twelvedata 1.0.2

Python client for Twelve Data API

Twelve Data API

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

• Get stock, forex and cryptocurrency OHLC time series.
• Get over 90+ technical indicators.
• Output data as: json, csv, pandas
• Full support for static and dynamic charts.

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

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

Time series

• TDClient requires apikey parameter. It accepts all common parameters.
• TDClient.time_series() accepts all common parameters. Time series may be converted to several formats:
  • TDClient.time_series().as_json() - will return JSON array
  • TDClient.time_series().as_csv() - will return CSV with header
  • TDClient.time_series().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 90+ technical indicators may be found in API Documentation.

• Technical indicators are part of TDClient.time_series() object.
• It has 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.
• 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 charting is based on matplotlib library. Make sure you have installed it.

• 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 charting is based on plotly library. Make sure you have installed it.

• Use .as_plotly_figure()

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()

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

• Save-load chart templates
• Auto-update charts
• 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.