candlestick

Name: candlestick
Availability: InStock
Author: hxhyyy

Identify 15 classic candlestick patterns in OHLCV dataframes and generate trading signals.

Updated Jul 8, 2026

Search Tags:#finance #pattern recognition #pandas #technical analysis #trading #candlestick

Ready to run

npx skills add https://github.com/hxhyyy/Vibe-Trading --skill candlestick-hxhyyy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: candlestick
Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/candlestick
Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill candlestick-hxhyyy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, requests.

What problem does it solve?

This skill automates the identification of complex technical trading patterns, removing the need for manual chart analysis and enabling rapid, data-driven decision-making.

Core Features & Use Cases

Pattern Recognition: Detects 15 classic candlestick patterns including single, double, and triple-candle formations.
Signal Generation: Aggregates bullish and bearish scores to provide clear long, short, or neutral trading signals.
Use Case: Traders can use this to scan large datasets of historical price action to identify potential trend reversals or confirmations across multiple assets simultaneously.

Quick Start

Use the candlestick skill to analyze the provided OHLCV data and generate trading signals for the specified asset list.

Frequently Asked Questions about candlestick

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

FAQPage Schema

How do I identify candlestick patterns in a pandas dataframe?▼

To identify candlestick patterns in a pandas dataframe, you can use a vectorized pattern recognition engine that processes OHLCV data to detect 15 classic single, double, and triple-candle formations. It outputs aggregated bullish and bearish scores for directional trading signals.

What are vectorized candlestick pattern recognition signals?▼

Vectorized candlestick pattern recognition signals are aggregated bullish and bearish scores derived from 15 classic candlestick formations. Processing OHLCV dataframes without manual loops, the mechanism outputs clear long, short, or neutral directional indicators for market analysis.

Can I scan large datasets of historical price action for multiple assets simultaneously?▼

Yes, you can scan large datasets of historical price action for multiple assets simultaneously. The vectorized pandas implementation processes bulk OHLCV dataframes efficiently, enabling rapid, data-driven identification of trend reversals or confirmations across specified asset lists.

Does this candlestick pattern recognition tool require numpy and requests?▼

Yes, the candlestick pattern recognition tool requires numpy and requests alongside pandas. These dependencies support the pure pandas vectorized calculations and potential data fetching operations needed to process OHLCV data and generate composite trading signals.

What is the best way to generate technical trading signals from OHLCV data?▼

The best way to generate technical trading signals from OHLCV data is using a vectorized pandas implementation that identifies 15 classic candlestick patterns. This approach automates complex pattern detection, outputting aggregated bullish and bearish scores for rapid decision-making.

Are there limitations to using pandas vectorized operations for technical analysis?▼

While pandas vectorized operations offer high-performance technical analysis for 15 candlestick patterns, limitations include the strict requirement for properly formatted OHLCV dataframes. Complex or non-standard market data structures may require preprocessing before pattern recognition can execute successfully.