ichimoku

Name: ichimoku
Availability: InStock
Author: wudye

Generate Ichimoku long, short, or stand-aside signals from OHLCV data.

Updated May 5, 2026

Search Tags:#time series #pandas #technical analysis #backtesting #trading signals #ichimoku

Ready to run

npx skills add https://github.com/wudye/traderAssistHK --skill ichimoku-wudye

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ichimoku
Source: https://github.com/wudye/traderAssistHK/tree/main/backend/src/skills/ichimoku
Command: npx skills add https://github.com/wudye/traderAssistHK --skill ichimoku-wudye

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, requests, and includes references (resource) components.

What problem does it solve?

It transforms OHLCV price data into actionable Ichimoku Kinko Hyo long/short/stand-aside signals, reducing the manual effort of computing five-line values and filtering crossovers.

Core Features & Use Cases

Ichimoku five-line computation: Calculates Tenkan-sen, Kijun-sen, Senkou Span A/B (with displacement), and uses cloud boundaries to contextualize signals.
Signal generation with strict filters: Triggers only on Tenkan/Kijun crossover events, filtered by (1) price position relative to the cloud and (2) cloud direction (Span A > Span B for bullish, Span A < Span B for bearish).
Stand-aside safety: Outputs 1 for long, -1 for short, and 0 when conditions are not met, avoiding low-quality trades.
Use Case: Backtest a Japanese Ichimoku strategy on daily candles for multiple symbols and count the resulting buy/sell events.

Quick Start

Run the ichimoku SignalEngine on a dictionary of OHLCV DataFrames keyed by symbol to produce a per-symbol signal series (1/-1/0).

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 from OHLCV candle data?▼

You can generate Ichimoku signals by running a pure pandas computation on OHLCV DataFrames with a datetime index, detecting crossovers filtered by cloud boundaries to output an integer signal series.

What does the Ichimoku cloud direction filter do for technical analysis?▼

The Ichimoku cloud direction filter validates trade quality by requiring Span A > Span B for bullish signals and Span A < Span B for bearish signals, ensuring Tenkan/Kijun crossovers align with the broader cloud trend.

Can I use pandas for multi-symbol backtesting with Ichimoku strategies?▼

Yes, you can use pandas for multi-symbol backtesting by passing a dictionary of OHLCV DataFrames keyed by symbol, which produces a per-symbol integer signal series suitable for systematic strategy research.

How does the signal engine handle warm-up periods for Senkou Span displacement?▼

The signal engine handles warm-up periods for Senkou Span displacement by managing the initial data points where displacement-dependent spans are not yet fully calculated, preventing premature or invalid crossover triggers.

Why does my Ichimoku backtest output a stand-aside signal when Tenkan crosses Kijun?▼

Your Ichimoku backtest outputs a stand-aside signal because strict filters require price to be positioned correctly relative to the cloud and the cloud direction must match the crossover, otherwise it outputs 0 to avoid low-quality trades.