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ichimoku

Generate long, short, or neutral trading signals from Ichimoku indicators using pandas.

6.1k|1.2k|Updated Jun 9, 2022
Search Tags:#cloud#signals#pandas#trading#technical-analysis#multi-asset#ichimoku
One-click install
npx skills add https://github.com/charliedream1/ai_quant_trade --skill ichimoku-charliedream1
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ichimoku
Source: https://github.com/charliedream1/ai_quant_trade/tree/main/a_%E5%85%A8%E7%BD%91%E4%BC%98%E7%A7%80%E8%B5%84%E6%BA%90/10_%E5%A4%A7%E6%A8%A1%E5%9E%8B/07_skill%E5%8C%85/vibe_trading_skills/ichimoku
Command: npx skills add https://github.com/charliedream1/ai_quant_trade --skill ichimoku-charliedream1

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provides a standalone Ichimoku Kinko Hyo signal engine that computes reliable trading signals without external technical-analysis libraries.

Core Features & Use Cases

  • Three-filter signal logic (TK cross, cloud position, cloud trend) to filter out false signals and improve signal quality.
  • Multi-asset capability: generate signals across several symbols from a shared OHLCV feed.
  • Deterministic, pandas-only implementation of Tenkan-sen, Kijun-sen, Senkou Span A/B, and Chikou Span, with a configurable warm-up period.

Quick Start

Load OHLCV data for your symbols and run the engine to generate per-symbol signals (1=long, -1=short, 0=neutral).

Frequently Asked Questions about ichimoku

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

FAQPage Schema
How do I generate Ichimoku trading signals using pandas without external libraries?▼

You can generate Ichimoku trading signals using a pure pandas implementation that computes Tenkan-sen, Kijun-sen, and Senkou Spans from OHLCV data, applying a three-filter rule to output long, short, or neutral stances.

What is the three-filter rule in Ichimoku technical analysis?▼

The three-filter rule in Ichimoku technical analysis validates signals by requiring TK crossover confirmation, cloud position alignment, and cloud trend verification to filter false signals and improve output quality.

Can I run Ichimoku indicator calculations across multiple assets simultaneously?▼

Yes, you can run Ichimoku indicator calculations across multiple assets by feeding shared OHLCV data into the engine, which computes separate Tenkan-sen, Kijun-sen, and Chikou Span values to produce per-symbol signals.

How do I set up OHLCV data for Tenkan-sen and Kijun-sen calculations?▼

To set up OHLCV data for Tenkan-sen and Kijun-sen calculations, load historical open, high, low, close, and volume records into a pandas DataFrame, ensuring enough rows exist to cover the configurable warm-up period.

Does this Ichimoku signals engine require any external technical-analysis dependencies?▼

No, this Ichimoku signals engine does not require external technical-analysis dependencies, relying entirely on a deterministic pandas-only implementation to compute indicators and generate trading signals.

Why does my Ichimoku cloud signal output neutral during the warm-up period?▼

Ichimoku cloud signal outputs neutral during the warm-up period because the engine requires sufficient historical OHLCV data points to accurately compute Senkou Span A and B before generating valid TK crossover signals.

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