# ta-lib

Provides C-optimized technical analysis with 150+ indicators and 61 candlestick patterns via TA-Lib. Essential for high-performance trading pipelines on large price datasets.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 493 installs · ★ 443
- Author: agiprolabs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/ta-lib/SKILL.md
- Updated: 2026-09-03
- HTML: https://affpapa.org/en/nevibecode/skills/ta-lib

## What it does

This skill brings C-optimized financial computations into your AI agent using the Python wrapper for TA-Lib. It covers over 150 indicators across momentum, volume, and volatility groups, alongside 61 candlestick pattern detectors. All inputs and outputs rely directly on NumPy arrays, executing 10 to 100 times faster than pure-Python libraries on large datasets.

You can work through either the standard function API or the dictionary-based abstract API. Keep in mind that Python packages will fail with an `ImportError` if the underlying C library is missing from the operating system. You must run `brew install ta-lib` on macOS or `sudo apt-get install -y ta-lib` on Debian and Ubuntu before installing the Python package. Also, continuous 24/7 crypto markets degrade gap-based candlestick patterns like morning star, while body-ratio patterns stay dependable.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill ta-lib -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/ta-lib/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/ta-lib/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill ta-lib -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/ta-lib/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill ta-lib -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/ta-lib/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install agiprolabs/claude-trading-skills ta-lib
```

2. **Manually: put the files in place**
- File: `.github/skills/ta-lib/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill ta-lib -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/agiprolabs/claude-trading-skills --path skills/ta-lib
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/ta-lib/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill ta-lib -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/ta-lib/SKILL.md

## When to use it
- Compute RSI, MACD, Bollinger Bands, and ATR directly on NumPy arrays
- Scan historical price bars for 61 distinct candlestick formations
- Accelerate backtest indicator calculations by 10 to 100 times over pure Python
- Run dynamic indicator selection using the TA-Lib abstract dictionary API

## Example prompts
- Compute RSI(14) and MACD on my close price array using talib
- Detect doji and engulfing candlestick patterns on OHLC bars with CDLDOJI and CDLENGULFING
- Pass OHLCV data into the TA-Lib abstract API and calculate Bollinger Bands

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: cedc4cf94207a146.

BEGIN THIRD-PARTY FILE cedc4cf94207a146
````text
---
name: ta-lib
description: C-optimized technical analysis with 150+ functions and 61 candlestick pattern recognition functions via TA-Lib
---

# ta-lib — C-Optimized Technical Analysis

TA-Lib (Technical Analysis Library) is a C library with a Python wrapper providing 150+ technical analysis functions and 61 candlestick pattern recognition functions. It is the industry standard for performance-critical indicator computation, used in production trading systems where pandas-ta or pure-Python alternatives are too slow.

## What TA-Lib Is

TA-Lib was originally written in C for financial market data analysis. The Python wrapper (`TA-Lib` on PyPI, imported as `talib`) provides:

- **150+ indicator functions** across overlap, momentum, volume, volatility, cycle, and math categories
- **61 candlestick pattern recognition functions** — the most comprehensive pattern library available
- **C-speed computation** — 10-100x faster than pure-Python equivalents on large datasets
- **Two APIs**: a function API (pass arrays directly) and an abstract API (pass dict of arrays)
- **NumPy native** — all inputs and outputs are NumPy arrays

## Installation

TA-Lib requires the underlying C library to be installed first:

```bash
# macOS
brew install ta-lib
uv pip install TA-Lib numpy pandas

# Ubuntu/Debian
sudo apt-get install -y ta-lib
uv pip install TA-Lib numpy pandas

# From source (any platform)
wget https://github.com/ta-lib/ta-lib/releases/download/v0.6.4/ta-lib-0.6.4-src.tar.gz
tar -xzf ta-lib-0.6.4-src.tar.gz
cd ta-lib-0.6.4
./configure --prefix=/usr/local
make && sudo make install
uv pip install TA-Lib numpy pandas
```

If the C library is not installed, `import talib` will fail with an `ImportError`. The scripts in this skill include fallback logic for environments without TA-Lib installed.

## When to Use TA-Lib vs pandas-ta

| Criterion | TA-Lib | pandas-ta |
|---|---|---|
| Speed | C-optimized, 10-100x faster | Pure Python, slower on large data |
| Candlestick patterns | 61 built-in patterns | Limited pattern support |
| Installation | Requires C library | `pip install` only |
| API style | NumPy arrays | DataFrame `.ta` accessor |
| Indicator count | 150+ | 130+ |
| Streaming | Single-value update possible | Recompute entire series |
| Dependencies | C lib + numpy | pandas only |

**Use TA-Lib when:**
- Processing millions of bars or running backtests at scale
- You need candlestick pattern recognition (TA-Lib is unmatched here)
- You are building a production pipeline where latency matters
- You need cycle indicators (Hilbert Transform family)

**Use pandas-ta when:**
- You want DataFrame-native convenience
- Installation simplicity matters (no C dependency)
- You need indicators not in TA-Lib (pandas-ta has some extras)

## Quick Start

```python
import numpy as np
import talib
````
END THIRD-PARTY FILE cedc4cf94207a146

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