skills.rest
DaddyElonMusk69
DaddyElonMusk69COMMUNITY
Explore all skills by this author →
GitHub

candlestick

Detect candlestick patterns in OHLC data and generate trading signals.

Updated Apr 12, 2026
Search Tags:#pandas#pattern detection#financial analysis#trading signals#candlestick#ohlc
One-click install
npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill candlestick-daddyelonmusk69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: candlestick
Source: https://github.com/DaddyElonMusk69/motis-agent/tree/main/skills/finance/candlestick
Command: npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill candlestick-daddyelonmusk69

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually identifying candlestick patterns in price data is time‑consuming and error‑prone, making it hard to derive reliable trading signals.

Core Features & Use Cases

  • Vectorized detection of 15 classic single, double, and triple candlestick patterns using pandas.
  • Composite scoring that aggregates bullish and bearish pattern scores into a single signal (-1, 0, 1).
  • Customizable parameters (body_pct, shadow_ratio) to fine‑tune pattern sensitivity.
  • Use case: Integrate into algorithmic trading strategies to automatically generate entry and exit signals from daily OHLCV data.

Quick Start

Ask the candlestick skill to evaluate your OHLC CSV file and return the latest trading signal.

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 OHLC data using pandas?▼

To detect candlestick patterns in OHLC data using pandas, this skill applies vectorized detection to open, high, low, and close columns, identifying 15 classic single, double, and triple patterns to generate trading signals.

What is composite scoring for candlestick trading signals?▼

Composite scoring for candlestick trading signals aggregates bullish and bearish pattern detections into a single numerical output, producing values of -1, 0, or 1 to indicate bearish, neutral, or bullish market direction.

Can I use vectorized pattern detection for intraday financial analysis?▼

Yes, vectorized pattern detection works for intraday financial analysis. The process applies to any time series dataset with open, high, low, and close columns, fully supporting both daily and intraday trading analysis.

How do I customize candlestick pattern sensitivity parameters?▼

You customize candlestick pattern sensitivity by adjusting parameters like body_pct and shadow_ratio. These settings fine-tune detection logic to match your specific OHLC data characteristics and trading strategy requirements.

Do I need numpy and requests to generate trading signals from CSV files?▼

Generating trading signals from CSV files requires pandas, numpy, and requests. Pandas and numpy handle vectorized OHLC data processing, while requests enables optional exchange API fetching for real-time market data.

Related Skills

Explore More Finance & Accounting Skills→
philipcoller-777philipcoller-777

candlestick

Identify and score 15 candlestick patterns in OHLCV data to generate a composite buy/sell/neutral signal.

Community
Intermediate
hxhyyyhxhyyy

candlestick

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

Community
Intermediate
OpenSuckerOpenSucker
15

candlestick

Identifies bullish and bearish candlestick patterns from market OHLC data.

Official
Intermediate
OpenSuckerOpenSucker
15

ichimoku

Analyze Ichimoku Kinko Hyo indicators to generate buy, sell, or standby signals from OHLC data.

Official
Intermediate