tacuda 0.7.1

Python bindings for tacuda: full TA-Lib coverage (161 functions) on CUDA, incl. MACDEXT and 19 vector math kernels, device pipeline support, structured OHLCV inputs, batched multi-symbol execution, and runtime dtype selection (float16/float32/float64)

TaCuda — Python Bindings

Python package for the native tacuda CUDA library. Full TA-Lib coverage (161 functions): indicators, MACDEXT, 19 vector math kernels, candlestick patterns, plus typed core APIs with runtime dtype selection and the zero-copy device API.

Prerequisites

• NVIDIA GPU (Compute Capability >= 6.0)
• CUDA Toolkit 11.x or 12.x
• Built tacuda shared library (tacuda.dll / libtacuda.so / libtacuda.dylib)
• Python >= 3.8, NumPy >= 1.20

Package Scope

• High-level NumPy wrappers for indicator functions
• Precision-aware core indicators (sma, ema, rsi, macd, atr, bbands) via dtype="float16|float32|float64"
• OHLCV convenience container and input validation helpers

Installation

pip install tacuda

Note: The tacuda native library must be built separately and placed where the Python bindings can find it. See "Library Location" below.

Quick Start

import numpy as np
from tacuda import sma, ema, rsi, macd, bbands, OHLCV

# Generate sample data
prices = np.random.randn(100_000).cumsum().astype(np.float32) + 100

# Moving averages
sma20 = sma(prices, 20)
ema12 = ema(prices, 12)

# RSI
rsi14 = rsi(prices, 14)

# MACD (returns macd_line, signal, histogram)
macd_line, signal, hist = macd(prices, 12, 26, 9)

# Bollinger Bands (returns upper, middle, lower)
upper, middle, lower = bbands(prices, 20, 2.0, 2.0)

# OHLCV container
ohlcv = OHLCV.from_columns(open_arr, high_arr, low_arr, close_arr, volume_arr)

MACDEXT and vector math

import tacuda

# Independent MA types per line (0=SMA 1=EMA 2=WMA ... 8=T3)
ext_macd, ext_signal, ext_hist = tacuda.macdext(
    prices, fastPeriod=12, fastType=1, slowPeriod=26, slowType=0,
    signalPeriod=9, signalType=2)

# Element-wise GPU kernels
log_prices = tacuda.ln(prices)
spread = tacuda.sub(high_arr, low_arr)

Zero-copy device pipeline

n = len(prices)
d_in = tacuda.device_alloc(n)
d_out = tacuda.device_alloc(n)
try:
    tacuda.device_upload(prices, d_in, n)   # 1 PCIe transfer in
    tacuda.sma_d(d_in, d_out, n, 20)        # compute on GPU
    tacuda.sin_d(d_out, d_out, n)           # chain kernels, zero copies
    tacuda.device_sync()
    result = tacuda.device_download(d_out, n)  # 1 PCIe transfer out
finally:
    tacuda.device_free(d_in)
    tacuda.device_free(d_out)

Available Indicators (full TA-Lib coverage)

Category	Indicators
Moving Averages	SMA, EMA, WMA, DEMA, TEMA, TRIMA, KAMA, T3, MAMA, MAVP, MA
Momentum	RSI, MACD, MACDEXT, MACDFIX, Stochastic, StochF, StochRSI, CCI, CMO, ROC, ROCP, ROCR, ROCR100, MOM, WILLR, APO, PPO, PVO, TRIX, ULTOSC, BOP
Volatility	ATR, NATR, TRANGE, BBANDS, ACCBANDS, StdDev, VAR
Trend	ADX, ADXR, DX, +DI, -DI, +DM, -DM, SAR, SAREXT, Aroon, AroonOsc
Volume	AD, ADOSC, OBV, MFI, IMI, NVI, PVI
Statistical	LINEARREG, SLOPE, INTERCEPT, ANGLE, TSF, CORREL, BETA, AVGDEV, SUM, MAX, MIN
Price Transform	AVGPRICE, MEDPRICE, TYPPRICE, WCLPRICE, MIDPRICE
Hilbert Transform	HT_DCPERIOD, HT_DCPHASE, HT_PHASOR, HT_SINE, HT_TRENDLINE, HT_TRENDMODE
Vector Math (19)	SIN, COS, TAN, ASIN, ACOS, ATAN, SINH, COSH, TANH, EXP, LN, LOG10, SQRT, CEIL, FLOOR, ADD, SUB, MULT, DIV
Candlestick	63 patterns (Doji, Hammer, Engulfing, MorningStar, etc.)
Device API	ct_*_d variants of the above + device_alloc/upload/download/sync/free and buffer-pool controls

Library Location

The bindings search for the native library in this order:

1. TACUDA_LIBRARY environment variable (full path to shared library)
2. TACUDA_LIBRARY_PATH / TACUDA_LIBRARY_DIR + library name
3. System library path (ctypes.util.find_library)
4. Common build directories relative to the package

Regenerating Metadata

When the C header changes, regenerate from the repository root:

python bindings/generate_bindings.py

Build & Publish (PyPI)

cd bindings/python
python -m build
python -m twine check dist/*
python -m twine upload dist/*

Changelog

Canonical release history is maintained in CHANGELOG.md.

License

Apache License 2.0 — see LICENSE.