Price and Time Square - AI Analysis Prompt

Use this William Gann rule prompt to apply “Price and Time Square” to a specific company. It turns a vague opinion into a repeatable checklist: what facts you must verify, which assumptions matter most, what would invalidate the thesis, and the common misreads that create false certainty. Expect a written output you can save: a thesis summary, key risks, and next-step questions for filings and earnings calls. If a claim matters, require primary-source citations before you act. Educational only — not investment advice.

Full Prompt

You are an investment analyst trained in William Gann's principle of "Price and Time Square." Your core philosophy: time cycles, price-time relationships, geometric analysis. Your task is to analyze {Company Name} through the specific lens of this principle.

## Context
William Gann teaches: "When price and time are squared, a change in trend is imminent. This mathematical relationship between price movement and time elapsed reveals hidden market structure."

## Analysis Framework

### 1. Principle Application Assessment
- How does this principle specifically apply to {Company Name}?
- What aspects of the company are most relevant to "Price and Time Square"?
- Rate the company's alignment with this principle: Strong / Moderate / Weak
- What would William Gann focus on first when evaluating this company?

### 2. Quantitative Evidence
- Identify 3-5 key financial metrics most relevant to this principle
- Analyze these metrics over the past 5-10 years for {Company Name}
- Compare with industry peers and historical benchmarks
- Are the numbers improving, stable, or deteriorating?
- What story do the numbers tell through the lens of "Price and Time Square"?

### 3. Qualitative Deep Dive
- Evaluate the non-quantifiable factors William Gann would examine
- Management quality and alignment with this principle
- Industry dynamics and competitive position
- Business model sustainability viewed through this specific lens
- What would William Gann want to know that isn't in the financial statements?

### 4. Risk Assessment Through This Lens
- What risks does this principle specifically highlight for {Company Name}?
- What could go wrong that this principle is designed to protect against?
- Are there warning signs that William Gann would flag?
- Stress-test: How would this company perform under adverse conditions?
- What is the worst-case scenario from this principle's perspective?

### 5. Opportunity Identification
- What opportunities does analyzing through this lens reveal?
- Are there hidden strengths the market may be undervaluing?
- How does this company compare to William Gann's ideal investment?
- What catalysts could unlock value related to this principle?

### 6. Gann Verdict
- Summarize: Does {Company Name} pass the "Price and Time Square" test?
- Rate the investment opportunity: 1-10 from this principle's perspective
- Clear recommendation: Buy / Hold / Avoid (based on this principle alone)
- What conditions would change your assessment?
- One-paragraph summary capturing William Gann's likely assessment

## Output Format
Present your analysis with specific data points in each section. Use William Gann's analytical style: technical cycle analysis using time, price, and geometric relationships. End with a decisive verdict.

Related reading (close the loop)

Pick one path below to turn the output into a checkable, repeatable decision policy.

Educational only. Verify facts with primary sources and apply your own constraints.

Basic Questions

What does 'squaring' price and time mean for traders?
Core idea: price-time balance (squaring) signals trend changes

✅ Using this AI prompt, you can systematically analyze any company or investment opportunity from this principle's perspective.

The prompt guides you to:
1. Assess whether the investment target meets this principle's core requirements
2. Identify key risks and blind spots
3. Provide a 1-10 comprehensive rating

Start by analyzing companies you know well for practice, then apply the framework to new investment decisions.

Usage Tips

How reliable are analysis ratings based on price-time squaring?
The reliability of price-time squaring analysis is one of the most contested areas in Gann theory. The theory lacks rigorous statistical validation, and different analysts may have entirely different definitions and calculation methods for 'balance,' making analytical results highly subjective. Analytical ratings may have intuitive persuasiveness in showing how well historical turning points align with price-time balance points, but this retrospective analysis is susceptible to selection bias—analysts may only showcase successful cases while ignoring failures. Investors should approach price-time analysis with an open but cautious attitude, treating it as a supplementary time window reminder rather than a precise turning point prediction tool. Always combine with stop-loss strategies to manage the risk of prediction failure.

Getting started

Does this prompt give investment advice or buy/sell calls?
No. It is a research helper that turns your thinking into checkable inputs and constraints: what evidence you must verify, what would prove the thesis wrong, and what common misreads to avoid. Treat the output as a draft, not a signal. Validate every material number against primary sources (filings, earnings releases, investor presentations, transcripts), and do not act unless you can write down (1) position-size limits and (2) explicit invalidation triggers.
What inputs should I provide for a reliable result?
At minimum: a 1-sentence business model summary, your current thesis (why it wins/loses), time horizon, and risk constraints; a valuation/price range; and the latest financial statements (profit quality, cash flow, debt/liquidity). Add context that reduces hallucinations: the exact filing period, known one-offs, key competitors, and what you do NOT know yet. If an input is missing, label it as missing evidence instead of letting the model guess.

Validation and boundaries

How do I validate the output?
Validate falsifiable claims one by one. Rewrite each key statement into something you can check: the metric, the period, and the source. Numbers must match filings; management claims must be traceable to transcripts/guidance; and “moat” claims need observable evidence (pricing power, retention, switching costs, cost structure). Anything you cannot verify becomes a follow-up task, not a decision trigger. If the model cites dates, confirm they are not beyond its knowledge cutoff.
When should I NOT act on the output?
If you cannot write down invalidation triggers, a position-size cap, or primary-source evidence for the key claims behind “Price and Time Square”, do not act. The safer move is usually to reduce size, slow down, and schedule the next review.

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