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ta-lib

Provides Python wrapper for TA-Lib C library enabling fast computation of 150+ technical indicators and 61 candlestick patterns.

266|54|Updated Mar 11, 2026
Search Tags:#performance#finance#technical analysis#trading#indicators#ta-lib#candlestick patterns
One-click install
npx skills add https://github.com/agiprolabs/claude-trading-skills --skill ta-lib
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ta-lib
Source: https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/ta-lib
Command: npx skills add https://github.com/agiprolabs/claude-trading-skills --skill ta-lib

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides access to a highly optimized C library for calculating over 150 technical analysis indicators and 61 candlestick patterns, offering significant speed improvements over pure Python implementations for large datasets.

Core Features & Use Cases

  • 150+ Indicators: Compute standard indicators like RSI, MACD, Bollinger Bands, ATR, etc.
  • 61 Candlestick Patterns: Detect complex chart patterns for trading signals.
  • High Performance: Ideal for backtesting and real-time analysis where speed is critical.
  • Use Case: Quickly calculate the 14-day RSI and MACD for a large historical price dataset to identify potential buy or sell signals.

Quick Start

Use the ta-lib skill to calculate the 14-day RSI for the provided closing prices.

Frequently Asked Questions about ta-lib

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

FAQPage Schema
How do I calculate technical analysis indicators like RSI and MACD for large historical price datasets?▼

This Skill computes over 150 technical analysis indicators and 61 candlestick patterns using a Python wrapper for the TA-Lib C library, requiring the underlying C library and Python TA-Lib package to be installed first.

How do I detect candlestick patterns in financial time-series data?▼

You can detect candlestick patterns in financial time-series data using the 61 built-in pattern recognition functions, which identify complex chart formations to generate trading signals.

What's the best way to run fast backtesting with technical indicators in Python?▼

The best way to run fast backtesting with technical indicators in Python is using C-optimized libraries like TA-Lib, which enable efficient processing of large financial datasets where computational speed is critical.

Do I need to install the underlying C library to use TA-Lib with pandas and numpy?▼

Yes, you need to install the underlying TA-Lib C library alongside the Python TA-Lib package, as the wrapper depends on it to compute indicators efficiently using numpy and pandas data structures.

Why use a C-optimized library over pure Python for technical analysis?▼

You should use a C-optimized library over pure Python for technical analysis to achieve significant speed improvements when processing large financial datasets for backtesting and real-time indicator generation.

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