Twelve Data Python Client for financial API & WebSocket

Official python library for Twelve Data. This package supports all main features of the service:

Get stock, forex, cryptocurrency, ETF, and index OHLC time series.
Companies' profiles, financials, options, and much more fundamentals data.
Get over 100+ technical indicators.
Output data as: json, csv, pandas
Full support for static and dynamic charts.
Real-time WebSocket data stream.

API key is required. If you don't have it yet, get it by signing up here.

Installation

Use the package manager pip to install Twelve Data API library (without optional dependencies):

pip install twelvedata

Or install with pandas support:

pip install twelvedata[pandas]

Or install with pandas, matplotlib, plotly, and websocket support:

pip install twelvedata[pandas,matplotlib,plotly,websocket-client]

Supported parameters

Parameter	Description	Type	Required
symbol	stock ticker (e.g. AAPL, MSFT); physical currency pair (e.g. EUR/USD, CNY/JPY); digital currency pair (BTC/USD, XRP/ETH)	string	yes
interval	time frame: 1min, 5min, 15min, 30min, 45min, 1h, 2h, 4h, 8h, 1day, 1week, 1month	string	yes
apikey	your personal API Key, if you don't have one - get it here	string	yes
exchange	if symbol is traded in multiple exchanges specify the desired one, valid for both stocks and cryptocurrencies	string	no
mic_code	Market Identifier Code (MIC) under ISO 10383 standard, valid for stocks	string	no
country	if symbol is traded in multiple countries specify the desired one, valid for stocks	string	no
outputsize	number of data points to retrieve	int	no
timezone	timezone at which output datetime will be displayed, supports: `UTC`, `Exchange` or according to IANA Time Zone Database	string	no
start_date	start date and time of sampling period, accepts `yyyy-MM-dd` or `yyyy-MM-dd hh:mm:ss` format	string	no
end_date	end date and time of sampling period, accepts `yyyy-MM-dd` or `yyyy-MM-dd hh:mm:ss` format	string	no
order	sorting order of the time series output, supports `desc` or `asc`	string	no
date	Could be the exact date, e.g. `2021-10-27`, or in human language `today` or `yesterday`	string	no

The basis for all methods is the TDClient object that takes the required apikey parameter.

Time series

TDClient.time_series() accepts all common parameters. Time series may be converted to several formats:
- ts.as_json() - will return JSON array
- ts.as_csv() - will return CSV with header
- ts.as_pandas() - will return pandas.DataFrame
- ts.as_url() - will return list of URLs used

from twelvedata import TDClient

# Initialize client - apikey parameter is requiered
td = TDClient(apikey="YOUR_API_KEY_HERE")

# Construct the necessary time series
ts = td.time_series(
    symbol="AAPL",
    interval="1min",
    outputsize=10,
    timezone="America/New_York",
)

# Returns pandas.DataFrame
ts.as_pandas()

Other core data endpoints:

Exchange rate - TDClient.exchange_rate(symbol)
Currency conversion - TDClient.currency_conversion(symbol, amount)
Quote - TDClient.quote() takes parameters as .time_series()
Real-time price - TDClient.price() takes parameters as .time_series()
End of day price - TDClient.eod() takes parameters as .time_series()

Fundamentals

All fundamentals are supported across global markets. Refer to API documentation here and find out which countries support which fundamentals by visiting this page.

.get_logo(symbol, exchange, country, type)
.get_profile(symbol, exchange, country, type)
.get_dividends(symbol, exchange, country, type)
.get_splits(symbol, exchange, country, type)
.get_earnings(symbol, exchange, country, type, period, outputsize, start_date, end_date)
.get_earnings_calendar(symbol, exchange, country, period, start_date, end_date)
.get_ipo_calendar(symbol, exchange, country, start_date, end_date)
.get_statistics(symbol, exchange, country, type)
.get_insider_transactions(symbol, exchange, country, type)
.get_income_statement(symbol, exchange, country, type, period, start_date, end_date)
.get_balance_sheet(symbol, exchange, country, type, period, start_date, end_date)
.get_cash_flow(symbol, exchange, country, type, period, start_date, end_date)
.get_options_expiration(symbol, exchange, country, type)
.get_options_chain(symbol, exchange, country, type, expiration_date, option_id, side)
.get_key_executives(symbol, exchange, country, type)
.get_institutional_holders(symbol, exchange, country, type)
.get_fund_holders(symbol, exchange, country, type)

Only JSON format is supported accessible via .as_json()

from twelvedata import TDClient

td = TDClient(apikey="YOUR_API_KEY_HERE")

# Get all expiration dates
expirations = td.get_options_expiration(
    symbol="AAPL",
).as_json()['dates']

# Extract only put options for the soonest expiration date
put_options = td.get_options_chain(
    symbol="AAPL",
    side="put",
    expiration_date=expirations[0]
).as_json()['puts']

print(put_options)

Technical indicators

This Python library supports all indicators implemented by Twelve Data. Full list of 100+ technical indicators may be found in API documentation.

