Generate Chan theory buy and sell signals from OHLCV data.
npx skills add https://github.com/loanntc/Paave --skill chan-theory-analysis
Please help me install this Agent Skill. Skill: chan-theory-analysis Source: https://github.com/loanntc/Paave/tree/main/skills/chanlun Command: npx skills add https://github.com/loanntc/Paave --skill chan-theory-analysis
It solves the problem of converting raw OHLCV price data into actionable Chan theory trading signals by detecting patterns (fractals), strokes, and consolidation zones automatically.
Freq in czsc, supporting use in backtesting and real-time monitoring pipelines.Provide a time-sorted OHLCV DataFrame (open/high/low/close/volume/amount) and run the signal engine to output a per-timestamp Series of 1 (buy), -1 (sell), or 0 (no signal).
High-intent search queries and answers about installing and using this skill.
Chan theory buy and sell signals are generated by detecting fractals, strokes, and consolidation zones from ordered OHLCV K-lines. The engine maps multi-stroke patterns into standardized outputs of 1 for long, -1 for short, and 0 for watch.
In Chan theory time-series analysis, fractals and strokes are structural components automatically identified from K-line price bars. Fractals mark turning points, strokes connect them, and consolidation zones form the basis for generating buy and sell trading signals.
Yes, you can use czsc for multi-frequency backtesting by configuring the Freq parameter. This allows you to scan historical turning points and evaluate strategy behavior across different K-line frequencies for stocks, crypto, or futures.
Yes, the Chan theory signal engine works with crypto and futures data. By processing ordered OHLCV bar inputs through DataFrame-to-RawBar conversion, it supports backtesting and screening workflows across various asset classes.
To convert a pandas DataFrame to RawBar for incremental signal updates, you use the specified czsc signal functions and RawBar conversion utilities. This processes time-series OHLCV data to continuously map detected patterns into 1, -1, or 0 outputs.
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