pandas-ti 1.1.0

A lightweight technical indicators library for pandas DataFrames and Series

pandas_ti

A lightweight and extensible technical analysis library for pandas DataFrames and Series.

Features

• Zero Configuration - Automatic OHLCV column detection with multiple naming conventions
• Pandas Native - Seamless integration via .ti accessor for DataFrames and Series
• Self-Documenting - Built-in help system with rich console output
• Extensible - Easy to add custom indicators with decorator pattern
• Fast & Lightweight - Minimal dependencies, maximum performance

Quick Start

Installation

pip install pandas-ti

Basic Usage

There are two ways to use indicators in pandas_ti:

1. Accessor Pattern (Recommended)

Use the .ti accessor for seamless integration with pandas. High and Low columns are auto-injected from your DataFrame.

import pandas as pd
import pandas_ti
import yfinance as yf

# Get market data
df = yf.Ticker("AAPL").history(period="1y")

# DataFrame indicators - automatic OHLCV detection
df['ATR_14'] = df.ti.ATR(n=14)
df['RTR'] = df.ti.RTR()
zz = df.ti.ZigZag(pct=0.05)

# Series indicators - work on any Series
df['SMA_20'] = df['Close'].ti.SMA(n=20)
df['EMA_50'] = df['Close'].ti.EMA(n=50)

2. Direct Import (Manual)

Import indicators directly for more control. You must explicitly pass the required data.

import pandas_ti as ti
import yfinance as yf

df = yf.Ticker("AAPL").history(period="1y")

# Import and use indicators directly
from pandas_ti import ATR, RTR, ZigZag, SMA, EMA

# DataFrame indicators - must specify columns
df['ATR_14'] = ATR(High=df['High'], Low=df['Low'], Close=df['Close'], n=14)
df['RTR'] = RTR(High=df['High'], Low=df['Low'], Close=df['Close'])
zz = ZigZag(High=df['High'], Low=df['Low'], pct=0.05)

# Series indicators - pass Series directly
df['SMA_20'] = SMA(series=df['Close'], n=20)
df['EMA_50'] = EMA(series=df['Close'], n=50)

# Alternative: use package-level access
df['ATR_14'] = ti.ATR(High=df['High'], Low=df['Low'], Close=df['Close'], n=14)

Built-in Help System

Help with Accessor Pattern

# List all available indicators
df.ti.indicators()
df['Close'].ti.indicators()

# Get detailed help for specific indicator
df.ti.help('ATR')
df['Close'].ti.help('SMA')

Help with Direct Import

from pandas_ti import ZigZag, ATR

# Using help() function
help(ZigZag)
help(ATR)

# Using __doc__ attribute
print(ZigZag.__doc__)
print(ATR.__doc__)

Available Indicators

DataFrame Indicators (require OHLCV data)

These indicators require High, Low, and Close columns (auto-detected with accessor pattern).

Indicator	Description	Parameters	Return Type
TR	True Range	None	pd.Series
ATR	Average True Range	n (window size)	pd.Series
RTR	Relative True Range	None	pd.Series
ARTR	Average Relative True Range	n (window size)	pd.Series
SRTR	Standardized Relative True Range	n, N=1000, expand=False, method='cluster'	SRTRClass
ZigZag	Significant price reversals detector	pct (percentage threshold)	ZigZagClass

Series Indicators (work on any Series)

These indicators work on any pandas Series.

Indicator	Description	Parameters	Return Type
SMA	Simple Moving Average	n (window size)	pd.Series
EMA	Exponential Moving Average	n (span)	pd.Series

Technical Details

Automatic Column Detection

The library automatically detects OHLCV columns using common naming variations:

Column Type	Accepted Names
Open	Open, OPEN, open, O, o
High	High, HIGH, high, H, h
Low	Low, LOW, low, L, l
Close	Close, CLOSE, close, C, c
Volume	Volume, VOLUME, volume, Vol, vol, V, v

Requirements

Core Dependencies

• Python >= 3.12
• pandas >= 2.3.3
• numpy >= 2.3.3
• rich >= 14.2.0
• scipy >= 1.16.2
• statsmodels >= 0.14.5

Optional dependencies

• yfinance >= 0.2.66 (for examples and testing)
• matplotlib >= 3.10.7 (for visualization)
• mplfinance >= 0.12.10b0 (for financial charts)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Javier Calzada Espuny

• GitHub: @JavierCalzadaEspuny
• Linkedin: JavierCalzadaEspuny
• Repository: pandas_ti