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# TA - Technical Analysis Library
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A comprehensive Python library for feature engineering from financial time series datasets (Open, High, Low, Close, Volume). Built on Pandas and NumPy, the TA library provides 43 technical indicators across five categories: Volume, Volatility, Trend, Momentum, and Others, offering both class-based and functional APIs for each indicator.
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## Package Information
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- **Package Name**: ta
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- **Language**: Python
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- **Installation**: `pip install ta`
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- **Dependencies**: numpy, pandas
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## Core Imports
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```python
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import ta
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```
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For adding all features at once:
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```python
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from ta import add_all_ta_features, add_volume_ta, add_volatility_ta, add_trend_ta, add_momentum_ta, add_others_ta
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```
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For individual indicators:
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```python
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from ta.volume import OnBalanceVolumeIndicator, MFIIndicator
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from ta.trend import MACD, EMAIndicator
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from ta.momentum import RSIIndicator, StochasticOscillator
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```
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## Basic Usage
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```python
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import pandas as pd
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import ta
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# Load your financial data with OHLCV columns
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df = pd.DataFrame({
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    'Open': [100, 101, 102, 99, 98],
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    'High': [105, 103, 104, 101, 100],
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    'Low': [99, 100, 101, 98, 97],
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    'Close': [104, 102, 103, 100, 99],
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    'Volume': [1000, 1100, 900, 1200, 800]
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})
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# Add all technical analysis features at once
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df_with_indicators = ta.add_all_ta_features(
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    df, 
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    open="Open", 
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    high="High", 
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    low="Low", 
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    close="Close", 
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    volume="Volume"
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)
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# Add specific category of indicators
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df_trend = ta.add_trend_ta(df, high="High", low="Low", close="Close")
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# Use individual indicators (class-based approach)
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from ta.momentum import RSIIndicator
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rsi = RSIIndicator(close=df['Close'], window=14)
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df['RSI'] = rsi.rsi()
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# Use individual indicators (functional approach)
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from ta.momentum import rsi
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df['RSI'] = rsi(close=df['Close'], window=14)
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```
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## Architecture
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The TA library follows a consistent design pattern across all indicators:
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- **Wrapper Functions**: High-level functions for adding multiple indicators at once
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- **Class-Based API**: Each indicator implemented as a class inheriting from `IndicatorMixin`
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- **Functional API**: Standalone functions that wrap class implementations
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- **Consistent Interface**: All indicators support `fillna` parameter for handling NaN values
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- **Pandas Integration**: Seamless integration with pandas DataFrames and Series
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This dual API approach provides flexibility for different use cases while maintaining consistency across all 43 indicators.
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## Capabilities
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### Wrapper Functions
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High-level functions for batch adding technical analysis features to DataFrames, supporting all 43 indicators organized by category.
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```python { .api }
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def add_all_ta_features(df, open, high, low, close, volume, fillna=False, colprefix="", vectorized=False): ...
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def add_volume_ta(df, high, low, close, volume, fillna=False, colprefix="", vectorized=False): ...
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def add_volatility_ta(df, high, low, close, fillna=False, colprefix="", vectorized=False): ...
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def add_trend_ta(df, high, low, close, fillna=False, colprefix="", vectorized=False): ...
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def add_momentum_ta(df, high, low, close, volume, fillna=False, colprefix="", vectorized=False): ...
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def add_others_ta(df, close, fillna=False, colprefix=""): ...
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```
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[Wrapper Functions](./wrapper-functions.md)
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### Volume Indicators
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Nine volume-based technical indicators that analyze the relationship between price movements and trading volume to identify buying/selling pressure and confirm price trends.
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```python { .api }
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class AccDistIndexIndicator: ...
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class OnBalanceVolumeIndicator: ...
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class ChaikinMoneyFlowIndicator: ...
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class ForceIndexIndicator: ...
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class EaseOfMovementIndicator: ...
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class VolumePriceTrendIndicator: ...
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class NegativeVolumeIndexIndicator: ...
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class MFIIndicator: ...
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class VolumeWeightedAveragePrice: ...
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```
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[Volume Indicators](./volume-indicators.md)
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### Volatility Indicators
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Five volatility-based indicators that measure price volatility and identify potential support/resistance levels through channel analysis and volatility bands.
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```python { .api }
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class AverageTrueRange: ...
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class BollingerBands: ...
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class KeltnerChannel: ...
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class DonchianChannel: ...
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class UlcerIndex: ...
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```
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[Volatility Indicators](./volatility-indicators.md)
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### Trend Indicators
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Twenty trend-based indicators that identify trend direction, strength, and potential reversal points through moving averages, oscillators, and directional movement analysis.
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```python { .api }
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class AroonIndicator: ...
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class MACD: ...
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class EMAIndicator: ...
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class SMAIndicator: ...
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class WMAIndicator: ...
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class TRIXIndicator: ...
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class MassIndex: ...
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class IchimokuIndicator: ...
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class KSTIndicator: ...
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class DPOIndicator: ...
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class CCIIndicator: ...
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class ADXIndicator: ...
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class VortexIndicator: ...
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class PSARIndicator: ...
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class STCIndicator: ...
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```
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[Trend Indicators](./trend-indicators.md)
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### Momentum Indicators
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Eleven momentum-based indicators that measure the rate of price change and identify overbought/oversold conditions, momentum shifts, and potential reversal points.
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```python { .api }
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class RSIIndicator: ...
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class TSIIndicator: ...
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class UltimateOscillator: ...
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class StochasticOscillator: ...
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class KAMAIndicator: ...
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class ROCIndicator: ...
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class AwesomeOscillatorIndicator: ...
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class WilliamsRIndicator: ...
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class StochRSIIndicator: ...
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class PercentagePriceOscillator: ...
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class PercentageVolumeOscillator: ...
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```
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[Momentum Indicators](./momentum-indicators.md)
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### Others Indicators
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Three fundamental financial analysis indicators for calculating various types of returns from price data.
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```python { .api }
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class DailyReturnIndicator: ...
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class DailyLogReturnIndicator: ...
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class CumulativeReturnIndicator: ...
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```
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[Others Indicators](./others-indicators.md)
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### Utility Functions
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Core utility functions and base classes used throughout the library for data handling and indicator calculations.
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```python { .api }
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class IndicatorMixin: ...
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def dropna(df): ...
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```
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[Utilities](./utilities.md)
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## Types
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```python { .api }
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# Base class for all indicators
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class IndicatorMixin:
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    def _check_fillna(self, series, value=0):
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        """Check if fillna flag is True"""
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        ...
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    @staticmethod
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    def _true_range(high, low, prev_close):
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        """Calculate true range"""
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        ...
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```