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# TA-Lib
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TA-Lib is a comprehensive Python wrapper for the TA-LIB C library, providing 175+ technical analysis indicators for financial market data analysis. This library is widely used by trading software developers requiring professional-grade technical analysis of financial data, offering efficient computations that are 2-4 times faster than alternative implementations.
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## Package Information
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- **Package Name**: TA-Lib
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- **Language**: Python
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- **Installation**: `pip install TA-Lib`
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- **Dependencies**: numpy (required), pandas (optional), polars (optional)
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## Core Imports
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```python
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import talib
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```
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For abstract interface:
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```python
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import talib.abstract
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from talib.abstract import Function
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```
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For streaming API:
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```python
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import talib.stream
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from talib import stream
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```
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## Basic Usage
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```python
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import talib
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import numpy as np
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# Create sample OHLC data
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close = np.random.random(100)
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high = np.random.random(100)
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low = np.random.random(100)
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open_prices = np.random.random(100)
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# Calculate simple moving average
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sma = talib.SMA(close, timeperiod=30)
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# Calculate RSI
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rsi = talib.RSI(close, timeperiod=14)
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# Calculate Bollinger Bands
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upper, middle, lower = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2)
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# Calculate MACD
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macd, macdsignal, macdhist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
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```
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## Architecture
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TA-Lib provides three distinct APIs for different use cases:
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- **Function API**: Direct function calls for batch processing of arrays
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- **Abstract API**: Flexible interface supporting DataFrames and named inputs  
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- **Streaming API**: Real-time processing returning only the latest value
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All functions support numpy arrays as primary input, with automatic conversion from pandas Series and polars Series. The library handles initialization and cleanup automatically, with robust NaN propagation and lookback period management.
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## Capabilities
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### Overlap Studies
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Trend-following indicators and moving averages that smooth price data to identify trends and provide support/resistance levels. Includes moving averages, Bollinger Bands, and trend line indicators.
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```python { .api }
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def SMA(real, timeperiod=30): ...
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def EMA(real, timeperiod=30): ...
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def BBANDS(real, timeperiod=5, nbdevup=2, nbdevdn=2, matype=MA_Type.SMA): ...
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def KAMA(real, timeperiod=30): ...
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def SAR(high, low, acceleration=0, maximum=0): ...
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```
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[Overlap Studies](./overlap-studies.md)
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### Momentum Indicators  
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Oscillators and momentum-based indicators that measure the rate of price change and help identify overbought/oversold conditions and potential reversal points.
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```python { .api }
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def RSI(real, timeperiod=14): ...
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def MACD(real, fastperiod=12, slowperiod=26, signalperiod=9): ...
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def STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=MA_Type.SMA, slowd_period=3, slowd_matype=MA_Type.SMA): ...
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def ADX(high, low, close, timeperiod=14): ...
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def CCI(high, low, close, timeperiod=14): ...
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def IMI(open, close, timeperiod=14): ...
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```
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[Momentum Indicators](./momentum-indicators.md)
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### Volume Indicators
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Indicators that analyze the relationship between price movements and trading volume to confirm trends and identify potential reversals.
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```python { .api }
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def AD(high, low, close, volume): ...
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def OBV(close, volume): ...
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def ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10): ...
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```
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[Volume Indicators](./volume-indicators.md)
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### Volatility Indicators
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Measures of price volatility and range to assess market conditions and potential breakout scenarios.
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```python { .api }
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def ATR(high, low, close, timeperiod=14): ...
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def NATR(high, low, close, timeperiod=14): ...
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def TRANGE(high, low, close): ...
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def ACCBANDS(high, low, close, timeperiod=20): ...
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```
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[Volatility Indicators](./volatility-indicators.md)
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### Pattern Recognition
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Candlestick pattern recognition functions that identify traditional Japanese candlestick patterns for technical analysis and trading signals.
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```python { .api }
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def CDLDOJI(open, high, low, close): ...
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def CDLHAMMER(open, high, low, close): ...
