Chapter 4: Investment Strategy

4.1. Introduction: discipline first

This chapter describes the investment logic that guides my buy and sell decisions for stocks. It is not financial advice. It formalizes a personal method built around one simple objective: maximize returns while protecting invested capital.

This strategy is intentionally systematic. By writing my rules clearly, I reduce the emotional component of decisions, improve consistency, and prepare the ground for AI assistance that can support me in line with my logic.

4.2. Scope: where and what I invest in

My investment universe is deliberately constrained to stay readable and manageable:

• Markets: Canadian and U.S. equity markets, namely the TSX, the S&P 500, and the Nasdaq.
• Asset class: strictly stocks. I do not trade other instruments (bonds, options, derivatives), in order to focus on the highest return potential.

This concentration is a deliberate choice: it is better to deeply understand a focused field than to spread across instruments I control less.

4.3. The core principle: secure first, grow second

My strategy is built on one central idea: a winning position should first repay its own initial stake before targeting more upside.

In practical terms, when a position reaches a sufficient gain (typically 100%), I apply a two-step rule:

1. Secure initial capital. I sell 50% of the shares in that transaction. This sale allows me to recover the initial stake. From that point onward, initial capital is protected: whatever happens next, the position can no longer lose the money originally committed.
2. Grow the remaining portion. The remaining 50% keeps working. I sell it progressively as the signals below appear.

This approach turns a winning position into a "no initial-capital-loss" setup, which lets me ride upside with greater composure.

4.4. Progressive selling of the remaining portion

For the half I keep, I do not target a single exit point. I sell in stages, based on one or more converging signals. A sell decision is triggered when one or more of the following criteria are met:

• Additional +100% return. An extra 100% gain on the position, i.e., a total return around +200% versus my initial entry price.
• Analyst upside. The highest analyst target (Highest Price Target). As price approaches or reaches it, expected upside narrows.
• High historical performance. The strongest performance observed over the last 5 years, used as a reference to assess current exuberance.
• Exceptional momentum. A strong investor appetite for a stock or sector. Example: exceptional demand for AI-related stocks during the first half of 2026.

These criteria are not mutually exclusive: the more they converge, the stronger the sell signal.

4.5. Pullback rule: reinforce on market weakness

Markets do not move in straight lines. My strategy therefore includes a mechanism to use drawdowns constructively.

When a stock drops 20% versus my last transaction, I consider a new transaction by buying an additional lot of shares. This reinforcement lowers my average cost basis and positions the trade for a potential rebound.

4.6. The isolated-decision principle

This is the most important part of my method, and probably the least intuitive.

Each new decision on a stock deliberately ignores earlier transactions. I do not reason from my full cumulative history on that ticker. I reason only from the performance of my most recent transaction.

From that most recent transaction, two paths are possible:

• Price doubles -> the position becomes a "Gain to secure". I then apply the core rule again: sell 50% to secure capital, then let the rest grow.
• Price drops another 20% -> the position becomes a new "Stock to reinforce or liquidate". I must then decide whether to reinforce further or exit.

This "transaction by transaction" framing keeps decisions simple, readable, and consistent over time, without being trapped by the psychological weight of past gains or losses.

4.7. Assessing stock quality

When a stock enters the "reinforce or liquidate" category, deciding to reinforce (buy more) or liquidate (exit) is not random. It depends on the stock's perceived quality at decision time.

I assess quality through four factors:

• The company: reputation, strength of products and services, and competitive position.
• Analyst recommendations: consensus and price targets reflecting professional market views.
• Performance history: the stock's long-term trajectory, as a resilience indicator or warning sign.
• Industry context: the sector's structural dynamics. Example: pharmaceutical stocks often take too long to become profitable, if they do at all, which weighs negatively in my assessment.

A high-quality stock experiencing a temporary drawdown can justify reinforcement. By contrast, uncertain quality in a prolonged drawdown is more likely to favor liquidation.

4.8. Strategy summary

In short, my investor logic can be read through a few clear principles:

• Focus on Canadian and U.S. equities for maximum return potential.
• Secure initial capital as soon as a position becomes strongly profitable (around 100% gain), by selling 50%.
• Let the remainder grow and sell it progressively on converging signals.
• Reinforce on a 20% pullback by adding a new lot.
• Decide transaction by transaction, without over-weighting previous decisions on the same stock.
• Choose reinforce vs liquidate based on the stock's actual quality at analysis time.

This discipline is the foundation that will feed the project's indicators and future AI assistance, so every recommendation stays aligned with the way I think about investing.

4.9. My main indicators

This section explains in detail the indicators added to the application. Each one translates a principle of my strategy into an automatic signal computed from market and portfolio data. The goal is twofold: make each signal transparent (we know exactly when and why it is triggered) and actionable (it points to a concrete buy, sell, or watch decision).

4.9.1. Analyst target benchmark

What analysts publish. Financial analysts covering a stock regularly publish a target price range: a lower bound and an upper bound representing their estimate of fair value.

What the application computes. The application compares the current price directly against the extreme bounds of this range and flags two limit situations:

• Price equal to or above high target. The current price reaches or exceeds the highest target published by analysts. This can be a possible overvaluation signal: expected upside narrows or is exhausted.
• Price equal to or below low target. The current price sits at or below the lowest target published by analysts. This can be a possible undervaluation signal or an overlooked opportunity.

How to read the signal. The farther the price is beyond the bound (high or low), the more the stock is highlighted: the most extreme cases are ranked first. A ticker appears only once, in its most extreme state.

Validity conditions. The indicator is computed only when all three values (price, low target, high target) are available and consistent, especially when low target is less than or equal to high target. Without that, no signal is emitted to avoid conclusions from incomplete data.

Interpretation limits. This signal reflects analyst views at a given moment; it does not explain the reason for the gap (good news not yet integrated, overly cautious consensus, or real business weakness). It is a starting point for analysis, not a verdict.

4.9.2. Contrary to expectations

What the indicator detects. This indicator captures a contradiction between two signals that usually move together. It is triggered only when both conditions are met at the same time:

• Investor sentiment is negative or very negative (the market expects weak performance).
• Yet portfolio gain increased by more than 50% over the period.

Why this matters. This gap suggests the stock is performing better than consensus expectations. It may indicate a trend reversal, or simply that the market has not fully priced in new information yet.

How to read the signal. Stocks are ranked by the magnitude of the gain: the larger the gain despite negative sentiment, the stronger the divergence and the higher the rank. Each ticker appears once, in its most extreme state.

Interpretation limits. A divergence is not a guarantee. Sentiment may eventually be right, or the gain may be temporary. The indicator invites a deeper review of stocks that are decoupling from consensus, without assuming the next move.

4.9.3. P/E ratio

What the ratio measures. The price-to-earnings ratio (P/E) relates stock price to earnings per share. It shows how much the market is willing to pay for each dollar of earnings.

How to read it.

• A high P/E usually reflects expectations of strong future earnings growth. It can also indicate the stock is overvalued.
• A low P/E can suggest a bargain, but it can also reflect underlying business difficulties.

What the application provides. The application highlights stocks with the highest and lowest P/E values and computes a portfolio-level weighted view. Only strictly positive ratios are kept: zero or negative P/E values (loss-making firms or unusable data) are excluded from this comparison.

Interpretation limits. P/E is meaningful only in relative comparison - against sector peers or the stock's own history. The same P/E level can be justified for a high-growth company and excessive for a mature one. This indicator must therefore always be read in context, never in isolation.