numta

Pure Python Technical Analysis Library

A modern, high-performance alternative to TA-Lib with zero C dependencies.

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Highlights

• :material-language-python: Pure Python

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  No C compiler required, works everywhere Python runs

• :material-lightning-bolt: Fast

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  5-10x speedup with optional Numba JIT compilation

• :material-check-all: Complete

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  130+ indicators, 60+ candlestick patterns, chart pattern detection

• :material-new-box: Modern

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  Pandas integration, streaming support, Jupyter visualization

Quick Example

import numpy as np
from numta import SMA, EMA, RSI

# Create sample price data
close_prices = np.random.uniform(100, 200, 100)

# Calculate indicators
sma = SMA(close_prices, timeperiod=20)
ema = EMA(close_prices, timeperiod=12)
rsi = RSI(close_prices, timeperiod=14)

Installation

# Basic installation
pip install numta

# With Numba for 5-10x speedup
pip install "numta[numba]"

# With pandas integration
pip install "numta[pandas]"

# Full installation with all features
pip install "numta[full]"

Features

Technical Indicators

numta provides 130+ technical indicators across multiple categories:

Category	Indicators
Overlap Studies	SMA, EMA, DEMA, TEMA, WMA, BBANDS, KAMA, MAMA, T3, SAR
Momentum	RSI, MACD, STOCH, ADX, CCI, MFI, ROC, MOM, WILLR
Volume	OBV, AD, ADOSC
Volatility	ATR, NATR, TRANGE
Cycle	Hilbert Transform functions
Statistical	LINEARREG, STDDEV, VAR, CORREL, BETA

Pandas Integration

import pandas as pd
import numta  # Auto-registers the .ta accessor

df = pd.DataFrame({
    'open': [...], 'high': [...], 'low': [...],
    'close': [...], 'volume': [...]
})

# Calculate and return as Series
sma = df.ta.sma(timeperiod=20)

# Append indicators to DataFrame
df.ta.sma(timeperiod=20, append=True)   # Adds 'SMA_20'
df.ta.rsi(timeperiod=14, append=True)   # Adds 'RSI_14'
df.ta.macd(append=True)                 # Adds MACD columns

Streaming Indicators

from numta.streaming import StreamingSMA, StreamingRSI, StreamingMACD

# Create streaming indicators
sma = StreamingSMA(timeperiod=20)
rsi = StreamingRSI(timeperiod=14)

# Process streaming data
for price in price_stream:
    sma_value = sma.update(price)
    rsi_value = rsi.update(price)

    if sma.ready and rsi.ready:
        print(f"SMA: {sma_value:.2f}, RSI: {rsi_value:.2f}")

Pattern Recognition

from numta import CDLDOJI, CDLENGULFING, CDLHAMMER

# Returns +100 (bullish), -100 (bearish), or 0 (no pattern)
doji = CDLDOJI(open_, high, low, close)
engulfing = CDLENGULFING(open_, high, low, close)

# Via pandas accessor
df.ta.cdldoji(append=True)
df.ta.cdlengulfing(append=True)

Getting Started

Check out the Installation Guide and Quick Start to begin using numta.

API Reference

Explore the complete API Reference for detailed documentation of all indicators.

License

MIT License - see LICENSE for details.