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candlestick

Detect candlestick patterns and generate composite bullish or bearish signals from OHLCV data.

Updated Apr 10, 2026
Search Tags:#patterns#signals#pandas#trading#candlestick#vectorized
One-click install
npx skills add https://github.com/ebrahim-sani/trading-automation --skill candlestick-ebrahim-sani
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: candlestick
Source: https://github.com/ebrahim-sani/trading-automation/tree/main/vibe-trading/agent/src/skills/candlestick
Command: npx skills add https://github.com/ebrahim-sani/trading-automation --skill candlestick-ebrahim-sani

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, requests.

What problem does it solve?

Many traders lack a fast, vectorized method to identify classic candlestick patterns across multiple symbols, leading to missed opportunities and manual analysis.

Core Features & Use Cases

  • Pattern Library: Detects 15 classic candlestick patterns (single, double, triple, and trend confirmation) using pure pandas vectorized calculations.
  • Scoring Engine: Assigns bullish (+1) or bearish (-1) scores and aggregates them into a composite signal for each bar.
  • Multi‑symbol Support: Processes a dictionary of OHLCV DataFrames, returning a signal series per instrument, ready for integration with automated trading pipelines.

Quick Start

Ask the candlestick skill to analyze your OHLCV DataFrame and return the generated trade signals.

Frequently Asked Questions about candlestick

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

FAQPage Schema
How do I detect candlestick patterns in a pandas DataFrame?▼

To detect candlestick patterns in a pandas DataFrame, you can process OHLCV time series data to identify 15 classic patterns and generate composite bullish or bearish trade signals using vectorized calculations.

Can I generate trade signals for multiple symbols simultaneously?▼

Yes, multi-symbol support allows you to process a dictionary of OHLCV DataFrames, returning a composite bullish or bearish trade signal series per instrument for automated trading pipelines.

What is the best way to identify 15 classic candlestick patterns without external dependencies?▼

The best way to identify classic candlestick patterns without external dependencies is using pure pandas and numpy vectorized computations, which assigns bullish or bearish scores to generate a composite signal.

Does vectorized OHLCV processing require numpy and pandas?▼

Vectorized OHLCV processing requires pandas and numpy to perform fast calculations, detecting single, double, and triple candlestick patterns to inform automated trading decisions efficiently.

How are bullish and bearish scores aggregated into a composite signal?▼

Bullish and bearish scores are aggregated by assigning +1 or -1 values to detected candlestick patterns, combining them into a single composite signal for each bar in the OHLCV time series.

What are the limitations of using vectorized calculations for candlestick pattern detection?▼

The vectorized calculation approach for candlestick pattern detection relies purely on OHLCV data and does not incorporate external dependencies or alternative data sources, limiting its contextual trading analysis.

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