pytafast 0.3.1

Python wrapper for ta-lib using nanobind

pytafast

A high-performance Python wrapper for TA-Lib built with nanobind. Provides 150+ technical analysis functions with pandas/numpy support and async capabilities.

Features

• 🚀 High Performance — C++ bindings via nanobind with GIL release for true parallelism
• 📊 Full TA-Lib Coverage — 150+ indicators including overlaps, momentum, volatility, volume, statistics, cycle indicators, and 61 candlestick patterns
• 🐼 Pandas Native — Seamless support for both numpy.ndarray and pandas.Series (preserves index)
• ⚡ Async Support — All functions available as async via pytafast.aio
• 🔒 Memory Safe — Zero-copy data access with proper ownership management
• 📦 Drop-in Replacement — Same API as ta-lib-python, easy migration

Installation

pip install pytafast

Build from Source

git clone --recursive https://github.com/twn39/pytafast.git
cd pytafast
pip install -v -e .

Quick Start

Basic Usage with NumPy

import numpy as np
import pytafast

# Generate sample price data
np.random.seed(42)
close = np.cumsum(np.random.randn(200)) + 100

# Moving Averages
sma = pytafast.SMA(close, timeperiod=20)
ema = pytafast.EMA(close, timeperiod=12)
wma = pytafast.WMA(close, timeperiod=10)

Momentum Indicators

# RSI — Relative Strength Index
rsi = pytafast.RSI(close, timeperiod=14)

# MACD — returns 3 arrays: macd line, signal line, histogram
macd, signal, hist = pytafast.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)

# Stochastic Oscillator — requires high, low, close
high = close + np.abs(np.random.randn(200)) * 2
low = close - np.abs(np.random.randn(200)) * 2
slowk, slowd = pytafast.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowd_period=3)

# ADX — Average Directional Index
adx = pytafast.ADX(high, low, close, timeperiod=14)

Bollinger Bands & Volatility

# Bollinger Bands — returns upper, middle, lower bands
upper, middle, lower = pytafast.BBANDS(close, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)

# ATR — Average True Range
atr = pytafast.ATR(high, low, close, timeperiod=14)

Volume Indicators

volume = np.random.random(200) * 1_000_000

# On-Balance Volume
obv = pytafast.OBV(close, volume)

# Chaikin A/D Line
ad = pytafast.AD(high, low, close, volume)

# Money Flow Index
mfi = pytafast.MFI(high, low, close, volume, timeperiod=14)

Statistics & Math

# Linear Regression
linreg = pytafast.LINEARREG(close, timeperiod=14)
slope = pytafast.LINEARREG_SLOPE(close, timeperiod=14)

# Correlation between two series
beta = pytafast.BETA(close, ema, timeperiod=5)
correl = pytafast.CORREL(close, ema, timeperiod=30)

# Standard Deviation
stddev = pytafast.STDDEV(close, timeperiod=20, nbdev=1.0)

Candlestick Pattern Recognition

# Generate OHLC data
open_ = close + np.random.randn(200) * 0.5

# Detect patterns — returns integer array (100=bullish, -100=bearish, 0=none)
engulfing = pytafast.CDLENGULFING(open_, high, low, close)
doji = pytafast.CDLDOJI(open_, high, low, close)
hammer = pytafast.CDLHAMMER(open_, high, low, close)
morning_star = pytafast.CDLMORNINGSTAR(open_, high, low, close, penetration=0.3)

# Find bullish signals
bullish_idx = np.where(engulfing == 100)[0]

Pandas Support

import pandas as pd

# Create a DataFrame of stock prices
df = pd.DataFrame({
    "open": open_,
    "high": high,
    "low": low,
    "close": close,
    "volume": volume,
}, index=pd.date_range("2024-01-01", periods=200, freq="D"))

# All functions accept and return pd.Series, preserving the DatetimeIndex
df["sma_20"] = pytafast.SMA(df["close"], timeperiod=20)
df["rsi_14"] = pytafast.RSI(df["close"], timeperiod=14)
df["atr_14"] = pytafast.ATR(df["high"], df["low"], df["close"], timeperiod=14)

upper, middle, lower = pytafast.BBANDS(df["close"], timeperiod=20)
df["bb_upper"] = upper
df["bb_lower"] = lower

Async Support

import asyncio
import pytafast

async def compute_indicators(close, high, low, volume):
    """Compute multiple indicators concurrently."""
    sma, rsi, macd_result, atr = await asyncio.gather(
        pytafast.aio.SMA(close, timeperiod=20),
        pytafast.aio.RSI(close, timeperiod=14),
        pytafast.aio.MACD(close),
        pytafast.aio.ATR(high, low, close, timeperiod=14),
    )
    macd, signal, hist = macd_result
    return sma, rsi, macd, atr

# asyncio.run(compute_indicators(close, high, low, volume))

Cycle Indicators

# Hilbert Transform indicators for cycle analysis
ht_period = pytafast.HT_DCPERIOD(close)
ht_trendline = pytafast.HT_TRENDLINE(close)
sine, leadsine = pytafast.HT_SINE(close)
trend_mode = pytafast.HT_TRENDMODE(close)  # 1 = trend, 0 = cycle