Technical indicators are part of TDClient.time_series() object.
It shares the universal format TDClient.time_series().with_{Technical Indicator Name}, e.g. .with_bbands(), .with_percent_b(), .with_macd()
Indicator object accepts all parameters according to its specification in API documentation, e.g. .with_bbands() accepts: series_type, time_period, sd, ma_type. If parameter is not provided it will be set to default value.
Indicators may be used in arbitrary order and conjugated, e.g. TDClient.time_series().with_aroon().with_adx().with_ema()
By default, technical indicator will output with OHLC values. If you do not need OHLC, specify TDClient.time_series().without_ohlc()

from twelvedata import TDClient

td = TDClient(apikey="YOUR_API_KEY_HERE")
ts = td.time_series(
    symbol="ETH/BTC",
    exchange="Huobi",
    interval="5min",
    outputsize=22,
    timezone="America/New_York",
)

# Returns: OHLC, BBANDS(close, 20, 2, EMA), PLUS_DI(9), WMA(20), WMA(40)
ts.with_bbands(ma_type="EMA").with_plus_di().with_wma(time_period=20).with_wma(time_period=40).as_pandas()

# Returns: STOCH(14, 1, 3, SMA, SMA), TSF(close, 9)
ts.without_ohlc().with_stoch().with_tsf().as_json()

Batch requests

With batch requests up to 120 symbols might be returned per single API call. There are two options on how to do this:

# 1. Pass instruments symbols as a string delimited by comma (,)
ts = td.time_series(
    symbol="V, RY, AUD/CAD, BTC/USD:Huobi"
)

# 2. Pass as a list of symbols 
ts = td.time_series(
    symbol=["V", "RY", "AUD/CAD", "BTC/USD:Huobi"]
)

Important. Batch requests are only supported with .as_json() and .as_pandas() formats.

With .as_json() the output will be a dictionary with passed symbols as keys. The value will be a tuple with quotes, just the same as with a single request.

ts = td.time_series(symbol='AAPL,MSFT', interval="1min", outputsize=3)
df = ts.with_macd().with_macd(fast_period=10).with_stoch().as_json()

{
    "AAPL": ({'datetime': '2020-04-23 15:59:00', 'open': '275.23001', 'high': '275.25000', 'low': '274.92999', 'close': '275.01001', 'volume': '393317', 'macd_1': '-0.33538', 'macd_signal_1': '-0.24294', 'macd_hist_1': '-0.09244', 'macd_2': '-0.40894', 'macd_signal_2': '-0.29719', 'macd_hist_2': '-0.11175', 'slow_k': '4.52069', 'slow_d': '7.92871'}, {'datetime': '2020-04-23 15:58:00', 'open': '275.07001', 'high': '275.26999', 'low': '275.00000', 'close': '275.25000', 'volume': '177685', 'macd_1': '-0.31486', 'macd_signal_1': '-0.21983', 'macd_hist_1': '-0.09503', 'macd_2': '-0.38598', 'macd_signal_2': '-0.26925', 'macd_hist_2': '-0.11672', 'slow_k': '14.70578', 'slow_d': '6.82079'}, {'datetime': '2020-04-23 15:57:00', 'open': '275.07001', 'high': '275.16000', 'low': '275.00000', 'close': '275.07751', 'volume': '151169', 'macd_1': '-0.30852', 'macd_signal_1': '-0.19607', 'macd_hist_1': '-0.11245', 'macd_2': '-0.38293', 'macd_signal_2': '-0.24007', 'macd_hist_2': '-0.14286', 'slow_k': '4.55965', 'slow_d': '2.75237'}),
    "MSFT": ({'datetime': '2020-04-23 15:59:00', 'open': '171.59000', 'high': '171.64000', 'low': '171.22000', 'close': '171.42000', 'volume': '477631', 'macd_1': '-0.12756', 'macd_signal_1': '-0.10878', 'macd_hist_1': '-0.01878', 'macd_2': '-0.15109', 'macd_signal_2': '-0.12915', 'macd_hist_2': '-0.02193', 'slow_k': '20.95244', 'slow_d': '26.34919'}, {'datetime': '2020-04-23 15:58:00', 'open': '171.41000', 'high': '171.61000', 'low': '171.33501', 'close': '171.61000', 'volume': '209594', 'macd_1': '-0.12440', 'macd_signal_1': '-0.10408', 'macd_hist_1': '-0.02032', 'macd_2': '-0.14786', 'macd_signal_2': '-0.12367', 'macd_hist_2': '-0.02419', 'slow_k': '39.04785', 'slow_d': '23.80945'}, {'datetime': '2020-04-23 15:57:00', 'open': '171.34500', 'high': '171.48000', 'low': '171.25999', 'close': '171.39999', 'volume': '142450', 'macd_1': '-0.13791', 'macd_signal_1': '-0.09900', 'macd_hist_1': '-0.03891', 'macd_2': '-0.16800', 'macd_signal_2': '-0.11762', 'macd_hist_2': '-0.05037', 'slow_k': '19.04727', 'slow_d': '14.92063'})
}

