pandas-ti 1.0.1

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

import pandas as pd
import pandas_ti
import yfinance as yf

# Get some 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()

# Series indicators - work on any Series
df['sma_20'] = df['Close'].ti.SMA(n=20)
df['ema_50'] = df['Close'].ti.EMA(n=50)

Built-in Help System

# List all available indicators
df.ti.indicators()

# Get detailed help for specific indicator
df.ti.help('ATR')

# Series indicators help
df['Close'].ti.help('SMA')

Available Indicators

DataFrame Indicators (require OHLCV data)

Indicator	Description	Parameters
TR	True Range	None
ATR	Average True Range	n (window size)
RTR	Relative True Range (normalized)	None
ARTR	Average Relative True Range	n (window size)
SRTR	Standardized Relative True Range	n, N=1000, expand=True, method='cluster', full=False

Series Indicators (work on any Series)

Indicator	Description	Parameters
SMA	Simple Moving Average	n (window size)
EMA	Exponential Moving Average	n (span)

Custom Column Names

The library automatically detects OHLCV columns regardless of naming convention:

# All these DataFrames work automatically
df_yahoo = yf.Ticker("AAPL").history(period="1mo")  # Capital case: Open, High, Low, Close
df_crypto = ...  # lowercase: open, high, low, close
df_custom = ...  # Short form: O, H, L, C

# All work the same way
df_yahoo['atr'] = df_yahoo.ti.ATR(n=14)
df_crypto['atr'] = df_crypto.ti.ATR(n=14)
df_custom['atr'] = df_custom.ti.ATR(n=14)

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)

Development

# Clone the repository
git clone https://github.com/JavierCalzadaEspuny/pandas-ti
cd pandas_ti

# Install in development mode
pip install -e .[dev]

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