pandas-ta for Trading

A feature-rich technical analysis library that extends pandas DataFrames with 130+ indicators, candlestick patterns, and performance metrics. The go-to library for efficient indicator calculations.

Difficulty: Beginner

Category: Technical Analysis

📊 130+ Indicators

Installation

Install pandas-ta with pip. It requires pandas and numpy which will be installed automatically.

# Install pandas-ta

$ pip install pandas-ta

# Install with data sources

$ pip install pandas-ta yfinance

Available Indicators

Overlap

SMA, EMA, WMA, DEMA, TEMA, VWAP, Bollinger Bands, Keltner Channels

Momentum

RSI, MACD, Stochastic, CCI, Williams %R, ROC, Ultimate Oscillator

Trend

ADX, Aroon, PSAR, Supertrend, Ichimoku, Pivot Points

Volatility

ATR, Bollinger Width, Keltner Width, Donchian Channels

Volume

OBV, CMF, MFI, VWAP, AD, Force Index

Candles

Doji, Hammer, Engulfing, Morning Star, Evening Star, Harami

Key Features

130+ Indicators

Comprehensive library covering momentum, trend, volatility, volume, and overlap indicators.

Native pandas Integration

Works directly with pandas DataFrames using df.ta syntax for seamless workflow.

Strategy Builder

Create custom indicator strategies and apply multiple indicators at once.

Performance Metrics

Built-in functions for returns, drawdown, and other trading performance analytics.

Code Examples

Add Multiple Indicators at Once

Apply common indicators with a single line

Python

import pandas as pd

import pandas_ta as ta

import yfinance as yf

# Download data

df = yf.download('EURUSD=X', period='1y', interval='1d')

# Add single indicator

df['RSI_14'] = ta.rsi(df['Close'], length=14)

df['SMA_50'] = ta.sma(df['Close'], length=50)

df['EMA_20'] = ta.ema(df['Close'], length=20)

# Or add multiple indicators using strategy

df.ta.strategy('AllSMA') # Adds all SMA variations

print(df.tail())

Create Custom Indicator Strategy

Define your own indicator combinations

Python

import pandas_ta as ta

# Create a custom strategy

CustomStrategy = ta.Strategy(

name="Forex Strategy",

description="Custom forex trading indicators",

ta=[

{"kind": "sma", "length": 20},

{"kind": "sma", "length": 50},

{"kind": "sma", "length": 200},

{"kind": "rsi", "length": 14},

{"kind": "macd", "fast": 12, "slow": 26, "signal": 9},

{"kind": "bbands", "length": 20, "std": 2},

{"kind": "atr", "length": 14},

{"kind": "adx", "length": 14},

]

)

# Apply strategy to dataframe

df.ta.strategy(CustomStrategy)

# Check new columns

print("Added columns:", [col for col in df.columns if col not in ['Open','High','Low','Close','Volume']])

Momentum Indicators Suite

RSI, MACD, Stochastic, and more

Python

import pandas_ta as ta

# RSI with overbought/oversold signals

df['RSI'] = ta.rsi(df['Close'], length=14)

df['RSI_Signal'] = 'Neutral'

df.loc[df['RSI'] > 70, 'RSI_Signal'] = 'Overbought'

df.loc[df['RSI'] < 30, 'RSI_Signal'] = 'Oversold'

# MACD

macd = ta.macd(df['Close'], fast=12, slow=26, signal=9)

df = pd.concat([df, macd], axis=1)

# Stochastic Oscillator

stoch = ta.stoch(df['High'], df['Low'], df['Close'], k=14, d=3)

df = pd.concat([df, stoch], axis=1)

# Williams %R

df['WILLR'] = ta.willr(df['High'], df['Low'], df['Close'], length=14)

# CCI - Commodity Channel Index

df['CCI'] = ta.cci(df['High'], df['Low'], df['Close'], length=20)

print(df[['Close', 'RSI', 'MACD_12_26_9', 'STOCHk_14_3_3', 'WILLR_14', 'CCI_20']].tail())

Trend & Volatility Indicators

ADX, ATR, Bollinger Bands, and more

Python

import pandas_ta as ta

# ADX - Average Directional Index

adx = ta.adx(df['High'], df['Low'], df['Close'], length=14)

df = pd.concat([df, adx], axis=1)

# ATR - Average True Range

df['ATR'] = ta.atr(df['High'], df['Low'], df['Close'], length=14)

# Bollinger Bands

bbands = ta.bbands(df['Close'], length=20, std=2)

df = pd.concat([df, bbands], axis=1)

# Keltner Channels

kc = ta.kc(df['High'], df['Low'], df['Close'], length=20)

df = pd.concat([df, kc], axis=1)

# Donchian Channels

donchian = ta.donchian(df['High'], df['Low'], lower_length=20, upper_length=20)

df = pd.concat([df, donchian], axis=1)

# Identify trend strength

df['Trend_Strength'] = 'Weak'

df.loc[df['ADX_14'] > 25, 'Trend_Strength'] = 'Strong'

df.loc[df['ADX_14'] > 50, 'Trend_Strength'] = 'Very Strong'

print(df[['Close', 'ADX_14', 'ATR_14', 'BBU_20_2.0', 'BBL_20_2.0', 'Trend_Strength']].tail())

Generate Trading Signals

Combine indicators for entry/exit signals

Python

import pandas_ta as ta

import numpy as np

# Calculate indicators

df['RSI'] = ta.rsi(df['Close'], length=14)

df['SMA_50'] = ta.sma(df['Close'], length=50)

df['SMA_200'] = ta.sma(df['Close'], length=200)

macd = ta.macd(df['Close'])

df = pd.concat([df, macd], axis=1)

