Installation¶

numta can be installed using pip with various optional dependencies.

Basic Installation¶

pip install numta

This installs the core library with NumPy support only.

Optional Dependencies¶

Numba Acceleration¶

For 5-10x performance speedup using Numba JIT compilation:

pip install "numta[numba]"

Pandas Integration¶

To use the .ta DataFrame accessor:

pip install "numta[pandas]"

Full Installation¶

For all features including visualization:

pip install "numta[full]"

From Source¶

To install the development version:

git clone https://github.com/deepentropy/numta.git
cd numta
pip install -e .

For development with test dependencies:

pip install -e ".[dev]"

Requirements¶

Python >= 3.8
NumPy >= 1.20.0

Optional Requirements¶

Feature	Package	Installation
Performance	numba >= 0.56.0	`pip install "numta[numba]"`
Pandas	pandas >= 1.3.0	`pip install "numta[pandas]"`
Visualization	lwcharts >= 0.1.0	`pip install "numta[viz]"`
All features	-	`pip install "numta[full]"`

Verifying Installation¶

import numta

# Check version
print(numta.__version__)

# Quick test
import numpy as np
close = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
sma = numta.SMA(close, timeperiod=3)
print(sma)  # [nan, nan, 2., 3., 4.]

Troubleshooting¶

Numba Installation Issues¶

If you encounter issues with Numba:

Ensure you have a compatible Python version (3.8-3.12)

Try installing LLVM libraries first:

# Ubuntu/Debian
sudo apt-get install llvm

# macOS
brew install llvm

Missing Indicators¶

If an indicator is not found, ensure you've imported it correctly:

# Import specific indicators
from numta import SMA, EMA, RSI

# Or import all
import numta
sma = numta.SMA(close, timeperiod=20)