Generate Ichimoku trading signals from OHLCV data with configurable parameters.
npx skills add https://github.com/prinzeval/Vibe-Trading --skill ichimoku-prinzeval
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
Provides an autonomous Ichimoku Kinko Hyo signal engine that converts OHLCV price data into actionable trading signals, reducing manual analysis and decision latency.
Provide a map of OHLCV data keyed by symbol to the engine and retrieve per-symbol buy, sell, or hold signals.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
You can configure tenkan, kijun, senkou_b, and displacement parameters for technical analysis. These settings control signal generation from daily OHLCV candles.
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.
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.
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