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Liangwei-zhang
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candlestick

Identify and score 15 candlestick patterns into a composite trading signal.

Updated Apr 14, 2026
Search Tags:#finance#pandas#backtesting#pattern-recognition#trading-signals#candlestick#vectorized
One-click install
npx skills add https://github.com/Liangwei-zhang/six-stock --skill candlestick-liangwei-zhang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: candlestick
Source: https://github.com/Liangwei-zhang/six-stock/tree/main/Vibe-Trading/agent/src/skills/candlestick
Command: npx skills add https://github.com/Liangwei-zhang/six-stock --skill candlestick-liangwei-zhang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Candlestick pattern recognition engine that identifies 15 classic patterns (5 single-candle, 5 double-candle, 4 triple-candle, plus a trend confirmation) and generates a composite trading signal from bullish/bearish scores, enabling systematic decision making without manual pattern scanning.

Core Features & Use Cases

  • Pattern coverage: detects 15 classic candlestick formations across single, double, and triple candles, plus a trend check.
  • Vectorized implementation: pure pandas-based, designed for fast and scalable computation on large OHLCV datasets.
  • Unified signal output: combines individual pattern scores into a single per-timestamp signal (1 for bullish, -1 for bearish, 0 for neutral).
  • Configurable sensitivity: exposes body_pct and shadow_ratio to tune for doji sensitivity and shadow length.
  • Use cases: backtest candlestick-based strategies, generate live signals, or build rule-based entry/exit systems.

Quick Start

Provide an OHLCV dataframe to the engine and obtain a per-row signal representing the aggregated candlestick pattern scores.

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 OHLCV dataframe?▼

You can identify candlestick patterns in a pandas OHLCV dataframe by running it through a vectorized engine that detects 15 classic single, double, and triple candle formations. This generates a unified per-timestamp trading signal indicating bullish, bearish, or neutral market sentiment.

Can I use vectorized candlestick pattern recognition for backtesting trading strategies?▼

Vectorized candlestick pattern recognition is fully designed for backtesting trading strategies. It applies a pure pandas-based engine for fast computation on large OHLCV datasets, outputting standardized bullish, bearish, or neutral signals to test rule-based entry and exit systems.

How do I configure doji sensitivity and shadow length thresholds for candlestick detection?▼

You configure doji sensitivity and shadow length thresholds for candlestick detection by adjusting the body_pct and shadow_ratio parameters. These configurable thresholds allow you to tune pattern detection sensitivity and filter results based on your specific asset and timeframe.

What's the best way to generate live trading signals from candlestick patterns?▼

The best way to generate live trading signals from candlestick patterns is using a pure pandas vectorized engine. It scores 15 classic patterns across various assets and timeframes, combining bullish and bearish results into a single composite signal for systematic decision making.

Does candlestick pattern detection work across different asset types and timeframes?▼

Candlestick pattern detection works across a wide range of asset types and timeframes. The engine evaluates 15 classic candlestick formations and a trend confirmation, producing a composite score per timestamp that supports both live trading and historical backtesting scenarios.

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