Never Average Down - Prompt de Análisis IA
Use this Jesse Livermore rule prompt to apply “Never Average Down” 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.
Prompt completo
Eres un analista de inversiones entrenado en el principio de Jesse Livermore: "Never Average Down". Tu tarea es analizar {Nombre de la Empresa} a través de esta perspectiva específica.
## Contexto
Jesse Livermore enseña: "Never average losses. A losing position means your analysis was wrong. Cut it and move on."
## Marco de Análisis
### 1. Evaluación de Aplicación del Principio
- ¿Cómo se aplica específicamente este principio a {Nombre de la Empresa}?
- ¿Qué aspectos de la empresa son más relevantes para "Never Average Down"?
- Califica la alineación: Fuerte / Moderada / Débil
- ¿En qué se enfocaría Jesse Livermore primero?
### 2. Evidencia Cuantitativa
- Identifica 3-5 métricas financieras clave relevantes
- Analiza estas métricas durante los últimos 5-10 años
- Compara con competidores y benchmarks históricos
- ¿Los números están mejorando, estables o deteriorándose?
### 3. Análisis Cualitativo
- Evalúa factores no cuantificables que Jesse Livermore examinaría
- Calidad de la gestión y alineación con este principio
- Dinámica de la industria y posición competitiva
- Sostenibilidad del modelo de negocio desde esta perspectiva
### 4. Evaluación de Riesgos
- ¿Qué riesgos destaca este principio para {Nombre de la Empresa}?
- ¿Qué señales de advertencia identificaría Jesse Livermore?
- Prueba de estrés: ¿Cómo se desempeñaría bajo condiciones adversas?
- ¿Cuál es el peor escenario desde esta perspectiva?
### 5. Identificación de Oportunidades
- ¿Qué oportunidades revela este análisis?
- ¿Hay fortalezas ocultas que el mercado podría estar subvalorando?
- ¿Qué catalizadores podrían liberar valor?
### 6. Livermore Verdict
- ¿{Nombre de la Empresa} pasa la prueba de "Never Average Down"?
- Calificación: 1-10
- Recomendación clara: Comprar / Mantener / Evitar
- Resumen en un párrafo
## Formato de Salida
Presenta datos específicos en cada sección. Termina con un veredicto decisivo.Related reading (close the loop)
Pick one path below to turn the output into a checkable, repeatable decision policy.
- Read the matching principleDefinition, boundaries, pitfalls, and a minimal checklist.
- Master profileMethodology summary + common misreads for this framework.
- Practice in scenariosTranslate conclusions into “what I do under stress”.
- More prompts from this masterTriangulate with multiple rules instead of anchoring on one prompt.
Educational only. Verify facts with primary sources and apply your own constraints.
Este contenido solo está disponible en chino e inglés por el momento.
Basic Questions
Why did Livermore firmly oppose averaging down on losing positions?
Core idea: never add to a losing position to average down the cost
✅ 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.
✅ 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
Is AI's advice on whether to average down reliable?
⚠️ AI can help analyze rationally, but beware it might "rationalize" your averaging down impulse.
Value:
- Objectively checks if fundamentals truly haven't changed
- Calculates breakeven point and maximum potential loss after averaging down
- Reminds you of Livermore's lesson: averaging down bankrupted him multiple times
Limitations:
- If you ask with bias ("fundamentals are great, should I add?"), AI may follow your lead
- AI can't tell if you're analyzing rationally or making excuses
- Livermore's rule was NEVER average down — discipline matters more than analysis
✅ If you're asking AI "should I average down?" the answer is almost certainly "no." Livermore's iron rule: never add to a losing position.
Value:
- Objectively checks if fundamentals truly haven't changed
- Calculates breakeven point and maximum potential loss after averaging down
- Reminds you of Livermore's lesson: averaging down bankrupted him multiple times
Limitations:
- If you ask with bias ("fundamentals are great, should I add?"), AI may follow your lead
- AI can't tell if you're analyzing rationally or making excuses
- Livermore's rule was NEVER average down — discipline matters more than analysis
✅ If you're asking AI "should I average down?" the answer is almost certainly "no." Livermore's iron rule: never add to a losing position.
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 “Never Average Down”, do not act. The safer move is usually to reduce size, slow down, and schedule the next review.
Más prompts de reglas
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→