# tulipy
*Forked by Drakkar-Software for the only purpose of keeping dependencies version up to date.*
## Python bindings for [Tulip Indicators](https://tulipindicators.org/)
Tulipy requires [numpy](http://www.numpy.org/) as all inputs and outputs are numpy arrays (`dtype=np.float64`).
## Installation
You can install via `pip install OctoBot-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
```python
import numpy as np
import tulipy as ti
```
```python
ti.TI_VERSION
```
'0.8.4'
```python
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:
```python
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)
```
```python
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.
```python
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])
```python
print_info(ti.sma)
```
Type: overlay
Full Name: Simple Moving Average
Inputs: ['real']
Options: ['period']
Outputs: ['sma']
```python
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`:
```python
try:
ti.sma(DATA, period=-5)
except ti.InvalidOptionError:
print("Invalid Option!")
```
Invalid Option!
```python
print_info(ti.bbands)
```
Type: overlay
Full Name: Bollinger Bands
Inputs: ['real']
Options: ['period', 'stddev']
Outputs: ['bbands_lower', 'bbands_middle', 'bbands_upper']
```python
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:
```python
DATA2 = np.array([83.15, 82.84, 83.99, 84.55, 84.36])
```
```python
# '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.])
Raw data
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