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tessl/pypi-ta

Technical Analysis Library in Python for financial time series datasets with 43 indicators across Volume, Volatility, Trend, Momentum, and Others categories

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tessl
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pypipkg:pypi/ta@0.11.x

To install, run

npx @tessl/cli install tessl/pypi-ta@0.11.0

index.mddocs/

TA - Technical Analysis Library

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.

Package Information

  • Package Name: ta
  • Language: Python
  • Installation: pip install ta
  • Dependencies: numpy, pandas

Core Imports

import ta

For adding all features at once:

from ta import add_all_ta_features, add_volume_ta, add_volatility_ta, add_trend_ta, add_momentum_ta, add_others_ta

For individual indicators:

from ta.volume import OnBalanceVolumeIndicator, MFIIndicator
from ta.trend import MACD, EMAIndicator
from ta.momentum import RSIIndicator, StochasticOscillator

Basic Usage

import pandas as pd
import ta

# Load your financial data with OHLCV columns
df = pd.DataFrame({
    'Open': [100, 101, 102, 99, 98],
    'High': [105, 103, 104, 101, 100],
    'Low': [99, 100, 101, 98, 97],
    'Close': [104, 102, 103, 100, 99],
    'Volume': [1000, 1100, 900, 1200, 800]
})

# Add all technical analysis features at once
df_with_indicators = ta.add_all_ta_features(
    df, 
    open="Open", 
    high="High", 
    low="Low", 
    close="Close", 
    volume="Volume"
)

# Add specific category of indicators
df_trend = ta.add_trend_ta(df, high="High", low="Low", close="Close")

# Use individual indicators (class-based approach)
from ta.momentum import RSIIndicator

rsi = RSIIndicator(close=df['Close'], window=14)
df['RSI'] = rsi.rsi()

# Use individual indicators (functional approach)
from ta.momentum import rsi

df['RSI'] = rsi(close=df['Close'], window=14)

Architecture

The TA library follows a consistent design pattern across all indicators:

  • Wrapper Functions: High-level functions for adding multiple indicators at once
  • Class-Based API: Each indicator implemented as a class inheriting from IndicatorMixin
  • Functional API: Standalone functions that wrap class implementations
  • Consistent Interface: All indicators support fillna parameter for handling NaN values
  • Pandas Integration: Seamless integration with pandas DataFrames and Series

This dual API approach provides flexibility for different use cases while maintaining consistency across all 43 indicators.

Capabilities

Wrapper Functions

High-level functions for batch adding technical analysis features to DataFrames, supporting all 43 indicators organized by category.

def add_all_ta_features(df, open, high, low, close, volume, fillna=False, colprefix="", vectorized=False): ...
def add_volume_ta(df, high, low, close, volume, fillna=False, colprefix="", vectorized=False): ...
def add_volatility_ta(df, high, low, close, fillna=False, colprefix="", vectorized=False): ...
def add_trend_ta(df, high, low, close, fillna=False, colprefix="", vectorized=False): ...
def add_momentum_ta(df, high, low, close, volume, fillna=False, colprefix="", vectorized=False): ...
def add_others_ta(df, close, fillna=False, colprefix=""): ...

Wrapper Functions

Volume Indicators

Nine volume-based technical indicators that analyze the relationship between price movements and trading volume to identify buying/selling pressure and confirm price trends.

class AccDistIndexIndicator: ...
class OnBalanceVolumeIndicator: ...
class ChaikinMoneyFlowIndicator: ...
class ForceIndexIndicator: ...
class EaseOfMovementIndicator: ...
class VolumePriceTrendIndicator: ...
class NegativeVolumeIndexIndicator: ...
class MFIIndicator: ...
class VolumeWeightedAveragePrice: ...

Volume Indicators

Volatility Indicators

Five volatility-based indicators that measure price volatility and identify potential support/resistance levels through channel analysis and volatility bands.

class AverageTrueRange: ...
class BollingerBands: ...
class KeltnerChannel: ...
class DonchianChannel: ...
class UlcerIndex: ...

Volatility Indicators

Trend Indicators

Twenty trend-based indicators that identify trend direction, strength, and potential reversal points through moving averages, oscillators, and directional movement analysis.

class AroonIndicator: ...
class MACD: ...
class EMAIndicator: ...
class SMAIndicator: ...
class WMAIndicator: ...
class TRIXIndicator: ...
class MassIndex: ...
class IchimokuIndicator: ...
class KSTIndicator: ...
class DPOIndicator: ...
class CCIIndicator: ...
class ADXIndicator: ...
class VortexIndicator: ...
class PSARIndicator: ...
class STCIndicator: ...

Trend Indicators

Momentum Indicators

Eleven momentum-based indicators that measure the rate of price change and identify overbought/oversold conditions, momentum shifts, and potential reversal points.

class RSIIndicator: ...
class TSIIndicator: ...
class UltimateOscillator: ...
class StochasticOscillator: ...
class KAMAIndicator: ...
class ROCIndicator: ...
class AwesomeOscillatorIndicator: ...
class WilliamsRIndicator: ...
class StochRSIIndicator: ...
class PercentagePriceOscillator: ...
class PercentageVolumeOscillator: ...

Momentum Indicators

Others Indicators

Three fundamental financial analysis indicators for calculating various types of returns from price data.

class DailyReturnIndicator: ...
class DailyLogReturnIndicator: ...
class CumulativeReturnIndicator: ...

Others Indicators

Utility Functions

Core utility functions and base classes used throughout the library for data handling and indicator calculations.

class IndicatorMixin: ...
def dropna(df): ...

Utilities

Types

# Base class for all indicators
class IndicatorMixin:
    def _check_fillna(self, series, value=0):
        """Check if fillna flag is True"""
        ...
    
    @staticmethod
    def _true_range(high, low, prev_close):
        """Calculate true range"""
        ...