What problem does it solve?
This Skill solves the problem of translating raw OHLCV price data into actionable technical signals by automatically detecting Chinese Chan Theory structures (fractals, strokes, and centers) and converting them into buy/sell decisions.
Core Features & Use Cases
- Fractal → Stroke → Center detection: Builds Chan Theory elements (分型/笔/中枢) from properly ordered OHLCV data.
- Buy/Sell point signal generation: Produces 一买/一卖 and multi-stroke pattern signals such as 三笔、五笔 classifications for directional decisions.
- Multi-cycle readiness: Works with configurable CZSC frequency inputs (e.g., daily and other supported frequencies) for scenario-specific analysis.
- Use Case: Use it to scan BTC-USDT (or any OHLCV market) over a rolling window and flag dates where structure-based buy or sell conditions are met.
Quick Start
Provide your OHLCV DataFrame (open/high/low/close/volume, datetime index sorted ascending) for a symbol like BTC-USDT and run the example signal engine to generate a time-aligned series of 1 (buy), -1 (sell), and 0 (wait) signals.