talipp 1.1.0

TALi++ - Incremental Technical Analysis Library

TALIpp - Incremental Technical Analysis Library

talipp (or tali++) is a Python library implementing financial indicators for technical analysis. The distinctive feature of the library is its incremental computation which fits extremely well real-time applications or applications with iterative input in general.

Unlike existing libraries for technical analysis which typically have to work on the whole input vector in order to calculate new values of indicators, talipp due to its incremental architecture calculates new indicators' values exclusively based on the delta input data. That implies, among others, it requires O(1) time to produce new values in comparison to O(n) (or worse) required by other libraries.

Supported incremental operations include:

• appending new values to the input
• updating the last input value
• removing arbitrary number of the input values

Besides the already mentioned superior time complexity for delta input operations, talipp's incremental approach immediately offers other interesting features for free, such as indicator chaining or building new indicators combined from other indicators. See section with examples to get an idea.

Incremental nature of talipp naturally excels in applications with frequent CUD operations but it can be used for charting, back-testing, ... as any other existing library.

Last but not least, talipp is a very young project and therefore open to any suggestions of amending the API to users' liking. You are encouraged to come up with proposals.

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What's new in version 1.1.0

• indicator chaining
• delta input values can be added as a single values as well as a list

For the full history of changes see CHANGELOG.

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List of incremental indicators

talipp currently provides below set of indicators. If your favourite indicator is missing, then create a ticket via GitHub Issues and there is a chance that it will be included in the future version of the library.

Type	Indicators
trend	Average Directional Index (ADX) Awesome Oscillator (AO) Ichimoku Kinko Hyo Know Sure Thing (KST) Moving Average Convergence Divergence (MACD) Mass Index Moving Average (ALMA, SMA, SMMA, DEMA, EMA, HMA, TEMA, VWMA, WMA) Parabolic SAR SFX TOR
support/resistance	High/Low Pivots
momentum	Chaikin Oscillator Rate of Change (ROC) Relative strength index (RSI) Stochastic Oscillator Stochastic RSI Ultimate Oscillator (UO)
volume	Accumulation/Distribution (ADL) On-balance Volume (OBV)
volatility	Average True Range (ATR) Bollinger Bands (BB) Donchian Channel (DC) Keltner Channel (KC) Standard deviation

Installation

pip install talipp

In case you want to install the latest version from the repo, use

pip install git+https://github.com/nardew/talipp.git@master

Examples

Consult examples folder to see usage of every single indicator included in the library. To get the basic look and feel of the API, see below.

from talipp.indicators import EMA, Stoch
from talipp.ohlcv import OHLCVFactory

# EMA indicator ([float] -> [float])
ema = EMA(period = 3, input_values = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10])

# treat indicators as any other list
print(f'EMA(3): {ema}') # [3.0, 5.0, 7.0, 4.5, 4.25, 5.125, 6.5625, 8.28125]
print(f'Last EMA value: {ema[-1]}') # 8.28125

# append a new input value incrementally
ema.add_input_value(11)
print(f'EMA after adding a new value:      {ema}') # [3.0, 5.0, 7.0, 4.5, 4.25, 5.125, 6.5625, 8.28125, 9.640625]

# change the last added value
ema.update_input_value(15)
print(f'EMA after updating the last value: {ema}') # [3.0, 5.0, 7.0, 4.5, 4.25, 5.125, 6.5625, 8.28125, 11.640625]

# change the last added value again
ema.update_input_value(18)
print(f'EMA after updating the last value: {ema}') # [3.0, 5.0, 7.0, 4.5, 4.25, 5.125, 6.5625, 8.28125, 13.140625]

# remove the last added value
ema.remove_input_value()
print(f'EMA after removing the last value: {ema}') # [3.0, 5.0, 7.0, 4.5, 4.25, 5.125, 6.5625, 8.28125]

# STOCH indicator ([OHLCV] -> [composite])
stoch = Stoch(5, 3, OHLCVFactory.from_dict({
    'high':     [5, 10, 15, 20, 25, 30, 35],
    'low':      [1, 4, 7, 10, 13, 16, 19],
    'close':    [3, 9, 8, 19, 18, 17, 19]
}))

# print result as a list of composite values for 'k' and 'd' output parameters
print(f'Stoch(5, 3) composite result: {stoch}') # [StochVal(k=70.83333333333333, d=None), StochVal(k=50.0, d=None), StochVal(k=42.857142857142854, d=54.563492063492056)]

# print result as lists per output parameters
print(f'Stoch(5, 3) decomposed result: {stoch.to_lists()}') # {'k': [70.83333333333333, 50.0, 42.857142857142854], 'd': [None, None, 54.563492063492056]} 

# Indicator chaining
sma1 = SMA(3)
sma2 = SMA(3, input_indicator = sma1)
sma3 = SMA(3, input_indicator = sma2)

sma1.add_input_value([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
print(f"SMA1: {sma1}") # [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
print(f"SMA2: {sma2}") # [3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
print(f"SMA3: {sma3}") # [4.0, 5.0, 6.0, 7.0]

Contact

• to report issues, bugs, corrections or to propose new features use preferably Github Issues
• for topics requiring more personal approach feel free to send an e-mail to

Support

If you like the library and you feel like you want to support its further development, enhancements and bug fixing, then it will be of great help and most appreciated if you:

• file bugs, proposals, pull requests, ...
• spread the word
• donate an arbitrary tip
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