Implementation:TA Lib Ta lib python Indicator Function API

Knowledge Sources	ta-lib-python TA-Lib C Library
Domains	Technical_Analysis, Quantitative_Finance
Last Updated	2026-02-09 22:00 GMT

Overview

Concrete tool for computing technical analysis indicators via 161 direct function calls provided by the TA-Lib Python wrapper.

Description

The Function API is the primary interface to TA-Lib. Each of the 161 technical indicators is exposed as a top-level function in the talib module. These functions are Cython wrappers around the TA-Lib C library, auto-generated from C header definitions. They accept numpy arrays (or pandas/polars Series via the _wrapper decorator) and return numpy arrays with NaN-filled lookback periods.

Representative functions include:

talib.SMA — Simple Moving Average
talib.BBANDS — Bollinger Bands (returns 3 arrays)
talib.RSI — Relative Strength Index
talib.MACD — Moving Average Convergence/Divergence (returns 3 arrays)
talib.ATR — Average True Range (requires high, low, close)

Usage

Import this when you need to compute any standard technical analysis indicator with a direct function call. This is the simplest API — pass arrays in, get arrays out.

Code Reference

Source Location

Repository: ta-lib-python
File: talib/_func.pxi
Lines: L1-5324 (all 161 indicator function Cython bindings)
Wrapped by: talib/__init__.py:L122-126 (pandas/polars wrapper application)

Signature

# Single-input, single-output indicators
def SMA(real: np.ndarray, timeperiod: int = 30) -> np.ndarray:
    """Simple Moving Average"""

def RSI(real: np.ndarray, timeperiod: int = 14) -> np.ndarray:
    """Relative Strength Index"""

# Single-input, multi-output indicators
def BBANDS(real: np.ndarray, timeperiod: int = 5,
           nbdevup: float = 2.0, nbdevdn: float = 2.0,
           matype: int = 0) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
    """Bollinger Bands - returns (upperband, middleband, lowerband)"""

def MACD(real: np.ndarray, fastperiod: int = 12,
         slowperiod: int = 26, signalperiod: int = 9) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
    """MACD - returns (macd, macdsignal, macdhist)"""

# Multi-input indicators
def ATR(high: np.ndarray, low: np.ndarray, close: np.ndarray,
        timeperiod: int = 14) -> np.ndarray:
    """Average True Range"""

Import

import talib
# All 161 functions available as talib.SMA, talib.RSI, etc.

I/O Contract

Inputs

Name	Type	Required	Description
real	np.ndarray (float64)	Yes (most indicators)	Single price series (typically close prices)
high	np.ndarray (float64)	Yes (some indicators)	High prices (for ATR, STOCH, CDL patterns, etc.)
low	np.ndarray (float64)	Yes (some indicators)	Low prices
close	np.ndarray (float64)	Yes (some indicators)	Close prices
volume	np.ndarray (float64)	Yes (volume indicators)	Volume data
timeperiod	int	No	Lookback window (default varies per function)
matype	int	No	Moving average type (0=SMA, 1=EMA, etc. via MA_Type)

Outputs

Name	Type	Description
result	np.ndarray (float64)	Single-output indicators (SMA, RSI, ATR)
result_tuple	Tuple[np.ndarray, ...]	Multi-output indicators (BBANDS returns 3, MACD returns 3)

Usage Examples

Simple Moving Average

import numpy as np
import talib

close = np.random.random(100)

# Compute 20-period SMA
sma = talib.SMA(close, timeperiod=20)
# sma[0:19] are NaN (lookback period), sma[19:] contain values

Bollinger Bands (Multi-Output)

upper, middle, lower = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)

RSI and MACD

rsi = talib.RSI(close, timeperiod=14)
macd, signal, hist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)

Multi-Input: Average True Range

high = np.random.random(100) + 1
low = np.random.random(100)
close = np.random.random(100) + 0.5

atr = talib.ATR(high, low, close, timeperiod=14)

Related Pages

Implements Principle

Principle:TA_Lib_Ta_lib_python_Technical_Indicator_Computation

Requires Environment

Uses Heuristic

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