chanlun

Generate Chan Theory trading signals from OHLCV data using CZSC.

Updated May 5, 2026
One-click install
npx skills add https://github.com/wudye/traderAssistHK --skill chanlun-wudye
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: chanlun
Source: https://github.com/wudye/traderAssistHK/tree/main/backend/src/skills/chanlun
Command: npx skills add https://github.com/wudye/traderAssistHK --skill chanlun-wudye

SYSTEM DOCUMENTATION & REQUIREMENTS

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

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.

Frequently Asked Questions about chanlun

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate Chan Theory trading signals from OHLCV data?

You can generate Chan Theory trading signals by feeding a sorted OHLCV DataFrame with a datetime index into the engine, which detects fractals, strokes, and centers to output directional buy, sell, or wait values.

What are fractals, strokes, and centers in technical analysis?

Fractals, strokes, and centers are structural price elements in Chinese Chan Theory that identify market turning points and consolidation zones to help determine directional buy and sell decisions.

Can I use CZSC to compute multi-frequency technical analysis signals for crypto?

Yes, you can use CZSC with configurable frequency inputs to compute structure-driven buy and sell signals for crypto assets like BTC-USDT, equities, or futures across multi-frequency time-series data.

How do I scan a rolling window for buy and sell points in a pandas DataFrame?

You scan a rolling window by passing your pandas OHLCV DataFrame to the signal engine, which evaluates multi-stroke pattern classifications and flags dates where structure-based buy or sell conditions are met.

Does the signal engine support incremental updates for time-series analysis?

Yes, the signal engine supports incremental updates, allowing you to process new OHLCV data appended to existing time-series without recalculating the entire historical fractal and stroke sequence.

Why do my Chan Theory buy sell signals output as 1, -1, or 0?

The signals output as 1, -1, or 0 to represent directional decisions where 1 indicates a buy signal, -1 indicates a sell signal, and 0 indicates a wait condition based on detected stroke patterns.