Polysemy in Trend Types

1. Today's Topic: Ambiguity in Trend Types

One of the core tenets of Chan Theory is the precise classification of trend types.

However, in practical application, many learners often encounter a confusion: Why can the same segment of a trend be classified into different types?

This is the issue of "ambiguity" in Chan Theory.

What is Ambiguity in Trend Types?

Ambiguity refers to the fact that during trend classification, due to different choices of central hub levels, the same segment of a trend can be given different type definitions.

For example:

In a trend segment, three overlapping zones appear. You can classify it as:

• A daily-level central hub extension (overlap of three consecutive sub-level trends)
• Two 30-minute-level central hubs (each hub formed by the overlap of three 5-minute-level trends)

Both classifications are correct. The difference lies in the observation level you choose.

The Root of Ambiguity

Ambiguity is not an error, but rather the inherent complexity of the trend itself reflected in the theory.

Chan Zhong Shuo Chan repeatedly emphasized: Trends exist at multiple levels. Any trend simultaneously exists across multiple levels. What you see is only the level you choose to observe.

There are three roots of ambiguity:

1. Blurred boundaries between central hub extension and expansion: When a central hub extends for 9 segments, it upgrades to a higher-level hub—but the starting point for counting "9 segments" can sometimes be traced back.
2. Flexibility in defining sub-level trends: The choice of starting point for a sub-level trend can affect the boundary of the current-level central hub.
3. Nesting of timeframes: Larger-level trends contain smaller-level structures, and smaller-level structures in turn affect the shape of larger-level trends.

How to Handle Ambiguity?

Chan Theory provides clear principles for dealing with it:

1. Choose Your Operating Level

Ambiguity becomes a "problem" often because you haven't fixed your own operating level.

If you are a daily-level operator, focus on the daily-level central hub and ignore smaller fluctuations. Ambiguity disappears through natural selection.

2. Recursive Definition of Levels

The core of Chan Theory is recursion: starting from the lowest level (e.g., 1-minute), define higher-level central hubs step by step. Each step strictly depends on the previous level, and ambiguity is constrained in the recursion.

3. Accept "Close Enough"

Chan Theory is not a mathematical proof, but a practical tool. In certain borderline cases, both classifications are reasonable—at such times, choosing one and sticking with it is more meaningful than agonizing over "which is absolutely correct."

2. Practical Chan Theory Skills: Multi-Level Observation Framework

Facing ambiguity, a practical solution is to build a multi-level observation framework.

| Level | Role | Focus of Observation | | ------------------------------ | -------------------- | ------------------------------------------------------ | | Operating Level (e.g., 30-min) | Decision core | Central hub position, trend type, buy/sell points | | Higher Level (e.g., daily) | Direction constraint | Major trend, major hub, key support/resistance | | Lower Level (e.g., 5-min) | Precise entry | Sub-level structure, detail verification |

Practical Points:

1. Higher Level Sets Direction

Signals at the operating level must not conflict with the direction of the higher level. If the daily level is in a downtrend, a "buy point" at the 30-minute level requires extra caution—it may be a rebound, not a reversal.

2. Lower Level Finds Precise Points

After a signal appears at the operating level, go to the lower level to find a precise entry point. The completion of a lower-level structure validates the effectiveness of the operating-level signal.

3. Ambiguity Naturally Dissolves in the Framework

Once you fix your "operating level," ambiguity no longer troubles you. Because the alternative classification you might have chosen belongs to a "non-operating level"—it is worth observing, but it does not drive your decisions.

3. Market News (June 29, 2026)

📊 News 1: Rapid Profit Growth in the Electronics Industry

The National Bureau of Statistics released industrial enterprise profit data for January–May 2026. Profits of scale-above equipment manufacturing grew 14.1% year-on-year, with profits in the electronics industry surging 103.9%, contributing 43.1% to the total profit growth of all scale-above industrial enterprises. The global AI technology transformation driving explosive demand for high-end computing chips and memory chips is the main driver.

🌍 News 2: Signs of Détente in U.S.-Iran Situation

The U.S. and Iran plan to meet this Tuesday in Doha, the capital of Qatar, to resolve disputes surrounding the Strait of Hormuz. This meeting arrangement represents new progress in the implementation of the cease-fire agreement between the two sides.

📅 News 3: Dense Release of Important Economic Data This Week

China's June manufacturing and non-manufacturing PMI data will be released today; the U.S. June non-farm payroll report will be out on Thursday; the Fed Chair will speak at the ECB Forum. Markets are closely watching these data as they may influence subsequent monetary policy expectations.

4. Cultivating the Mind: Accepting Market Uncertainty

The existence of ambiguity reminds us of a profound truth:

The market is not certain, and your analysis cannot be the only correct one.

True cultivation of the mind is to accept:

• The same trend segment can be viewed differently
• Different views can all be profitable—as long as you execute properly
• Losses are not due to "wrong classification," but to "failure to follow your own rules"

The market is like Chan. Only by not clinging to "being right" can you see the "truth."

Chan has no measure · Proving Chan with Chan Disclaimer: This article is a technical exchange on Chan Theory and does not constitute any investment advice. Markets involve risk; operate with caution.