With .as_pandas() the output will be a 3D DataFrame with MultiIndex for (symbol, datetime).

ts = td.time_series(symbol='AAPL,MSFT', interval="1min", outputsize=3)
df = ts.with_macd().with_macd(fast_period=10).with_stoch().as_pandas()

#                                open       high  ...    slow_k    slow_d
# AAPL 2020-04-23 15:59:00  275.23001  275.25000  ...   4.52069   7.92871
#      2020-04-23 15:58:00  275.07001  275.26999  ...  14.70578   6.82079
#      2020-04-23 15:57:00  275.07001  275.16000  ...   4.55965   2.75237
# MSFT 2020-04-23 15:59:00  171.59000  171.64000  ...  20.95244  26.34919
#      2020-04-23 15:58:00  171.41000  171.61000  ...  39.04785  23.80945
#      2020-04-23 15:57:00  171.34500  171.48000  ...  19.04727  14.92063
# 
# [6 rows x 13 columns]

df.loc['AAPL']

#                           open       high  ...    slow_k   slow_d
# 2020-04-23 15:59:00  275.23001  275.25000  ...   4.52069  7.92871
# 2020-04-23 15:58:00  275.07001  275.26999  ...  14.70578  6.82079
# 2020-04-23 15:57:00  275.07001  275.16000  ...   4.55965  2.75237
# 
# [3 rows x 13 columns]

df.columns

# Index(['open', 'high', 'low', 'close', 'volume', 'macd1', 'macd_signal1',
#        'macd_hist1', 'macd2', 'macd_signal2', 'macd_hist2', 'slow_k',
#        'slow_d'],
#       dtype='object')

Charts

Static
Interactive

Charts support OHLC, technical indicators and custom bars.

Static

Static charts are based on matplotlib library and require mplfinance package to be installed.

Use .as_pyplot_figure()

from twelvedata import TDClient

td = TDClient(apikey="YOUR_API_KEY_HERE")
ts = td.time_series(
    symbol="MSFT",
    outputsize=75,
    interval="1day",
)

# 1. Returns OHLCV chart
ts.as_pyplot_figure()

# 2. Returns OHLCV + BBANDS(close, 20, 2, SMA) + %B(close, 20, 2 SMA) + STOCH(14, 3, 3, SMA, SMA)
ts.with_bbands().with_percent_b().with_stoch(slow_k_period=3).as_pyplot_figure()

Interactive

Interactive charts are built on top of plotly library.

Use .as_plotly_figure().show()

from twelvedata import TDClient

td = TDClient(apikey="YOUR_API_KEY_HERE")
ts = td.time_series(
    symbol="DNR",
    outputsize=50,
    interval="1week",
)

# 1. Returns OHLCV chart
ts.as_plotly_figure()

# 2. Returns OHLCV + EMA(close, 7) + MAMA(close, 0.5, 0.05) + MOM(close, 9) + MACD(close, 12, 26, 9)
ts.with_ema(time_period=7).with_mama().with_mom().with_macd().as_plotly_figure().show()

WebSocket

With the WebSocket, a duplex communication channel with the server is established.