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def CDLENGULFING(open, high, low, close): ...
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def CDLMORNINGSTAR(open, high, low, close, penetration=0): ...
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def CDL3BLACKCROWS(open, high, low, close): ...
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```
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[Pattern Recognition](./pattern-recognition.md)
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### Price Transform
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Functions that transform OHLC data into standardized price representations for further analysis.
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```python { .api }
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def AVGPRICE(open, high, low, close): ...
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def MEDPRICE(high, low): ...
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def TYPPRICE(high, low, close): ...
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def WCLPRICE(high, low, close): ...
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```
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[Price Transform](./price-transform.md)
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### Cycle Indicators
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Hilbert Transform-based indicators for cycle analysis and trend vs cycle mode determination.
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```python { .api }
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def HT_DCPERIOD(real): ...
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def HT_SINE(real): ...
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def HT_TRENDMODE(real): ...
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def HT_PHASOR(real): ...
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def HT_DCPHASE(real): ...
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```
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[Cycle Indicators](./cycle-indicators.md)
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### Statistical Functions
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Statistical analysis functions for correlation, regression, and statistical measures of price data.
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```python { .api }
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def BETA(real0, real1, timeperiod=5): ...
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def CORREL(real0, real1, timeperiod=30): ...
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def LINEARREG(real, timeperiod=14): ...
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def STDDEV(real, timeperiod=5, nbdev=1): ...
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def VAR(real, timeperiod=5, nbdev=1): ...
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def AVGDEV(real, timeperiod=14): ...
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```
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[Statistical Functions](./statistical-functions.md)
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### Math Operations
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Mathematical transformation and operator functions for array manipulation and calculations.
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```python { .api }
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def ADD(real0, real1): ...
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def SUM(real, timeperiod=30): ...
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def MAX(real, timeperiod=30): ...
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def MIN(real, timeperiod=30): ...
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def SQRT(real): ...
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def SIN(real): ...
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def LOG10(real): ...
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```
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[Math Operations](./math-operations.md)
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### Abstract and Streaming APIs
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Alternative interfaces providing flexible DataFrame support and real-time processing capabilities.
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```python { .api }
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# Abstract API
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class Function:
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    def __init__(self, function_name, *args, **kwargs): ...
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# Streaming functions return single values
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def stream_SMA(real, timeperiod=30): ...  # Returns latest SMA value only
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```
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[Abstract and Streaming APIs](./abstract-streaming.md)
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## Common Types
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```python { .api }
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from enum import Enum
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from typing import Tuple
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import numpy as np
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class MA_Type(Enum):
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    """Moving Average Types"""
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    SMA = 0    # Simple Moving Average
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    EMA = 1    # Exponential Moving Average  
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    WMA = 2    # Weighted Moving Average
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    DEMA = 3   # Double Exponential Moving Average
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    TEMA = 4   # Triple Exponential Moving Average
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    TRIMA = 5  # Triangular Moving Average
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    KAMA = 6   # Kaufman Adaptive Moving Average
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    MAMA = 7   # MESA Adaptive Moving Average
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    T3 = 8     # Triple Exponential Moving Average (T3)
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# Type aliases
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NDArray = np.ndarray
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PriceArray = np.ndarray[np.float64]
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IntArray = np.ndarray[np.int32]
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```
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## Utility Functions
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```python { .api }
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def get_functions() -> list[str]:
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    """Returns a list of all supported TA-Lib function names"""
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def get_function_groups() -> dict[str, list[str]]:
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    """Returns functions organized by group (e.g., 'Overlap Studies', 'Momentum Indicators')"""
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def set_unstable_period(func_id: int, period: int) -> None:
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    """Set unstable period for a function"""
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def get_unstable_period(func_id: int) -> int:
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    """Get unstable period for a function"""
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def set_compatibility(value: int) -> None:
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    """Set compatibility mode"""
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def get_compatibility() -> int:
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    """Get compatibility mode"""
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```