Generate Ichimoku long, short, or stand-aside signals from OHLCV data.
npx skills add https://github.com/wudye/traderAssistHK --skill ichimoku-wudye
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
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.
1 for long, -1 for short, and 0 when conditions are not met, avoiding low-quality trades.Run the ichimoku SignalEngine on a dictionary of OHLCV DataFrames keyed by symbol to produce a per-symbol signal series (1/-1/0).
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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