Make sure to have websocket_client package installed.

Features

Real-time low latency stream of financial quotes.
You might subscribe to stocks, forex, and crypto.

Example

import time
from twelvedata import TDClient


messages_history = []


def on_event(e):
    # do whatever is needed with data
    print(e)
    messages_history.append(e)


td = TDClient(apikey="YOUR_API_KEY_HERE")
ws = td.websocket(symbols="BTC/USD", on_event=on_event)
ws.subscribe(['ETH/BTC', 'AAPL'])
ws.connect()
while True:
    print('messages received: ', len(messages_history))
    ws.heartbeat()
    time.sleep(10)

Parameters accepted by the .websocket() object:

symbols list of symbols to subscribe
on_event function that invokes when event from server is received
logger instance of logger, otherwise set to default
max_queue_size maximum size of queue, default 12000
log_level accepts debug or info, otherwise not set

Applicable methods on .websocket() object:

ws.subscribe([list of symbols]): get data from the symbols passed
ws.unsubscribe([list of symbols]): stop receiving data from the symbols passed
ws.reset(): unsubscribe from all symbols
ws.connect(): establish connection with WebSocket server
ws.disconnect(): close connection with WebSocket server
ws.heartbeat(): send heartbeat to server

Important. Do not forget that WebSockets are only available for Twelve Data users on the Pro plan and above. Checkout the trial here.

Advanced

Custom endpoint

This method is used to request unrepresented endpoints on this package, but which are available at Twelve Data.

endpoint = td.custom_endpoint(
    name="quote",
    symbol="AAPL",
)
endpoint.as_json()

The only required parameter is name which should be identical to the endpoint used at Twelve Data. All others can be custom and will vary according to the method.

Debugging

When the method doesn't return the desired data or throws an error, it might be helpful to understand and analyze the API query behind it. Add .as_url() to any method or chain of methods, and it will return the list of used URLs.

ts = td.time_series(
    symbol="AAPL",
    interval="1min",
    outputsize=10,
    timezone="America/New_York",
).with_bbands().with_ema()

ts.as_url()
# ['https://api.twelvedata.com/time_series?symbol=AAPL&interval=1min&outputsize=10&dp=5&timezone=America/New_York&order=desc&prepost=false&format=JSON&apikey=demo', 
# 'https://api.twelvedata.com/bbands?symbol=AAPL&interval=1min&series_type=close&time_period=20&sd=2&ma_type=SMA&outputsize=10&dp=5&timezone=America/New_York&order=desc&prepost=false&format=JSON&apikey=demo', 
# 'https://api.twelvedata.com/ema?symbol=AAPL&interval=1min&series_type=close&time_period=9&outputsize=10&dp=5&timezone=America/New_York&order=desc&prepost=false&format=JSON&apikey=demo']

API usage

This method gives an overview of the current API credits consumption.

api = td.api_usage()

Support

Visit our official website contact page or support center.

Announcements

Follow us for announcements and updates about this library.

Roadmap

Contributing

Clone repo and create a new branch: $ git checkout https://github.com/twelvedata/twelvedata -b name_for_new_branch.
Make changes and test.
Submit Pull Request with comprehensive description of changes.

License

This package is open-sourced software licensed under the MIT license.

Name		Name	Last commit message	Last commit date
Latest commit History 95 Commits
.github		.github
asset		asset
docs		docs
src/twelvedata		src/twelvedata
tests		tests
.coveragerc		.coveragerc
.gitignore		.gitignore
.travis.yml		.travis.yml
AUTHORS.rst		AUTHORS.rst
CHANGELOG.rst		CHANGELOG.rst
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
LICENSE.txt		LICENSE.txt
Pipfile		Pipfile
Pipfile.lock		Pipfile.lock
README.md		README.md
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
setup.cfg		setup.cfg
setup.py		setup.py

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Twelve Data Python Client for financial API & WebSocket

Installation

Usage

Supported parameters

Time series

Fundamentals

Technical indicators

Batch requests

Charts

Static

Interactive

WebSocket

Features

Example

Advanced

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API usage

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Twelve Data Python Client for financial API & WebSocket

Installation

Usage

Supported parameters

Time series

Fundamentals

Technical indicators

Batch requests

Charts

Static

Interactive

WebSocket

Features

Example

Advanced

Custom endpoint

Debugging

API usage

Support

Announcements

Roadmap

Contributing

License

About

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