My Universe of Stocks

My quantitative model casts a wide net, analyzing nearly 15,000 stocks across the US, Canada, the UK, Europe, Norway, Switzerland, and Israel. From this vast universe, I identify approximately 3,700 stocks that meet specific market and quality constraints (e.g. No OTC stocks, acceptable leverage etc.). About 2,500 of these are what I consider "liquid" – trading with at least $50,000 average daily volume and a price above $0.5. Nearly all are accessible through Interactive Brokers, my primary brokerage.

My Ranking System

I apply a multi-factor ranking to these 3,700 qualified stocks, focusing on the metrics that have historically generated the strongest alpha. The top 5% of this ranking contains about 50 liquid stocks, which forms the potential buy list. I update the entire ranking weekly to ensure I'm always working with the most current data. Check my ranking example for a typical weekly stock ranking post.

The graph below shows the performance of a purely mechanical strategy: buying the top 50 liquid stocks and selling them when they drop below a certain ranking threshold (or drop from the ranking). This approach demonstrates the power of the "quantitative edge" without any subjective input. I track this 50-stock model portfolio as a benchmark to showcase the strategy’s average performance. All transactions for this benchmark portfolio are provided on Sunday prior to buying or selling on Monday, and you can follow its performance in the Portfolio section. Subscribe for access.

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Adding a Qualitative Edge

My main stock portfolio follows a purely quantitative approach – buying the top-ranked stocks without subjective judgment. However, I also maintain a separate, smaller “deep-dive” stock portfolio where I conduct detailed qualitative research on select top-ranked stocks. This additional analysis helps me identify potential multi-baggers – stocks with +100% upside potential – which I showcase in detailed analyses in the Deep-Dive section. This approach allows me to benefit from both the systematic edge of the quantitative model and the deeper insights that come from thorough company research. For more detailed information, check my process for deep-dive reports.

Portfolio Construction

For my main portfolio, I maintain 20 stocks – a sweet spot I've found through years of testing. A smaller portfolio increases volatility, while a larger one becomes unwieldy to manage. You could reasonably go with 15 or 25 stocks as well, depending on your preferences.

My Buy Process

With proceeds from sales, I purchase new stocks from the buy list. I allow position sizes to reach up to 1.5x the ideal allocation (5% for a 20-stock portfolio). This means the maximum position size for a new purchase would be 7.5%. I keep any remaining cash and dividend income in reserve for future opportunities.

My Sell Strategy

I sell stocks when their ranking falls below my threshold or when they drop from the ranking entirely (no longer have a ranking).

The selling threshold directly impacts portfolio turnover and performance. For a threshold between 90% and 70%, turnover varies roughly between 200% to 100%. Finding the right balance depends on transaction costs, tax situation, and the time available to manage the portfolio.

A Weekly Schedule

I follow a weekly schedule for transactions based on the updated ranking. Alternatively, reviewing every 2, 3, or 4 weeks (or monthly) is possible, typically with up to a few % reduction in performance but also reduced time commitment.

My approach is essentially "set and forget" for each transaction:

• I set limit orders at or near the closing price

• I don't chase stocks if my orders don't fill

• I wait for the next ranking update to reassess

• I'm comfortable holding partial positions when necessary

My Weekly Process in a Nutshell

1. Each Monday: Review the updated ranking of stocks

2. Sell: Any stocks that have fallen below my ranking threshold or are filtered out (or occasionally to make room for an exceptional new opportunity)

3. Buy: New top-ranked stocks with the proceeds

   • Up to 1.5x the nominal position size

   • Nominal position size = portfolio value ÷ number of stocks

This disciplined, systematic approach removes emotion from the investment process and lets the quantitative edge work in my favor over time.

Fine-Tuning to Personal Preferences

While the core methodology is systematic, several parameters can be adjusted to match your personal situation – including portfolio size, sell threshold, and transaction frequency. I’ve written a detailed guide on how to fine-tune the method to your preferences, and you’ll find additional research on optimization approaches in the Research section.

To make implementation as straightforward as possible, I provide the Portfolio Manager tool, which guides you through the weekly stock transactions based on the updated rankings. I also have an FAQ page. I’ll update this regularly as new questions arise.

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Paid Subscription Benefits:

• Weekly Stock Rankings — My proprietary ranking of approximately 3,700 stocks updated every week, covering US, European, UK, and Canadian markets.

• Methodology & Tools — Access to my complete methodology, portfolio manager tool, and ongoing research for implementation.

• Model Portfolio — Full transparency on the 50-stock portfolio transactions and performance tracking.

• Deep-Dive Analysis — Detailed research on potential multibagger candidates that survive both quantitative and qualitative filters.

• Direct Access — Reach me via direct message and the paid subscriber chat, where I share what I’m working on, flag upcoming articles, and announce when I buy or sell deep-dive positions in real time.

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.

Understanding the Method

Q: Do you follow the trades of the model portfolio?

No. I follow the method, not the specific trades of the model portfolio. The model portfolio tracks the general performance of the quantitative approach using 50 stocks. I personally hold only 20 stocks, which provides good results with better manageability (see my fine-tuning analysis for details). My personal results align completely with what the strategy should produce.

Q: What’s the difference between the method and your deep-dive research?

The quantitative Alpha Engineer method is the core of my stock investment strategy and the foundation of this blog. The deep-dives are complementary research for paid subscribers—the icing on the cake.

Not everyone follows the method exactly. Some subscribers use the ranking as an additional tool in their stock picking process, combining it with other screeners and checklists. I add value through selective research on specific top-ranked stocks, and I have skin in the game by investing in these separately in my deep-dive portfolio.

In the long run, I’ll discover whether this portfolio adds value beyond the quantitative method. Many successful investors with quantitative strategies have found that beating their ranking model through discretionary selection is difficult. I try to do better than my model by posting only high-conviction ideas. As a result, I can’t post ideas regularly—there may be 5 or 10 in a given year, depending on what I discover.

Stock Selection

Q: Which stocks do you buy from the ranking?

The ranking is alphabetical within each percentile rank, and my actual selection process is random. When I need to buy a stock, I choose randomly from available liquid stocks at percentile rank 99. If none are available that I don’t already hold, I move to rank 98.

About 5% of the time, I can’t buy a chosen stock (my broker doesn’t support that exchange). In those cases, I simply move to the next stock.

Q: Why are stocks with the same rank listed alphabetically?

The ranking has a very broad top. This is why selecting just the top 50 or even top 100 stocks delivers similar returns as selecting only 20 stocks. Going for the absolute highest-ranked stocks within the percentile rank 99 group doesn’t make sense. If that were the process, the method’s performance would decline if too many paid subscribers chased the same stocks on Monday morning. The alphabetical ordering within percentiles prevents this crowding issue.

Q: What if too many people follow the strategy?

Successful blogs posting microcap stocks with limited liquidity will influence prices if many followers make the same trades, limiting returns for those buying or selling late. The Alpha Engineer method reduces this risk because:

• Not everyone buys or sells the same stocks at the same time

• The ranking has a broad top with many equivalent options

• The random selection process distributes demand

• Stocks that are liquid one week might be illiquid another week, so the buy list is dynamic due to rank and liquidity changes

To ensure future alpha generation in case of huge success, I intend to set a cap on the total number of paid subscribers.

Q: What about concentration risk?

I don’t deviate from the “random selection” discipline to ensure even distribution across sectors or countries. I let the ranking decide which stocks to buy. This means I may hold more stocks from Europe than the US, or more from certain sectors than a typical market index distribution.

This makes sense. Some countries and sectors score higher at certain times, so my holdings should reflect that. On average, a 20-stock portfolio with rank 90 as the sell criterion sells stocks every 6 months—about 40 trades per year. Chosen randomly, this produces a representative sample of the major sectors and countries at the top of the ranking.

Q: What about trading costs for certain markets?

This depends on your broker and position size. For me, Canadian and UK stocks (due to the 0.5% stamp duty) cost more to trade than most US and EU markets, but not enough to exclude them from selection.

Backtests show that an average trade returns 16% in 6 months including 2% transaction costs (1% buying, 1% selling). If transaction costs are 2% instead of 1%, that return drops to 14%—still worthwhile.

Some investors exclude specific markets if costs are significantly higher. For investors with larger restrictions, focusing on specific markets (US only, UK only, EU only, etc.) still works but with somewhat lower return and risk-adjusted return expectations (see my market impact analysis).

Portfolio Construction

Q: How much do you invest in each position?

I start with equal positions: 20 stocks at 5% of total allocation each. When a stock drops below my ranking threshold (or loses its rank), I sell it and buy a new stock with the proceeds. If I need to sell two stocks, I buy two stocks splitting the proceeds equally. I follow the same process for multiple sales.

The stocks generate dividends—currently about 1.5% yield for the model portfolio. Other portfolios created from the ranking will have similar yields. I keep dividends in the portfolio and add them to the cash balance for the next purchases.

When buying new stocks, I apply minimum and maximum position sizes:

• Minimum position: 2.5% of portfolio value (0.5 × average position)

• Maximum position: 7.5% of portfolio value (1.5 × average position)

• Average position: Total portfolio value ÷ target number of stocks

Sometimes a stock sale doesn’t provide enough cash for a full new position, or provides too much. In both cases, I leave cash in the portfolio for the next purchases. The model portfolio uses the same method. An alternative approach is adding the cash to top-ranked positions if you prefer not holding cash for a week or two.

Q: Should I buy all portfolio stocks at once when starting?

I did that initially, though some investors prefer dollar-cost averaging to build the portfolio over time if they believe market risks may provide better prices in the future. For example, you could buy a few stocks each month and slowly build a 20-stock portfolio over several quarters. In the meantime, simply trade the stocks you own following the method.

Trading Mechanics

Q: How do you buy stocks?

I set a limit order prior to market open at the latest transaction price. I might set it slightly above that price if I have a large order relative to the previous day’s volume, but typically I only adjust the next day if the order didn’t succeed or only partially filled.

I try to buy at that slightly higher price during the week. I never raise my price more than 2% to catch a stock that’s running away (which does happen). I check the ranking the following week to determine whether to place the higher-priced order or abandon that stock and choose another.

This means I sometimes have partial stock positions. I generally consider them full positions unless they’re too small (less than a half position). In those cases, I may sell them immediately—typically they’re already significantly higher in price, so they’re a win.

Note that the model portfolio also includes 1% transaction costs and slippage and buys/sells at the next day’s average transaction price to correspond to real trading.

Q: How do you sell stocks?

I follow a similar process. I set a limit order prior to market open at the latest transaction price. The next day, I check if the order succeeded. If not, I sell at a limit price up to 2% lower. If I still can’t sell, I check the following week to see if it makes sense to sell at the new lower price.

In general, stocks that drop below my ranking threshold don’t return immediately (as momentum fades), so I normally sell those stocks the next week—sometimes at a significantly lower price. This happens, but it also happens in the model portfolio, which sells at the average price of the next day.

Special Considerations

Q: What about the 3-month holding period?

The model portfolio has a 3-month holding period. Stocks are held for 90 days before being considered for selling based on their ranking. This reduces turnover slightly without impacting return (check the fine-tuning post for details). Since transaction costs are already included in the return calculations, the actual benefit is zero. My main purpose was to demonstrate that the method relies on longer-term price appreciation, not short-term trading.

For my personal portfolio, I don’t use a 3-month holding period. I simply sell based on ranking. The portfolio manager tool also doesn’t account for a holding period. If you use the portfolio manager with a holding period, you would need to disregard sell signals until the stock is available for selling.

Q: Will my subscription price change?

Subscriptions at Substack renew at your original rate as long as you don’t cancel and resubscribe. As long as you maintain your monthly or yearly subscription, your price remains the same.

I do plan to update pricing yearly for new subscribers to account for inflation and the growing value of the service. The earliest subscribers will benefit the most, as it should be. Pricing for 2025 was $39/month or $390/year. In 2026, subscriber pricing increased to $45/month or $450/year.

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Learn more about my investing framework:

• Read about my overall portfolio strategy.

• See the full details of my systematic method for stock picking.

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.

In my article Engineering Alpha, I covered the philosophy behind systematic investing—why an engineering mindset works, how to think about factors, and the general process of developing a quantitative approach. This article is an introduction to Portfolio123, the platform I use to build and test my quantitative models.

Fair warning: this process takes significant time and effort to master. But understanding it will make you a better investor regardless of whether you build your own model or leverage mine.

The Building Blocks: Universes and Ranking Systems

Portfolio123 organizes quantitative model development around two core components: universes and ranking systems. Master these, and you have the foundation for systematic investing.

Universes: Defining Your Hunting Ground

A universe defines which stocks your model will consider. This sounds simple, but it’s where many DIY quants make their first mistake—starting too narrow and missing opportunities, or too broad and drowning in noise.

Portfolio123 lets you build universes using filters across multiple dimensions. You can filter by country (US, Canada, Europe, UK), exchange (NYSE, NASDAQ, TSX, LSE…), sector, industry, and market cap. But the real power comes from adding quality constraints that eliminate stocks likely to cause problems.

Two examples:

• No OTC stocks: These trade outside major exchanges with limited liquidity and limited reporting standards

• No penny stocks: Stocks under $1 (or $0.5) face higher delisting risk, have higher bid spreads and may attract manipulation

The universe definition is where you balance opportunity against practicality. Define it too broadly and your model fills with untradeable stocks. Define it too narrowly and you miss the inefficient corners where alpha lives.

Ranking Systems: Testing Your Hypotheses

The ranking system is the core engine of quantitative investing. This is where you translate investment hypotheses into testable, systematic rules.

A ranking system combines multiple factors such as value metrics, momentum indicators and quality measures. It assigns each stock in your universe a composite score. Portfolio123 lets you weight factors, create custom formulas, and test how different combinations perform.

The process typically works like this:

1. Start with a hypothesis (e.g., “stocks with high EPS growth during the last quarter outperform”)

2. Define the factor mathematically

3. Test that single factor against your universe over multiple time periods

4. Observe whether top-ranked stocks outperform bottom-ranked stocks

5. Combine successful factors into a composite ranking

6. Test the combined ranking against individual factors

What separates rigorous quant research from data mining is this: factors should make intuitive sense AND show statistical significance across different periods. A factor that only worked from 2010-2015 isn’t a discovery—it’s probably noise.

Portfolio123’s ranking system tools let you see performance by bucket—deciles (top 10%, next 10%, etc.), or any size you choose (1%, 5%, etc.)—which reveals whether a factor truly differentiates winners from losers or just identifies extremes.

Testing Your Model: Simulations and Live Strategies

Once you have a universe and ranking system, the bucket tests show whether your factors generate returns. But simulations and live strategies let you see what to expect in a real portfolio—with position sizing, rebalancing, and transaction costs. Portfolio123 provides two approaches: simulated strategies (backtests) and live strategies.

Simulated Strategies: Backtesting Your Approach

A simulated strategy takes your ranking system and tests it as if you had traded it historically. You define the rules—how many stocks to hold, when to buy, when to sell—and Portfolio123 calculates what your returns would have been.

Some key parameters you’ll configure:

• Number of holdings: 20, 50, 100 stocks? More holdings reduce volatility but dilute alpha and increase portfolio management

• Rebalancing frequency: Weekly, monthly, quarterly? More frequent could mean higher return but also higher turnover and portfolio management

• Buy rules: Minimum rank to purchase, liquidity thresholds, maybe sector limits for diversification…

• Sell rules: Rank drops below a threshold, holding period limits, special situations (e.g. merger)…

The sell rules deserve special attention. Many DIY quants focus obsessively on what to buy but treat selling as an afterthought. In my experience, disciplined selling matters as much as stock selection. A rank-based sell rule (e.g., sell when a stock falls below the top 10%) systematically removes deteriorating positions.

Portfolio123 includes realistic transaction cost modeling which is essential for strategies involving small-cap or micro-cap stocks where spreads can be significant.

The Backtesting Trap

Here’s where I must be direct: backtesting is seductive and dangerous. It’s trivially easy to build a model that shows 50% annual returns over the past decade. It’s extremely hard to build one that continues working going forward.

The problem is overfitting. Every backtest is an in-sample test—you’re using historical data to find patterns, then measuring performance on that same data. With enough parameters to tweak, you can always fit past data. The model learns the noise, not the signal.

Signs you’ve overfit:

• Performance looks too good to be true (it probably is)

• Results are sensitive to small parameter changes

• Factors don’t have intuitive explanations

• High-frequency trading with unrealistic cost modeling—massive returns evaporate once you account for real spreads and slippage

This is why out-of-sample testing matters so much. Out-of-sample means testing on data your model hasn’t seen during development—either through live testing (tracking performance going forward) or backtesting on periods you deliberately excluded from your optimization. Both approaches help reveal whether your model found real patterns or just memorized historical noise.

Live Strategies: Proving Your Model in Real Time

A live strategy tracks your model’s performance in real-time, going forward. No hindsight, no curve-fitting—just your model meeting the market as it unfolds. You still need to model transaction costs and slippage realistically—live testing doesn’t eliminate that requirement, it just removes the hindsight bias.

Portfolio123 lets you run paper portfolios that track what your model would recommend each period (week, month…). These results accumulate over time, building a genuine track record.

Live testing is uncomfortable. Your model will underperform for stretches. Individual picks will blow up. You’ll question every design decision. This discomfort is the point—it’s what separates real edges from backtested fantasies.

Advanced Tools: Rolling Tests and Optimization

Portfolio123 includes tools that help you stress-test your model beyond simple backtests.

Rolling Tests: Checking for Luck

A single backtest uses one starting date. But what if you were lucky with that specific start? Seasonality, quarterly patterns, and monthly fluctuations all affect results. Starting your simulation at a market peak could overstate your expected returns.

Rolling tests address this by running your simulation multiple times with different start dates. Instead of one 15-year backtest, you get dozens of overlapping backtests that reveal how consistent your model’s performance really is.

One way to reduce timing sensitivity is using weekly rebalancing—more frequent transactions smooth out the impact of any single entry point. But even then, rolling tests help you verify that performance variations across different start dates aren’t significant. Robust strategies show relatively consistent results regardless of when you begin measuring.

The Optimizer: Handle with Extreme Caution

Portfolio123 includes an optimization tool that automatically searches for the best parameter values—factor weights, buy & sell rules, universe filters—to maximize historical returns.

I use this tool sparingly, and you should too.

The optimizer allows you to find the parameter combination that would have worked best in the past. The problem is that you want to let it work in the future. You may overfit if you over do this.

If you use the optimizer at all:

• Optimize on one time period, test on a completely separate period

• Use it to understand sensitivity, not to find “perfect” parameters

• Prefer simple models with few parameters over complex models that are at risk of overfitting

• Be deeply suspicious of optimized results that look dramatically better than unoptimized ones

The goal is out-of-sample performance—returns on data your model has never seen. Optimization, by definition, improves in-sample results at the risk of degrading out-of-sample performance.

For The Alpha Engineer model, I deliberately limited tuning. Because I built the model using factors with established research backing their individual performance, I used the optimizer mainly to check robustness—not to find better parameters. The model didn’t require changes after the initial build using the ranking tool. I also tested the final model on an out-of-sample period of several years before starting the live test—giving me two layers of validation beyond the initial backtest. That combination—proven factors, out-of-sample backtesting, and live testing—is what gave me confidence to deploy real capital.

The Reality Check

If you’ve read this far, you understand why quantitative model development is genuinely difficult. Building a universe, designing factors, combining them into rankings, backtesting with realistic costs, running live tests, checking robustness—each step requires careful thought and significant time.

Most DIY attempts fail. Not because the people aren’t smart, but because:

• They underestimate the data requirements (garbage in, garbage out)

• They overfit to historical patterns that don’t repeat

• They lack the discipline to follow systematic rules when they’re uncomfortable

• They give up during inevitable drawdown periods

This is precisely why The Alpha Engineer exists. I’ve spent years building and testing models, making mistakes, and refining the approach. Subscribers get the output—weekly rankings, model portfolio, clear methodology—without needing to replicate the development process. Publishing for subscribers keeps me disciplined too—I follow the model myself, in good times and bad.

Getting Started with Portfolio123

For those determined to explore model-building, Portfolio123 is the best platform available to individual investors. Institutional tools cost tens of thousands annually. Portfolio123 provides comparable capabilities at a fraction of the cost.

What makes it valuable:

• 20+ years of point-in-time data: Prevents look-ahead bias that corrupts backtests

• Global coverage: U.S., Canada, Europe, with Asia coming soon

• Sophisticated ranking tools: Build complex multi-factor models

• Realistic simulation: Transaction costs, slippage, liquidity constraints

• Live tracking: Monitor real-time performance without risking capital

Get a 35-day free trial using my referral link – Full access to explore the platform and test strategies.

The trial gives you enough time to build a basic universe, create a ranking system, run backtests, and understand whether model development is something you want to pursue seriously.

Two Paths Forward

After exploring Portfolio123, you’ll likely reach one of two conclusions:

Path 1: “This is fascinating—I want to go deeper.” Great. You now have professional-grade tools to develop your own systematic approach. Expect the learning curve to take months, not weeks. Start simple. Test rigorously. Be humble about what backtests can tell you.

Path 2: “This confirms I’d rather leverage someone else’s work.” Also great. Understanding the process makes you a more informed user of quantitative strategies—including The Alpha Engineer rankings. You’ll better appreciate why systematic approaches work, how to interpret rankings, and why discipline matters.

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Either path makes you a better investor. That’s the real value of understanding how quant models are built.

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.

The outcome: All ten stocks gained between 48% and 345%. The three top-ranked stocks I analyzed in depth? They returned +345%, +121%, and +95%—averaging 187% compared to 103% for the other seven stocks.

This isn’t a backtest. These are real-time results: the ranking found the opportunities, and the deep-dive research confirmed ones that had multibagger potential.

The Results: A Clean Sweep

Every single stock on this list delivered positive returns. The average gain across all ten holdings was 128% over five months—substantially outperforming broader market indices during the same period.

To put these returns in perspective: the VanEck Junior Gold Miners ETF (GDXJ), a representative benchmark for this sector, gained 87% during the same five-month period. The average return of my top 10 ranked stocks exceeded this benchmark by 41 percentage points. The deep-dive selection of the three highest-ranked stocks exceeded it by 100 percentage points.

More significantly, the ranking itself demonstrated predictive power. The three highest-ranked stocks (percentile ranks 98, 97, and 96) averaged 187% returns—nearly double the 103% average of the remaining seven stocks. The top-ranked stock delivered a 345% return. The system didn’t just identify a winning sector—it identified the best stocks within that sector.

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What This Proves About Systematic Selection

This case study validates three critical principles:

1. Momentum-based ranking systems can identify sector rotations before they’re obvious.

In May 2025, my ranking system had identified multiple precious metal miners in the top percentiles. This wasn’t coincidental—the momentum factor in my model was detecting early signals of sector strength before it became consensus. Gold prices were rising, but not all miners were benefiting equally. The ranking concentrated on those showing the strongest combination of value, quality, and momentum characteristics. While the sector itself performed well (as evidenced by GDXJ’s 87% gain), my systematic approach identified which specific stocks within that sector offered superior opportunities.

2. Quantitative ranking systems can identify opportunities institutional investors overlook.

These weren’t mega-cap miners with billion-dollar market capitalizations. All three deep-dive selections were trading in the $150 - $500 million market cap range when identified. They operated in markets where limited analyst coverage and smaller float sizes created pricing inefficiencies. My systematic model, analyzing nearly 15,000 stocks globally, found them precisely because they met specific criteria: exceptional value metrics, strong quality indicators, and positive momentum signals.

3. High rankings correlate with superior performance when backed by fundamental analysis.

The ranking identified the universe. The deep-dive analysis confirmed why these stocks deserved their high rankings. While gaining momentum, they were still cheap compared to the run up in the industry. Andean Precious Metals traded at 2.3x forward EV/EBITDA while expanding production. Mineros traded at 4.8x P/E with a 6% dividend yield while generating substantial free cash flow. Serabi traded at 5.0x P/E while ramping production toward a doubling of output. The quantitative metrics reflected genuine business quality—not statistical noise.

4. Top-ranked stocks as a group significantly outperform the broader selection.

While individual stock performance varied, the statistical reality is undeniable: the three highest-ranked stocks returned 187% on average versus 103% for the other seven. This 84-percentage-point spread demonstrates that the ranking system doesn’t just identify winners—it identifies which winners offer the best risk-reward profiles. Even in a strong sector, position sizing toward the highest-ranked opportunities would have dramatically improved returns.

The Core Strategy: Trust the Ranking

Here’s what matters most: you didn’t need the deep-dive analyses to capture these returns. The ranking itself identified all ten stocks. Simply following the systematic methodology would have delivered a 128% average return over five months.

The Alpha Engineer methodology is built on a systematic, quantitative approach that hase the ranking at its core: My Method Explained.

The core process is straightforward:

• Buy stocks from the top of the ranking

• Hold them while they remain highly ranked

• Sell when they drop below the ranking threshold (typically around rank 90)

• Reinvest proceeds in the current top-ranked opportunities

This systematic approach has historically delivered 37% CAGR since 2010 and 42% return since late February 2025 (with each trade of the model portfolio documented and announced prior to the transaction for paid subscribers). This five-month case study demonstrates it working in real-time. The ranking identified the sector momentum. The ranking identified the specific stocks. The ranking separated the best performers from good performers.

No discretionary decisions. No emotional reactions. No need to time entries and exits based on market sentiment. The system continuously analyzes nearly 15,000 stocks and tells you which currently offer the best combinations of value, quality, and momentum.

The Dynamic Nature of the Ranking

The May snapshot represented one moment in time. The ranking system operates continuously, updating as new data emerges and market conditions shift.

This dynamic process continued identifying opportunities beyond the original ten stocks. For example, in July Golconda Gold (GG:CAN) moved to the top of the ranking as its metrics improved. My model portfolio added it, as did my personal portfolio. I mentioned it in a note to my followers and subscribers. The result? An additional 185% return for those following the systematic process.

This illustrates a critical advantage of systematic approaches over static stock picking: the ranking adapts as conditions change. New opportunities emerge. Some stocks maintain their rankings. Others drop out as fundamentals deteriorate or valuations expand. The quantitative model continuously identifies which stocks currently offer the best risk-reward characteristics across the entire universe of nearly 15,000 analyzed stocks.

The Deep-Dive Add-On

The deep-dive analyses represent an additional layer—the icing on the cake, not the cake itself.

I maintain a separate portfolio for deep-dive selections. These aren’t regular publications. I only publish deep-dive analyses when my research identifies stocks with convincing multibagger potential (100%+ returns) within a two-year timeframe. Why that threshold? With a systematic strategy delivering 36% annual returns, a deep-dive selection needs exceptional upside to justify concentrated position sizing outside the core methodology.

For these three precious metal miners, the deep-dive research identified specific characteristics that suggested multibagger potential:

Andean Precious Metals (APM:CAN) returned +345% since May 15th and +391% since the deep-dive on March 29th. The analysis highlighted the opportunities for its Bolivian silver mine and US gold mine. Read the full Andean deep-dive.

Mineros SA (MSA:CAN) returned +121%. The scenario analysis calculated probability-weighted returns of 83%, with specific catalysts including operating leverage to gold prices and M&A expansion opportunities. Read the full Mineros deep-dive.

Serabi Gold (SRB:GBR) returned +95%. The analysis calculated probability-weighted returns of 152% driven by the Coringa project ramp-up and production doubling trajectory. Read the full Serabi deep-dive.

These deep-dives provided conviction for concentrated positions. But the ranking system had already identified these stocks as exceptional opportunities. The deep-dives simply explained why in comprehensive detail.

What the Data Shows

This five-month case study demonstrates four distinct approaches to capturing sector opportunities, each with different risk-reward profiles:

Approach 1: Sector ETF Exposure

The simplest approach: buy a sector ETF when you believe it will outperform. GDXJ delivered +87% during this period—a strong result. The challenge: you need the sector insight upfront, and you get exposure to everything—the winners and the losers, the efficient operators and the struggling ones. Your returns will approximate the sector average.

Approach 2: Top-Ranked Sector Concentration

A more refined approach: when a sector becomes statistically overrepresented at the top of the ranking (indicating both attractive valuations and building momentum), concentrate in those top-ranked names. The May snapshot showed 10 precious metal miners ranked 90 or higher. Following this approach delivered +128% average returns—41 points above the sector benchmark. The ranking filtered out the underperformers and concentrated on stocks with the best quantitative characteristics.

Approach 3: Systematic Portfolio Method

The core Alpha Engineer methodology: maintain a diversified 50-stock portfolio by continuously buying stocks ranked 97-99 percentile across all sectors and selling when they drop below a threshold. No sector bets required—the ranking naturally allocates to sectors showing the best risk-reward characteristics. My model portfolio currently holds 6 precious metal miners (12% of portfolio) acquired over the past months. This is notable: precious metal miners typically represent only about 1% of the 15,000 stocks analyzed by the system. The ranking concentrated them to 12% of the portfolio—a 12x overweight that occurred organically as these stocks rose to the top of the rankings. Their average return: 144% over an average 4 months holding period (sofar), or approximately +180% extrapolated for 5 months. This approach captured the sector opportunity without requiring any upfront sector view—the systematic process identified it automatically and allocated accordingly.

Approach 4: Deep-Dive Enhanced Systematic Approach

The most sophisticated approach: follow the systematic method while conducting deep-dive analysis on the highest-ranked stocks to identify those with exceptional multibagger potential. Overweight these positions in a separate conviction portfolio. The three deep-dive selections averaged +187%—marginally outperforming the systematic approach alone.

Beating the System

Here’s what this comparison reveals: beating a systematic quant method is difficult. Even with extensive fundamental research, detailed scenario modeling, and probability-weighted return calculations, the deep-dive selections (187%) only marginally outperformed the model portfolio’s systematic approach (180% extrapolated). The quantitative ranking, applied consistently without discretionary judgment, captured nearly all the available alpha.

Does this mean deep-dive research adds no value? Not at all. The ability to identify and overweight the highest-conviction opportunities—like the 345% winner—can significantly enhance overall portfolio returns when done correctly. But the data demonstrates that the systematic method alone delivers exceptional results. The deep-dive layer is valuable enhancement, not a necessary requirement.

The ranking operates continuously, identifying the best opportunities across nearly 15,000 stocks regardless of sector. When precious metals showed favorable momentum and value characteristics, the system naturally allocated to them. When other sectors show better characteristics, the system reallocates. No predictions required. No sector timing needed. Just systematic execution of a quantitative process.

The ranking system continues operating, analyzing nearly 15,000 stocks across global markets. Some current top-ranked stocks are precious metal miners. Many are in completely different sectors. The model focuses on quantitative characteristics that historically correlate with superior returns, regardless of sector narratives.

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Paid subscribers receive:

• Weekly Stock Rankings --- Access to my proprietary ranking of approximately 3,700 stocks across the US, Europe, UK, and Canada, updated weekly

• Buy/Sell Strategy --- Access to my method, porfolio manager tool and additional research

• Model Portfolio --- Weekly transactions of the Alpha Engineer 50-stock model portfolio with performance tracking and insights in my own portfolio

• Deep-Dive Analysis --- Regular, detailed research on potential multibagger stocks that have passed my quantitative and qualitative filters

• Direct Access --- Get insights from someone who’s spent two decades testing and refining investment strategies with skin in the game

Free subscribers now have access to the three deep-dive analyses:

Learn more about my investing framework:

• Read about my overall portfolio strategy.

• See the full details of my systematic method for stock picking.

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.

New Features in v1.1

What’s New:

• Available Top Stocks Display - Automatically shows all stocks ranked 95-99 that are liquid and not in your portfolio

• Manual Stock Selection - Click to select individual stocks for targeted buying decisions

• Intelligent Random Selection - Automatically selects replacement stocks equal to your SELL count, prioritizing by rank (99→98→97→96→95)

• Smart Exclusion System - Remove stocks you can’t or won’t buy from consideration with a single click

• Selection Persistence - Your selections remain visible while updating your portfolio during trading

• Visual Rank Coding - Color-coded backgrounds for each rank tier for instant visual recognition

• Dynamic Portfolio Integration - Available stocks update in real-time as you modify your holdings

• Enhanced Export Functionality - Exports include reference data such as selected stocks to buy (marked “REFERENCE ONLY”) for audit trail without affecting imports

• Session-Based Memory - Removed stocks stay excluded during your session but reset with new ranking data

Read more

Key Features

• Automatic Signal Generation: Upload the weekly ranking file and instantly see which positions require action

• Portfolio Persistence: Export the portfolio each week, then import it the following week to maintain continuity

• Position Sizing Calculator: Automatically calculates minimum and maximum position sizes based on the portfolio value

• 100-Stock Capacity: Track up to 100 positions simultaneously

• Gather Stock Data: Displays name, sector, country, market cap, current rank, previous rank, and current liquidity status

• Browser-Based: Works on any device with a web browser—no installation required

How to Use the Tool: Step-by-Step Guide

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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.

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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.

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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.

Learn more about my investing framework:

• Read about my overall portfolio strategy.

• See the full details of my systematic method for stock picking.

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.

For details on how these rankings are utilized in both my personal portfolio and The Alpha Engineer Model Portfolio, check out the methodology. My method section provides additional tools and context, while my research demonstrates how the method can be customized to individual preferences.

Below you'll find an example of a typical ranking blog post. Note that this illustrative example utilizes Joel Greenblatt's magic formula methodology rather than The Alpha Engineer model—my purpose here is to demonstrate the format and not the actionable data.

New top stocks:

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Understanding the Ranking Terms

• Ticker – Every stock is identified by its primary ticker, based on the country where most of its trading occurs. Non-US stocks include a colon followed by a three-letter country ISO code.

• Name – The official company name.

• Sector – The primary industry sector of the company. While it's useful to ensure portfolio diversification, I don’t actively manage sector weights.

• Country ISO Code – A three-letter ISO standard country code (more details here). My model covers stocks globally to maximize opportunities and minimize market risk, but individual investors can adjust the universe based on personal preference or brokerage limitations.

• Market Cap (USD) – Market capitalization in USD. My strategy primarily targets microcaps (below $300M) and small caps (below $2B). Typically, the model portfolio consists of approximately 80% microcaps and 20% small caps. Note: Microcaps, particularly those between $20M and $50M (nanocaps), are significant contributors to excess returns.

• Current Rank – The stock’s position in the latest weekly ranking.

• Previous Rank – The stock’s ranking position in the prior update.

• Liquid – Indicates whether the stock has an average liquidity greater than $50K USD and a stock price above $0.50 USD over the past two weeks.

New Ranking

Important: The Alpha Engineer Stock Ranking contains proprietary data and research licensed to you as a paid subscriber. Please do not share, forward, or distribute. Your support makes this quantitative research possible.

The Alpha Engineer Ranking Example

231KB ∙ XLSX file

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Learn more about my investing framework:

• Read about my overall portfolio strategy.

• See the full details of my systematic method for stock picking.

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.

The Statistical Foundation

The Alpha Engineer ranking establishes a statistical baseline for the deep-dive reports. Analysis of 400 top-50 ranked stocks held for two years during the last 16 years reveals important performance characteristics:

• Average 2-year return: 32% (15% annualized)

• Positive return frequency: 62% of stocks

• Multibagger frequency: 15% delivered returns exceeding 100%

• Exceptional performers: 2% generated returns exceeding 300%

• Downside risk profile: 9% experienced declines exceeding 50%

These metrics aren't merely descriptive—they establish clear probability thresholds that potential multibagger candidates must significantly exceed to warrant special attention.

The Multibagger Identification Framework

To qualify for a deep-dive report, a stock must project significantly better outcomes over two years than the baseline:

• Projected average return 80%* or better (versus 30% baseline)

• Probability of positive returns 70% or better (versus 62% baseline)

• Probability of 100%+ returns 30% or better (versus 15% baseline)

*This approximately equals the historical result of The Alpha Engineer model portfolio (36.4%). It makes no sense to focus on stocks that are projected to under-perform the model.

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Analytical Methodology

Quantitative Assessment

Despite a stock's favorable ranking, I conduct targeted financial analysis to identify specific dynamics not fully captured by the quantitative model. I leverage multiple specialized financial research platforms and check third-party analyses when available.

Qualitative Evaluation

The qualitative assessment examines internal and external factors through a SWOT framework. For this work, I check reports, online sources and use AI deep research.

• Strengths: Competitive advantages, management quality, operational excellence

• Weaknesses: Financial vulnerabilities, execution challenges, structural limitations

• Opportunities: Industry tailwinds, expansion vectors, strategic catalysts

• Threats: Competitive pressures, regulatory dynamics, technological disruption

Scenario Development

This analytical foundation enables the construction of three distinct scenarios:

• Bear Case: Dominated by identified weaknesses and threats

• Base Case: Aligned with consensus expectations and steady-state industry growth

• Bull Case: Leveraging core strengths to capitalize on identified opportunities

For each deep-dive candidate, I create return projections based on different scenarios. I give each scenario (bear, base, and bull case) a probability of occurring. This allows me to compute the numbers needed to identify a potential multibagger.

Integration with Portfolio Strategy

For me, these analyses complement rather than replace The Alpha Engineer methodology. I incorporate multibagger candidates within my own portfolio based on The Alpha Engineer process and may establish a higher allocation for these high-conviction opportunities. I still use a minimum ranking as my trigger for selling these stock—not an arbitrary holding period—maintaining disciplined exit criteria even for my highest conviction positions.

It’s important to notice that due diligence is critical when targeted stock picking is part of the process. Selecting stocks only based on personal preferences may introduce behavioral biases that diminish returns of the quant model instead of increasing them.

Publication Cadence

True multibagger opportunities remain relatively rare—even within a refined universe of top-ranked stocks. With my framework I try to capture approximately one compelling candidate per month on average. However, I may also publish analyses of "near-miss" candidates that exhibit promising characteristics but fall short of full qualification, or those that present compelling opportunities under specific scenarios. This allows subscribers to use my report and conduct further due diligence.

Remember: While these deep-dive reports provide substantial analytical groundwork, they should serve as a starting point for your own research journey, not a destination.

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Learn more about my investing framework:

• Read about my overall portfolio strategy.