# Generate signals

def generate_signals(row):

score = 0

# Trend condition (SMA crossover)

if row['SMA_50'] > row['SMA_200']:

score += 1

elif row['SMA_50'] < row['SMA_200']:

score -= 1

# Momentum condition (RSI)

if row['RSI'] < 30:

score += 1 # Oversold = potential buy

elif row['RSI'] > 70:

score -= 1 # Overbought = potential sell

# MACD condition

if row['MACD_12_26_9'] > row['MACDs_12_26_9']:

score += 1

else:

score -= 1

return score

df['Signal_Score'] = df.apply(generate_signals, axis=1)

df['Signal'] = 'HOLD'

df.loc[df['Signal_Score'] >= 2, 'Signal'] = 'BUY'

df.loc[df['Signal_Score'] <= -2, 'Signal'] = 'SELL'

# View signals

signals = df[df['Signal'] != 'HOLD'][['Close', 'RSI', 'MACD_12_26_9', 'Signal_Score', 'Signal']]

print(signals.tail(10))

Volume Indicators

OBV, CMF, MFI, and volume analysis

Python

import pandas_ta as ta

# On-Balance Volume

df['OBV'] = ta.obv(df['Close'], df['Volume'])

# Chaikin Money Flow

df['CMF'] = ta.cmf(df['High'], df['Low'], df['Close'], df['Volume'], length=20)

# Money Flow Index

df['MFI'] = ta.mfi(df['High'], df['Low'], df['Close'], df['Volume'], length=14)

# Volume Weighted Average Price

df['VWAP'] = ta.vwap(df['High'], df['Low'], df['Close'], df['Volume'])

# Accumulation/Distribution

df['AD'] = ta.ad(df['High'], df['Low'], df['Close'], df['Volume'])

# Volume SMA for comparison

df['Volume_SMA'] = ta.sma(df['Volume'], length=20)

df['Volume_Ratio'] = df['Volume'] / df['Volume_SMA']

# Volume breakout detection

df['High_Volume'] = df['Volume_Ratio'] > 1.5

print(df[['Close', 'Volume', 'OBV', 'MFI', 'CMF', 'High_Volume']].tail())

Candlestick Pattern Recognition

Detect candlestick patterns automatically

Python

import pandas_ta as ta

# Detect candlestick patterns

# Doji

df['DOJI'] = ta.cdl_doji(df['Open'], df['High'], df['Low'], df['Close'])

# Hammer

df['HAMMER'] = ta.cdl_hammer(df['Open'], df['High'], df['Low'], df['Close'])

# Engulfing

engulfing = ta.cdl_pattern(df['Open'], df['High'], df['Low'], df['Close'], name='engulfing')

if engulfing is not None:

df['ENGULFING'] = engulfing

# Morning/Evening Star

morning_star = ta.cdl_pattern(df['Open'], df['High'], df['Low'], df['Close'], name='morningstar')

if morning_star is not None:

df['MORNING_STAR'] = morning_star

# Filter days with patterns

pattern_cols = [col for col in df.columns if 'CDL' in col or col in ['DOJI', 'HAMMER', 'ENGULFING', 'MORNING_STAR']]

patterns_found = df[pattern_cols].apply(lambda x: x != 0).any(axis=1)

print(f"Days with patterns: {patterns_found.sum()}")

print(df[patterns_found][['Close'] + pattern_cols].tail())

Performance Metrics

Calculate trading performance statistics

Python

import pandas_ta as ta

# Calculate returns

df['Returns'] = df['Close'].pct_change()

df['Log_Returns'] = ta.log_return(df['Close'])

# Cumulative returns

df['Cumulative'] = (1 + df['Returns']).cumprod()

# Performance metrics using pandas_ta

# Drawdown

drawdown = ta.drawdown(df['Close'])

df = pd.concat([df, drawdown], axis=1)

# Calculate Sharpe-like metrics

returns = df['Returns'].dropna()

mean_return = returns.mean() * 252 # Annualized

std_return = returns.std() * (252 ** 0.5) # Annualized

sharpe = mean_return / std_return

# Win rate

wins = (returns > 0).sum()

losses = (returns < 0).sum()

win_rate = wins / (wins + losses)

print(f"Annualized Return: {mean_return:.2%}")

print(f"Annualized Volatility: {std_return:.2%}")

print(f"Sharpe Ratio: {sharpe:.2f}")

print(f"Win Rate: {win_rate:.2%}")

print(f"Max Drawdown: {df['DD_PCT'].min():.2%}")

Common Use Cases

Quick indicator calculation

Multi-indicator strategy building

Candlestick pattern detection

Volume analysis

Trend identification

Momentum screening

Volatility measurement

Performance analytics

Signal generation

Technical screening

Best Practices & Common Pitfalls

Use Strategies for Bulk Indicators

Create ta.Strategy() objects to apply multiple indicators efficiently instead of calling each separately.

Handle Column Names

pandas_ta auto-generates column names. Use df.columns to verify names before accessing (e.g., MACD_12_26_9).

Combine with pandas

Leverage pandas filtering and groupby with pandas_ta indicators for powerful analysis.

Check for NaN Values

Early rows will have NaN for indicators with lookback periods. Always handle these before backtesting.

Verify Indicator Parameters

Different sources may define indicators differently. Verify parameters match your expectations.

Candlestick Pattern Returns

Pattern functions return 0, 100, or -100 (bullish/bearish), not boolean. Handle accordingly.

Additional Resources

Official Documentation

Related Libraries

TA-Lib - Alternative indicator library
finta - Financial indicator library
tulipy - Tulip Indicators wrapper

Next Steps

Now that you can calculate indicators, learn to backtest strategies and connect to live trading:

Backtrader - Backtesting vectorbt - Fast Analysis TA-Lib - More Indicators