pandas-ta

Name: pandas-ta
Availability: InStock
Author: agiprolabs

Calculate over 130 technical analysis indicators on pandas DataFrames for cryptocurrency market data.

266|54|Updated Mar 11, 2026

Search Tags:#data science #pandas #technical analysis #backtesting #crypto trading #indicators

Ready to run

npx skills add https://github.com/agiprolabs/claude-trading-skills --skill pandas-ta

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pandas-ta
Source: https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/pandas-ta
Command: npx skills add https://github.com/agiprolabs/claude-trading-skills --skill pandas-ta

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas-ta, pandas, httpx, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the complex process of applying over 130 technical analysis indicators to cryptocurrency market data, enabling faster and more informed trading decisions.

Core Features & Use Cases

Extensive Indicator Library: Access a vast array of trend, momentum, volatility, and volume indicators.
Easy DataFrame Integration: Apply indicators directly to pandas DataFrames with a single method call (df.ta).
Strategy Automation: Define and execute custom or pre-built indicator strategies for backtesting and live analysis.
Use Case: Analyze Bitcoin's price action by calculating RSI, MACD, and Bollinger Bands simultaneously on your OHLCV DataFrame to identify potential buy or sell signals based on predefined strategy rules.

Quick Start

Apply the RSI indicator with a length of 14 to your OHLCV DataFrame named 'df'.

Frequently Asked Questions about pandas-ta

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I calculate technical analysis indicators on a pandas DataFrame?▼

To calculate technical analysis indicators on a pandas DataFrame, you can apply over 130 trend, momentum, volatility, and volume indicators directly using a single method call (`df.ta`). This integrates seamlessly with your existing financial time-series data manipulation workflow.

Can I use pandas-ta for backtesting crypto trading strategies?▼

Yes, you can use pandas-ta for backtesting crypto trading strategies by defining and executing custom or pre-built indicator strategies. This allows you to evaluate trend, momentum, and volume signals on historical OHLCV data to identify potential buy or sell conditions.

Does this Skill work with cryptocurrency OHLCV data?▼

This Skill works directly with cryptocurrency OHLCV data structured as a pandas DataFrame. It is specifically optimized for crypto markets, allowing you to calculate indicators like RSI, MACD, and Bollinger Bands to analyze price action efficiently.

What's the best way to apply multiple technical indicators like RSI and MACD simultaneously?▼

The best way to apply multiple technical indicators like RSI and MACD simultaneously is by using the built-in strategy automation features. You can define custom strategies to append various indicators to your DataFrame in a single operation for comprehensive market analysis.

Do I need pandas and numpy installed to compute momentum and volatility indicators?▼

Yes, you need pandas and numpy installed as dependencies to compute momentum and volatility indicators. The Skill requires these libraries alongside httpx and pandas-ta to perform efficient data manipulation and mathematical calculations on time-series data.