Supported Indicators

Overlap Studies

SMA, EMA, BBANDS, DEMA, KAMA, MA, T3, TEMA, TRIMA, WMA, MIDPOINT, SAR

Momentum Indicators

RSI, MACD, MACDEXT, MACDFIX, ADX, ADXR, CCI, ROC, ROCP, ROCR, ROCR100, STOCH, STOCHF, STOCHRSI, MOM, WILLR, MFI, CMO, DX, MINUS_DI, MINUS_DM, PLUS_DI, PLUS_DM, APO, AROON, AROONOSC, PPO, TRIX, ULTOSC, BOP

Volatility

ATR, NATR, TRANGE

Volume

OBV, AD, ADOSC

Price Transform

AVGPRICE, MEDPRICE, TYPPRICE, WCLPRICE, MIDPRICE

Statistics

STDDEV, BETA, CORREL, LINEARREG, LINEARREG_ANGLE, LINEARREG_INTERCEPT, LINEARREG_SLOPE, TSF, VAR, AVGDEV, MIN, MAX, SUM, MINMAX, MINMAXINDEX

Math Operators

ADD, SUB, MULT, DIV

Math Transforms

ACOS, ASIN, ATAN, CEIL, COS, COSH, EXP, FLOOR, LN, LOG10, SIN, SINH, SQRT, TAN, TANH

Cycle Indicators

HT_DCPERIOD, HT_DCPHASE, HT_PHASOR, HT_SINE, HT_TRENDLINE, HT_TRENDMODE

Candlestick Patterns (61 patterns)

CDL2CROWS, CDL3BLACKCROWS, CDL3INSIDE, CDL3LINESTRIKE, CDL3OUTSIDE, CDL3STARSINSOUTH, CDL3WHITESOLDIERS, CDLABANDONEDBABY, CDLADVANCEBLOCK, CDLBELTHOLD, CDLBREAKAWAY, CDLCLOSINGMARUBOZU, CDLCONCEALBABYSWALL, CDLCOUNTERATTACK, CDLDARKCLOUDCOVER, CDLDOJI, CDLDOJISTAR, CDLDRAGONFLYDOJI, CDLENGULFING, CDLEVENINGDOJISTAR, CDLEVENINGSTAR, CDLGAPSIDESIDEWHITE, CDLGRAVESTONEDOJI, CDLHAMMER, CDLHANGINGMAN, CDLHARAMI, CDLHARAMICROSS, CDLHIGHWAVE, CDLHIKKAKE, CDLHIKKAKEMOD, CDLHOMINGPIGEON, CDLIDENTICAL3CROWS, CDLINNECK, CDLINVERTEDHAMMER, CDLKICKING, CDLKICKINGBYLENGTH, CDLLADDERBOTTOM, CDLLONGLEGGEDDOJI, CDLLONGLINE, CDLMARUBOZU, CDLMATCHINGLOW, CDLMATHOLD, CDLMORNINGDOJISTAR, CDLMORNINGSTAR, CDLONNECK, CDLPIERCING, CDLRICKSHAWMAN, CDLRISEFALL3METHODS, CDLSEPARATINGLINES, CDLSHOOTINGSTAR, CDLSHORTLINE, CDLSPINNINGTOP, CDLSTALLEDPATTERN, CDLSTICKSANDWICH, CDLTAKURI, CDLTASUKIGAP, CDLTHRUSTING, CDLTRISTAR, CDLUNIQUE3RIVER, CDLUPSIDEGAP2CROWS, CDLXSIDEGAP3METHODS

Performance

pytafast achieves near-parity with the official ta-lib-python — both wrap the same underlying TA-Lib C library. Benchmarked across 62 indicators on 100,000 data points:

Category	Indicators	Avg Speedup
Overlap Studies	SMA, EMA, BBANDS, DEMA, KAMA, TEMA, TRIMA, T3, SAR, MIDPOINT	1.03x
Momentum	RSI, MACD, ADX, CCI, STOCH, ROC, MOM, WILLR, PPO, BOP, MFI…	1.03x
Volatility	ATR, NATR, TRANGE	0.99x
Volume	OBV, AD, ADOSC	1.00x
Price Transform	AVGPRICE, MEDPRICE, TYPPRICE, WCLPRICE	0.98x
Statistics	STDDEV, BETA, CORREL, LINEARREG, VAR, TSF	1.07x
Math Operators	ADD, SUB, MULT, DIV	0.97x
Math Transforms	SIN, COS, SQRT, LN, EXP	1.00x
Cycle	HT_DCPERIOD, HT_DCPHASE, HT_TRENDLINE, HT_TRENDMODE	1.01x
Candlestick	CDLENGULFING, CDLDOJI, CDL3WHITESOLDIERS, CDLHAMMER…	0.97x

Overall: 39/62 indicators equal or faster · Average speedup 1.01x · Notable: BETA 1.42x, BBANDS 1.27x, CCI 1.13x

pytafast's key advantages are pandas native support, async capabilities, and GIL release for true parallelism — not raw single-call speed (both use the same C core).

See BENCHMARK_RESULTS.md for full details.

API Compatibility

pytafast follows the same function signatures as the official TA-Lib Python wrapper, making it a drop-in replacement in most cases.

License

This project is licensed under the MIT License - see the LICENSE file for details.

This project includes and statically links to the TA-Lib C library, which is distributed under a BSD License. Copyright (c) 1999-2025, Mario Fortier. See third_party/ta-lib/LICENSE for details.