ichimoku

Generate Ichimoku trading signals from OHLCV data with configurable parameters.

Updated Aug 23, 2026

Search Tags:#cloud #signals #pandas #trading #technical-analysis #ichimoku

Ready to run

npx skills add https://github.com/prinzeval/Vibe-Trading --skill ichimoku-prinzeval

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ichimoku
Source: https://github.com/prinzeval/Vibe-Trading/tree/main/VALENDATA/agent/src/skills/ichimoku
Command: npx skills add https://github.com/prinzeval/Vibe-Trading --skill ichimoku-prinzeval

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Provides an autonomous Ichimoku Kinko Hyo signal engine that converts OHLCV price data into actionable trading signals, reducing manual analysis and decision latency.

Core Features & Use Cases

TK cross filtering: Detects bullish or bearish Tenkan/Kijun crossovers with a three-filter validation (price relative to cloud and cloud direction).
Cloud-based trend confirmation: Utilizes Senkou Span A/B to determine cloud direction and strength for signal validation.
Multi-symbol signal production: Accepts a map of symbol -> OHLCV data and outputs per-symbol signals suitable for backtesting or live monitoring.

Quick Start

Provide a map of OHLCV data keyed by symbol to the engine and retrieve per-symbol buy, sell, or hold signals.

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 data?▼

To generate Ichimoku trading signals from OHLCV data, provide a map of daily candles keyed by symbol. The engine computes per-symbol buy, sell, or hold signals as integers.

How does a TK cross filter validate bullish or bearish signals?▼

A TK cross filter validates Ichimoku signals by checking the Tenkan/Kijun crossover alongside three filters: price position relative to the cloud and the overall cloud direction.

Can I use pandas dataframes for multi-symbol backtesting and live monitoring?▼

Yes, you can use pandas for multi-symbol backtesting and live monitoring by passing a map of symbol-keyed OHLCV dataframes to retrieve per-symbol signals.

What Ichimoku parameters can I configure for technical analysis?▼

You can configure tenkan, kijun, senkou_b, and displacement parameters for technical analysis. These settings control signal generation from daily OHLCV candles.

How does cloud position and displacement affect signal output?▼

Cloud position and displacement affect signal output by utilizing Senkou Span A/B to determine cloud direction and strength, which validates the final per-symbol trading signals.

Do I need to format daily candles differently for multi-symbol signal production?▼

No, you do not need to format daily candles differently for multi-symbol signal production. Simply structure the OHLCV data as a map keyed by symbol to output per-symbol signals.