Tulipy - Financial Technical Analysis Indicator Library (Python bindings for https://github.com/TulipCharts/tulipindicators)
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tulipy

Python bindings for Tulip Indicators

Tulipy requires numpy as all inputs and outputs are numpy arrays (dtype=np.float64).

Installation

You can install via pip install tulipy. If a wheel is not available for your system, you will need to pip install Cython numpy to build from the source distribution. When building from source on Windows, you will need the Microsoft Visual C++ Build Tools installed.

Usage

import numpy as np
import tulipy as ti
ti.TI_VERSION
'0.8.3'
DATA = np.array([81.59, 81.06, 82.87, 83,    83.61,
                 83.15, 82.84, 83.99, 84.55, 84.36,
                 85.53, 86.54, 86.89, 87.77, 87.29])

Information about indicators are exposed as properties:

def print_info(indicator):
    print("Type:", indicator.type)
    print("Full Name:", indicator.full_name)
    print("Inputs:", indicator.inputs)
    print("Options:", indicator.options)
    print("Outputs:", indicator.outputs)
print_info(ti.sqrt)
Type: simple
Full Name: Vector Square Root
Inputs: ['real']
Options: []
Outputs: ['sqrt']

Single outputs are returned directly. Indicators returning multiple outputs use a tuple in the order indicated by the outputs property.

ti.sqrt(DATA)
array([ 9.03271831,  9.00333272,  9.10329611,  9.11043358,  9.14385039,
        9.11866218,  9.1016482 ,  9.16460583,  9.19510739,  9.18477   ,
        9.24824308,  9.30268778,  9.32148057,  9.36856446,  9.34291175])
print_info(ti.sma)
Type: overlay
Full Name: Simple Moving Average
Inputs: ['real']
Options: ['period']
Outputs: ['sma']
ti.sma(DATA, period=5)
array([ 82.426,  82.738,  83.094,  83.318,  83.628,  83.778,  84.254,
        84.994,  85.574,  86.218,  86.804])

Invalid options will throw an InvalidOptionError:

try:
    ti.sma(DATA, period=-5)
except ti.InvalidOptionError:
    print("Invalid Option!")
Invalid Option!
print_info(ti.bbands)
Type: overlay
Full Name: Bollinger Bands
Inputs: ['real']
Options: ['period', 'stddev']
Outputs: ['bbands_lower', 'bbands_middle', 'bbands_upper']
ti.bbands(DATA, period=5, stddev=2)
(array([ 80.53004219,  80.98714192,  82.53334324,  82.47198345,
         82.41775044,  82.43520292,  82.51133078,  83.14261781,
         83.53648779,  83.8703237 ,  85.28887096]),
 array([ 82.426,  82.738,  83.094,  83.318,  83.628,  83.778,  84.254,
         84.994,  85.574,  86.218,  86.804]),
 array([ 84.32195781,  84.48885808,  83.65465676,  84.16401655,
         84.83824956,  85.12079708,  85.99666922,  86.84538219,
         87.61151221,  88.5656763 ,  88.31912904]))

If inputs of differing sizes are provided, they are right-aligned and trimmed from the left:

DATA2 = np.array([83.15, 82.84, 83.99, 84.55, 84.36])
# 'high' trimmed to DATA[-5:] == array([ 85.53,  86.54,  86.89,  87.77,  87.29])
ti.aroonosc(high=DATA, low=DATA2, period=2)
array([  50.,  100.,   50.])