symbol=ticker, horizon="12month")

# IPO Calendar
ipos_df = df.av.ipos()


# Listing Status
# Optionally can use "state". Options: "active" or "delisted".
#   Default: "active".
# Optionally can use "date". Options: None or "YYYY-MM-DD".
#   Default: None
listed_df = df.av.listed()
listedon_df = df.av.listed(date="2013-08-03")

delisted_df = df.av.listed(state="delisted")
delistedon_df = df.av.listed(state="delisted", date="2009-02-14")


# Balance Sheet, Cash Flow, and the Income Statement each return two DataFrames
ticker_quarterly_balance, ticker_annual_balance = df.av.balance(ticker)
ticker_quarterly_cashflow, ticker_annual_cashflow = df.av.cashflow(ticker)
ticker_quarterly_income, ticker_annual_income = df.av.income(ticker)

# FX / Currency
fx_I5_df = df.av.fx_intraday(base_fx, to_currency=to_fx, interval=5)        # Intraday as int
fx_I60_df = df.av.fx_intraday(base_fx, to_currency=to_fx, interval="60min") # Intraday as str
fx_D_df = df.av.fx_daily(base_fx, to_currency=to_fx)   # Daily
fx_W_df = df.av.fx_weekly(base_fx, to_currency=to_fx)  # Weekly
fx_M_df = df.av.fx_monthly(base_fx, to_currency=to_fx) # Monthly

# Crypto Rating
btc_rating_df = df.av.crypto_rating(crypto)
btc_rating_df.av.name # returns "BTC"

# Digital/Crypto
btc_usd_D_df = df.av.digital_daily(crypto, market=base_fx)   # Daily
btc_usd_D_df.av.name # returns "BTC.USD"
btc_usd_W_df = df.av.digital_weekly(crypto, market=base_fx)  # Weekly
btc_usd_M_df = df.av.digital_monthly(crypto, market=base_fx) # Monthly

# Equities/ ETFs
ticker_I5_df = df.av.intraday(ticker, interval=5)        # Intraday as int
ticker_I5_df.av.name # returns "MSFT"
ticker_I60_df = df.av.intraday(ticker, interval="60min") # Intraday as str
ticker_IE5_df = df.av.intraday_ext(ticker, interval=5) # Adjusted Intraday Extended. Default slice: "year1month1"
ticker_IE30_df = df.av.intraday_ext(ticker, interval="30min", slice="year2month6") # Adjusted Intraday Extended with slice
ticker_IE60_R_df = df.av.intraday_ext(ticker, interval=60, slice="year2month1", adjusted=False) # Raw Intraday Extended with slice

ticker_D_df = df.av.daily(ticker)                        # Daily
ticker_DA_df = df.av.daily_adjusted(ticker)              # Daily Adjusted
ticker_W_df = df.av.weekly(ticker)                       # Weekly
ticker_WA_df = df.av.weekly_adjusted(ticker)             # Weekly Adjusted
ticker_M_df = df.av.monthly(ticker)                      # Monthly
ticker_MA_df = df.av.monthly_adjusted(ticker)            # Monthly Adjusted

Inspiration

If this module does not suit your style or workflow, consider some of the following AlphaVantage API Python Wrapper implementations by: Romel Torres or portfoliome