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candlestick

Detect 15 candlestick patterns from OHLC series and output long, short, or neutral signals.

Updated May 5, 2026
Search Tags:#pattern recognition#pandas#backtesting#OHLCV#trading signals#candlestick
One-click install
npx skills add https://github.com/wudye/traderAssistHK --skill candlestick-wudye
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: candlestick
Source: https://github.com/wudye/traderAssistHK/tree/main/backend/src/skills/candlestick
Command: npx skills add https://github.com/wudye/traderAssistHK --skill candlestick-wudye

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, requests.

What problem does it solve?

This Skill helps you transform raw OHLC candlestick data into actionable directional signals by recognizing classic chart patterns.

Core Features & Use Cases

  • 15 candlestick pattern recognition: Detects 5 single-candle, 5 double-candle, 4 triple-candle, plus trend confirmation for Shooting Star.
  • Pure pandas vectorized scoring: Computes bullish/bearish pattern scores in a vectorized way for each timestamp.
  • Composite trade signal output: Produces 1 (long), -1 (short), or 0 (stand aside) based on the total pattern score.
  • Use Case: You run a daily pipeline for multiple symbols (e.g., BTC-USDT, ETH-USDT) and want consistent pattern-based features/signals for backtesting or execution.

Quick Start

Run the candlestick signal engine on your OHLCV DataFrame(s) containing open, high, low, and close, then read the resulting per-symbol signal series.

Frequently Asked Questions about candlestick

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

FAQPage Schema
How do I generate trading signals from OHLCV price data using pandas?▼

You can generate trading signals by running a pandas vectorized engine on your OHLCV DataFrame to detect 15 classic candlestick patterns, scoring bullish versus bearish evidence into long (1), short (-1), or neutral (0) outputs.

What candlestick patterns can I detect for backtesting multi-symbol price series?▼

You can detect 15 classic candlestick patterns for backtesting multi-symbol price series, including 5 single-candle, 5 double-candle, 4 triple-candle formations, plus trend confirmation for the Shooting Star pattern.

Does this candlestick pattern detection work with real-time feature generation?▼

Yes, this candlestick pattern detection works for real-time feature generation and backtesting, producing consistent pattern-based features for multiple symbols like BTC-USDT and ETH-USDT using pure pandas vectorized scoring.

Can I use numpy and pandas for vectorized candlestick pattern recognition?▼

Yes, this implementation uses numpy and pandas for vectorized candlestick pattern recognition, computing bullish and bearish pattern scores across each timestamp using parameterized doji thresholds.

How do I handle doji threshold parameters when detecting candlestick patterns?▼

The engine uses parameterized doji thresholds to evaluate single, double, and triple-candle formations, ensuring accurate bullish and bearish scoring across your OHLC series when detecting candlestick patterns.

What's the best way to score bullish and bearish candlestick evidence for trade execution?▼

The best way to score bullish and bearish candlestick evidence for trade execution is to aggregate pattern detections into a composite score per timestamp, producing actionable signals as 1 for long, -1 for short, or 0 to stand aside.

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