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nardew / talipp

talipp - incremental technical analysis library for python
https://nardew.github.io/talipp
MIT License
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crypto finance incremental indicators python technical-analysis

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talipp - Incremental Technical Analysis Library

python PyPy unit tests

!!! New documentation page available !!!

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) required by other libraries.

Supported incremental operations include:

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 community project and therefore open to any suggestions how to make it better. You are encouraged to come up with proposals.

What's new in the recent versions

For the full history of changes see CHANGELOG.

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 good chance that it will be included in the future version of the library.

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@main

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.indicator_util import composite_to_lists
from talipp.indicators import EMA, SMA, 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(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(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(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()
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]

# purge the oldest input value
ema.purge_oldest(1)
print(f'EMA after purging the oldest value: {ema}') # [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: {composite_to_lists(stoch)}') # {'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)

print(f"Chain three moving averages:")
sma1.add([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
print(f"SMA1: {sma1}") # [0, 0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
print(f"SMA2: {sma2}") # [0, 0, 0,   0,   3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
print(f"SMA3: {sma3}") # [0, 0, 0,   0,   0,   0,   4.0, 5.0, 6.0, 7.0]

print(f"Purge oldest 3 values:")
sma1.purge_oldest(3)
print(f"SMA1: {sma1}") # [5.0, 6.0, 7.0, 8.0, 9.0]
print(f"SMA2: {sma2}") # [6.0, 7.0, 8.0]
print(f"SMA3: {sma3}") # [7.0]

Performance

To illustrate performance scaling of talipp we ran several tests together with the industry standard talib library and its python wrapper ta-lib. The takeaway from the comparison is following:

SMA(20) TEMA(20) StochRSI(14,3,3)

Contact

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: