Engineering Alpha

The Engineering Mindset behind the Alpha Engineer Model

Most investors approach the market like weather forecasters—trying to predict what's coming next. I approach it like an engineer designing a bridge. The difference? Bridges work reliably for decades. Weather predictions barely work for next week.

After years of applying engineering principles to investing, I've built a systematic model that has generated exceptional returns. Today, I'm sharing the philosophy behind it—not the blueprints, but the mindset that makes systematic investing work.

Starting with the Right Problem

Engineers don't build solutions in search of problems. They start by defining exactly what needs to be solved. In investing, most people think the problem is "picking winning stocks." They're wrong.

The real problem is maximizing compounded annual returns over time while managing risk. This means generating alpha (returns above the market) without excessive volatility that destroys long-term wealth.

Here's a sobering example: If you gain 100% one year and lose 50% the next, you've made 0% despite averaging 25% yearly returns. But gain 40% and lose 20%? You're up 12% with just a 10% average. Volatility isn't just uncomfortable—it's mathematically destructive to wealth building.

Learning from the Masters

Good engineers don't reinvent the wheel. They study what works and improve upon it. My investment education included the obvious—Buffett's shareholder letters—but the real insights came from practitioners who blend systematic thinking with market wisdom, such as:

• Howard Marks on market cycles and risk ("The Most Important Thing")

• Seth Klarman on margin of safety

• Joel Greenblatt on systematic value investing

• Wesley Gray and Tobias Carlisle on quantitative approaches

• John Mihaljevic’s on proven frameworks for value investing

• Frederik Vanhaverbeke on generating excess returns

Each contributed pieces to the puzzle. But reading about investing is like reading about swimming—eventually, you need to test theories with real data.

Understanding the Constraints

Here's where engineering thinking becomes crucial. In engineering, constraints aren't obstacles—they're design parameters that lead to optimal solutions. The market has clear constraints that create opportunities:

Information Asymmetry: Not everyone has the same information at the same time. Large institutions focus on large caps where information flows freely. Small caps? That's where inefficiencies live.

Behavioral Biases: Even smart investors make emotional decisions. They overreact to news, chase trends, and panic during drawdowns. These patterns are predictable and measurable.

Structural Limitations: Mutual funds can't buy micro-caps. Pension funds can't hold volatile stocks. Index funds must buy overvalued stocks. Their constraints create systematic inefficiencies.

Understanding these constraints shaped every aspect of The Alpha Engineer model—from the universe I analyze to the factors I prioritize.

Building Robust Solutions

Engineers test extensively before deployment. A bridge gets stress-tested with computer simulations long before real cars drive across it. Same principle applies to quantitative investing.

I've built and tested dozens of strategies over the years. The ones that prove robust share common characteristics:

• They work across different time periods (10, 15, 20 years)

• They don't depend on outliers or perfect timing

• They align with fundamental business logic

• They can be implemented without constant monitoring

My systematic approach has generated 20-30% annual alpha versus relevant market indices in historical testing. The challenge is identifying which strategies hold up in real-world conditions with real money.

The Power of Systematic Methodology

The Alpha Engineer provides powerful tools for systematic investing:

• The Ranking System: Updated weekly, ranking thousands of stocks based on my quantitative model (check an example here).

• Buy/Sell Methodology: Clear guidelines on rank-based buying and selling

• Model Portfolio: A 50-stock portfolio that tracks the model's live performance

• Strategic Insights: Detailed analysis of how to adapt the approach to your preferences (check the research section)

I've published comprehensive guides on how subscribers can tune the methodology—whether you prefer concentration or diversification, higher or lower turnover, or specific market focuses.

Why I Won't Share the Secret Sauce

You might wonder why I don't detail the specific factors and weightings. Simple: That's what makes The Alpha Engineer valuable to subscribers.

Publishing the exact methodology would be like Coca-Cola publishing their formula. Once everyone knows it, the edge disappears. Joel Greenblatt’s famous Magic Formula proved this—stellar returns until the book came out, mediocre performance shortly after.

What I will share is this: The model evaluates thousands of stocks globally using factors across three pillars:

• Value: Identifying mispricing patterns

• Momentum: Measuring behavioral trends

• Quality: Assessing business fundamentals

Documentation and Transparency

Good engineers document everything. I track every model update, every refinement, every lesson learned. Subscribers benefit from this discipline through:

• Consistent weekly rankings

• Clear methodology documentation

• Detailed performance tracking of the model portfolio

• Transparent updates when changes are made

• Strategic insights explaining the reasoning behind the approach

The model you're using today reflects years of iteration and refinement. I don't plan fundamental changes—stability in methodology is crucial for consistent performance.

Using The Alpha Engineer Effectively

Understanding the philosophy helps to maximize the value:

1. Study the Methodology: My guides explain how to interpret rankings and apply buy/sell guidelines to your situation.

2. Understand Volatility: Individual positions identified by the model can swing wildly. Diversification matters.

3. Think Systematically: The power comes from following a consistent methodology, not from any individual pick.

4. Take a Long-Term View: Quantitative strategies should be evaluated over years, not weeks.

For Those Who Want to Build

While most subscribers leverage my model rather than building from scratch, some of you have the engineering curiosity to understand the process. Here's what's involved in quantitative model development:

Step 1: Define Your Universe
Start broad—cast a wide net across markets and cap sizes. You can always filter down, but starting too narrow means missing opportunities.

Step 2: Research and Select Factors
Study what has worked historically. Read academic research. Test individual factors across different time periods. Look for factors that make intuitive sense AND show statistical significance.

Step 3: Combine and Weight Factors
Build a ranking system that combines your factors. Start simple—complexity rarely adds value. Test different weighting schemes, but don't over-optimize.

Step 4: Backtest Rigorously
Test across multiple time periods. Include transaction costs. Watch for survivorship bias. Remember: past performance doesn't predict future results.

Step 5: Out-of-Sample Testing
Paper trade before implementing. Track meticulously. Be prepared for real-world performance to differ from backtests.

The Right Tools Make the Difference

This process requires robust data and testing capabilities. I use Portfolio123 for my quantitative modeling, which offers:

• 20+ years of point-in-time data preventing look-ahead bias

• Advanced ranking and screening tools for building complex models

• Realistic backtesting with customizable transaction costs

• Coverage across multiple markets (U.S., Canada and Europe - Asia coming soon)

What sets Portfolio123 apart is its accessibility to individual investors. While institutional platforms cost tens of thousands annually, Portfolio123 provides professional-grade tools at a fraction of the cost.

Get a 35-day free trial of Portfolio123 using my referral link - This gives you full access to explore the platform and test various strategies.

For full transparency, I document all my backtesting results using Portfolio123 and make these reports available to paid subscribers. For ease of retrieval, I also collect the backtest reports and performance analytics documents in my backtests reports archive.

Beyond Pure Quant: The Role of Stock Analysis

While I focus on systematic approaches as my foundation, there's real value in combining quantitative rankings with fundamental analysis—if done correctly.

Here's an interesting pattern from my data: about 15% of the top 100 ranked stocks reach +100% in less than 2 years. This shows why some investors find value in deeper analysis of high-ranking stocks of the weekly Alpha Engineer ranking.

How Rankings Guide Stock Selection

Using quantitative rankings as a starting point offers several advantages:

1. The model surfaces hidden gems—often obscure companies with compelling characteristics

2. High rankings indicate multiple factors aligning—value, momentum, and quality converging

3. The rankings reveal sector and geographic patterns—for instance, when Swedish and Polish stocks dominate the top 100 while US stocks are notably absent, or when precious metal miners cluster at the top, it signals where the best opportunities are concentrating

My Deep-Dive Analysis Process

For paid subscribers, I regularly publish deep-dive analyses on high-ranking stocks showing multibagger potential. They're comprehensive reports examining:

• Business models and competitive positions

• Financial metrics and trends beyond the quantitative factors

• Industry dynamics and potential catalysts

• Multiple scenarios with different assumptions

Understand my process here and check the latest reports in my deep-dives section.

The Discipline Factor

Combining quant rankings with fundamental analysis requires the discipline to:

• Define clear criteria (not just "interesting stories")

• Establish systematic evaluation methods

• Document the process consistently

• Maintain analytical frameworks

Without this discipline, analysis becomes speculation and mental biases will take over.

Remember: This is not personal advice. This is my process. Always do your own homework. All investing involves uncertainty. Past patterns don't guarantee future results, and even the most thorough analysis can't eliminate risk.

The Bottom Line

The Alpha Engineer demonstrates that systematic investing, guided by engineering principles, can consistently generate significant alpha. By identifying market constraints—information asymmetry, behavioral biases, and structural limitations—and building robust models around them, individual investors can access opportunities beyond the reach of institutions and large investors.

Success comes not from complex, fine-tuned algorithms or high-frequency trading, but from disciplined application of proven principles. Whether used purely systematically or combined with fundamental analysis, the engineering approach replaces emotional decision-making with repeatable processes—turning market volatility from an enemy into an opportunity.

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The Alpha Engineer — Investing with a quantitative edge

Disclaimer: The Alpha Engineer shares insights from sources I believe are reliable, but I can't guarantee their accuracy—data's only as good as its inputs! This content (whether on Substack, via email newsletters, X, or elsewhere) is for informational and educational purposes only—it's not personalized investment advice. I'm not a registered investment advisor, just an engineer crunching numbers for alpha. My opinions are my own and may shift without notice. Investing involves risks, including the chance of losing money. Past performance, whether from back-testing or historical data, does not guarantee future results—outcomes can vary. So, please consult your financial advisor to see if any strategy fits your situation. Full disclosure: I may own positions in the securities I mention, as I actively manage my own portfolio based on these strategies.