Implementation:TA Lib Ta lib python Wrapper Price Data Input

Knowledge Sources	ta-lib-python NumPy Documentation
Domains	Data_Preparation, Technical_Analysis
Last Updated	2026-02-09 22:00 GMT

Overview

Concrete pattern for preparing price data as numpy float64 arrays, with transparent pandas/polars Series support via the _wrapper decorator.

Description

The _wrapper decorator in talib/__init__.py implements transparent type conversion for all TA-Lib indicator functions. When a pandas Series or polars Series is passed as input, the wrapper:

Detects the input type (pandas, polars, or numpy)
Converts to float64 numpy arrays
Calls the underlying C function
Converts output back to the original type (preserving pandas index)

This is a Pattern Doc — it documents the input data preparation pattern rather than a single API call.

Usage

Use this pattern when passing data to any TA-Lib indicator function. The wrapper is applied automatically to all 161 indicator functions and all streaming functions at module import time.

Code Reference

Source Location

Repository: ta-lib-python
File: talib/__init__.py
Lines: L1-106 (pandas/polars detection and _wrapper decorator)

Signature

# Input preparation patterns (not a single function signature)

# Pattern 1: Direct numpy array
import numpy as np
close = np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype=float)

# Pattern 2: Pandas Series (auto-converted by _wrapper)
import pandas as pd
close = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0], index=pd.date_range('2024-01-01', periods=5))

# Pattern 3: Polars Series (auto-converted by _wrapper)
import polars as pl
close = pl.Series([1.0, 2.0, 3.0, 4.0, 5.0])

Import

import numpy as np
import talib

I/O Contract

Inputs

Name	Type	Required	Description
price_data	np.ndarray / pd.Series / pl.Series	Yes	Price data (OHLCV components). Must be numeric (float64).
dtype	type	No	Must be float64. Integer arrays raise exceptions.

Outputs

Name	Type	Description
prepared_data	np.ndarray (float64)	Contiguous float64 array ready for C function consumption

Usage Examples

NumPy Array Input

import numpy as np
import talib

# Create float64 price data
close = np.random.random(100)

# Pass directly to indicator function
sma = talib.SMA(close, timeperiod=20)

Pandas Series Input

import pandas as pd
import talib

# Create pandas Series with datetime index
dates = pd.date_range('2024-01-01', periods=100)
close = pd.Series(np.random.random(100), index=dates)

# _wrapper auto-converts and preserves index
sma = talib.SMA(close, timeperiod=20)
# sma is a pd.Series with the same index

Polars Series Input

import polars as pl
import talib

close = pl.Series("close", [1.0, 2.0, 3.0, 4.0, 5.0] * 20)

# _wrapper auto-converts to numpy, computes, converts back
sma = talib.SMA(close, timeperiod=5)
# sma is a pl.Series

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