• See the full details of my systematic method for stock picking.

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.

For the private investor managing their own portfolio, I believe this kind of diversification is crucial based on my personal experience and approach. A well-structured portfolio strategy allows an investor to stay in the game during downturns and take advantage of opportunities when markets recover.

My Portfolio Structure

1. Rainy Day Fund: Six months of family expenses in cash, held in savings accounts. This ensures I don't have to sell investments in a downturn, avoiding forced liquidations at unfavorable prices. Cash acts as an immediate buffer against personal financial shocks such as job loss, medical emergencies, or unforeseen expenses.

2. Defensive Assets: Cash equivalents (short-term bonds), gold, and a limited use of options as a temporary hedge. These assets help protect against inflation and systemic market stress. Gold, for instance, has historically been a safe haven during economic turmoil, serving as a hedge against currency devaluation and geopolitical instability.

3. Offensive Assets: Stocks and funds focused on maximizing long-term returns. While equities carry higher volatility, they have consistently outperformed other asset classes over long periods. My focus is on quantitative stock selection that balances high compound annual growth rates (CAGR) with risk management principles.

Why Not the Traditional 60/40 Portfolio?

The classic 60% equities / 40% bonds allocation is outdated. After four decades of declining interest rates, bonds no longer provide the counterbalance they once did. We are now in a macro environment where long-term bonds may not hedge against equity downturns effectively. With rising interest rates and persistent inflation, traditional bond-heavy portfolios may struggle to deliver adequate returns.

In my view, relying too heavily on bonds for diversification carries risks that are often underestimated, which is why I personally prefer incorporating a mix of cash, alternative assets, and selective equities. While bonds once acted as a stabilizer, they now carry risks that are often underestimated. Instead, incorporating a mix of cash, alternative assets, and selective equities – from my micro-cap and small-cap quant model - provides a more flexible and adaptive approach.

The Barbell Strategy: Balancing Risk and Stability

I follow Lyn Alden's barbell approach, where defensive assets act as a counterweight to an aggressive equity strategy. Lyn Alden, an investment analyst and macro expert, is known for her data-driven approach to macroeconomics and investment strategy. Her expertise lies in identifying long-term financial trends and creating robust investment models that withstand various economic cycles. As an electrical engineer she adds an analytical engineering mindset to her research, which I obviously like a lot. More about her work can be found at Lyn Alden's website.

The barbell strategy involves placing capital at both ends of the risk spectrum—high-quality defensive assets on one side and high-growth, high-return equities on the other. This approach avoids overexposure to the middle ground, such as long-duration bonds, which may not provide sufficient returns or protection in today's economic climate.

Today, my defensive allocation includes gold as a store of value, particularly in response to global inflation and aggressive monetary policies. Central banks worldwide have engaged in unprecedented levels of quantitative easing, increasing the supply of fiat currency. Gold, in contrast, maintains intrinsic value and has served as a reliable hedge against fiat devaluation over centuries.

My Offensive Strategy: Maximizing CAGR with Controlled Downside

The ‘Offensive’ portion of my portfolio is where I generate alpha. It is run using my core methodology, The Alpha Engineer Quant Strategy, which focuses on systematically finding high-upside micro and small-cap stocks.

My investment process is straightforward:

• Alpha Focus: Seeking above-market returns without leverage. While leverage can amplify returns, it also significantly increases risk, often leading to severe drawdowns during market downturns.

• Value-Oriented: Margin of safety as a core principle. By focusing on undervalued assets, I aim to minimize downside risk while maximizing potential upside.

• Data-Driven: Financial models guide stock selection, using quant stock screeners, rankings, and clear buy/sell rules. This quantitative approach reduces emotional decision-making and ensures consistency in portfolio management.

• Global Perspective: As an international investor, I believe a broad geographic approach increases my options to find alpha while reducing market risk. Many investors suffer from home bias, limiting their exposure to local opportunities. Expanding my scope allows for greater diversification and better risk-adjusted returns.

• No Subjectivity: My core investment strategy is purely quantitative, driven entirely by data and systematic rules.

Separately, I also conduct qualitative analysis for my “Deep-Dive” research. These reports feature a small selection of stocks that are first identified by the core quant model. I then analyze these specific companies fundamentally. While I invest in these personally, I treat them as a non-systematic, satellite part of my portfolio—they are the “cherry on the cake” and are kept entirely separate from the rules-based, core quantitative strategy.

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For the self-directed investor, maintaining a strict process is key to avoiding common pitfalls. Many retail investors fall into behavioral traps such as herd mentality, fear of missing out (FOMO), and panic selling. Having a well-defined system helps counteract these tendencies and ensures disciplined execution when building a high-alpha stock portfolio.

The defense-offense ratio depends on personal risk appetite and long-term plans. For me, its currently about 25% - 75%. I am 46 and I intend to compound my gains for at least 20 years (hopefully!). Even then I probably continue with the funds I don't need and I hope my children get the vibe too. Modeling alpha is too much fun!

Why Process Matters More Than Predictions

Investing without a clear portfolio strategy and process is a recipe for failure. The financial media often focuses on short-term market predictions, but trying to time the market is a losing game for most investors. Instead, a structured, systematic approach to investing ensures discipline and long-term success.

A solid investment process allows me to navigate market cycles with confidence. It reduces emotional biases, enables consistent decision-making, and ensures that my portfolio aligns with long-term objectives.

In my experience, smart investing isn't about reacting to market noise but rather about designing a resilient portfolio that thrives in all conditions. Engineering a strategy with clear rules, diversification, and risk management is the best way to stay ahead of the game.

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.