Chan Theory First Principles Analysis

Problem

Technical analysis often slips into subjective judgment: traders infer a turn from one opposite candle, revise the chart after market noise, or treat terms like divergence, pivot, and buy/sell points as slogans. chanlun-analysis targets this failure mode by forcing Chan Theory into a checkable workflow: separate facts, derivations, assumptions, and noise, then identify structure through fractal, stroke, segment, and pivot, and finally produce condition-based conclusions from trend divergence, range divergence, and the three buy/sell point types.

How It Works

The skill uses the 108 Chan Theory lessons as the rule base and adds a first-principles review: each judgment must be traced to an axiom, then decomposed into the smallest observable units such as candles, fractals, and strokes, rather than jumping to direction. Key steps include:

• Candle normalization: handle inclusion relationships before identifying top and bottom fractals
• Structure partitioning: build strokes from fractals and segments from strokes, with segments broken only by reverse segments
• Pivot identification: compute ZD, ZG, DD, and GG to classify consolidation or trend
• Divergence validation: compare entry and exit segment strength, supported by MACD histogram area and signal-line height
• Trade signal output: label first, second, and third buy/sell points with level, precondition, and stop-loss reference

Boundaries

It fits analysis tasks where conclusions must rest on data and rules, not on requests to predict short-term direction. Results still depend on complete candle data, correct level selection, and consistent structure partitioning; noisy data or mixed levels can invalidate third buy and divergence judgments. Use the output as a conditional interpretation, not as a trade order.