twelvedata 1.2

Python client for Twelve Data API

Twelve Data Python Client for APIs & WebSockets

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

• Get stock, forex and cryptocurrency OHLC time series.
• Get over 100+ technical indicators.
• Output data as: json, csv, pandas
• Full support for static and dynamic charts.
• Real-time WebSockets data stream.

Free API Key is required. It might be requested 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 and plotly support used for charting:

pip install twelvedata[pandas,matplotlib,plotly]

Usage

• Time series
• Technical Indicators
• Batch requests
• Charts
• WebSocket

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
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

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

from twelvedata import TDClient
# Initialize client - apikey parameter is requiered
td = TDClient(apikey="YOUR_API_KEY_HERE")
# Construct the necessary time serie
ts = td.time_series(
    symbol="AAPL",
    interval="1min",
    outputsize=10,
    timezone="America/New_York",
)
# Returns pandas.DataFrame
ts.as_pandas()

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

from twelvedata import TDClient

def on_event(e):
    # do whatever is needed with data
    print(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()
ws.keep_alive()

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.keep_alive(): run connection forever until closed

Important. Do not forget that WebSockets are only available for Twelve Data Prime members.

Support

Visit our official website https://twelvedata.com or reach out to the Twelve Data team at [e-mail].

Announcements

Follow @TwelveData on Twitter for announcements and updates about this library.

Roadmap

• WebSocket
• Batch requests
• Custom plots coloring
• Interactive charts (plotly)
• Static charts (matplotlib)
• Pandas support

Contributing

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

License

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