Alan is an orthopedic surgeon running a solo practice near Detroit. He sees roughly 200 patients a week, manages 13 employees, has two teenage children, and has studied martial arts for over 30 years. He’s also – in just under two months using our framework – built 75 highly robust trading strategies across seven markets.
That’s not a typo. Seventy-five strategies. Seven markets. Under two months.
This podcast interview was partly recorded to prove Al is a real person, because when we posted the case studies on the blog, some traders didn’t believe the results. His achievement is that unusual.
But the more interesting story isn’t the number. It’s the method – how someone with almost no free time managed to get there, and what he learned along the way that most full-time traders never figure out.
Al’s Background: 30 Years of False Starts
Al started trading when TradeStation was still called Omega Research in the 1980s. He learned EasyLanguage. He bought other people’s systems, tried to build his own, attended conferences, read books. None of it got him where he needed to be.
The core lesson he kept learning the hard way: beautiful back-tests don’t predict future success. If anything, he discovered that a strategy with a perfect-looking equity curve could often be inverted to produce an equally perfect-looking disaster – a sure sign of curve fitting. He submitted six strategies to independent testing, strategies that had passed his own walk-forward analysis, and they all failed in live trading.
At one point he lost 50% of his account in a single day. He was in the operating room when TradeStation went offline. The margin call came through on his cell phone between cases. It took 45 minutes to reach a broker to close the positions. That experience – along with the realisation that a $25,000 account isn’t going to compound to millions in five years at 30% annual returns – forced him to think differently about what he needed.
He needed a framework that was mathematically sound, built for reliability rather than back-test performance, and compatible with the tight time constraints of his schedule.
Why He Chose This Approach (Despite Being Sceptical)
Al found the BTA free introductory content – a BOSS tool system on the YM (E-mini Dow Jones). He expected it to fail. He coded it up, ran it through simulated testing, and it held up. That was enough to earn a closer look.
He enrolled in the Breakout Strategies Masterclass with high expectations but low conviction. He was, as he put it, preparing to pay another tuition fee to the markets. By the end, he was blown away.
The core realisation that changed everything: it’s not about finding one great system. It’s about having a proven framework for reliably building and validating many good systems. The BOSS tool automated the generation and initial filtering of strategy candidates. The robustness testing procedures – working through Robustness Levels 1, 2, and 3 – gave him a systematic way to separate real from curve-fitted. The automation tools slashed the hours required at each step.
The Daily Routine That Made It Possible
This is the part of the conversation that I think is most useful for traders who say they don’t have time. Al’s schedule:
- Morning (before patients arrive): Drop daughter at school, come in to the practice. Fire up two computers and run the BOSS template. Generate 4,000 strategy candidates before lunch.
- Lunch: Use the Masterclass filtering tools to cut those 4,000 candidates down to approximately 10 viable candidates. Launch the next batch of 4,000 for the afternoon session.
- Evening (after kids in bed): Scan the afternoon’s filtered candidates, identify one or two, begin rigorous robustness testing. This runs to around 11pm or midnight.
- Weekends: Review strategies that have passed at least RL2, apply further testing, generate modifications using false breakout techniques.
What made this work wasn’t exceptional discipline – it was extreme specificity. At each time slot, Al knew exactly which part of the process he was doing. Morning is generation. Lunch is filtering. Evening is robustness testing. No ambiguity about what to do with a free 30 minutes. This methodical approach – which shouldn’t surprise anyone given his engineering and surgical background – is what allowed him to move at a pace that would otherwise seem impossible.
The Robustness Level Framework
Understanding Al’s results requires understanding how robustness levels work in this framework:
| Level | What It Means |
|---|---|
| Robustness Level 1 (RL1) | A decent strategy – passes basic validation criteria |
| Robustness Level 2 (RL2) | A good strategy – passes more demanding stress testing |
| Robustness Level 3 (RL3) | An exceptional strategy – passes the most demanding testing, very few strategies reach this level |
At the time of recording, Al had 75 strategies at RL2 or above – with 52 of those having reached RL3. The remaining strategies were RL2, and he was working to convert them. The seven markets covered: Crude Oil, EMD, E-mini S&P, Coffee, NASDAQ, Soybeans, and Natural Gas.
The Lesson He Learned About Portfolio Construction
Al’s first instinct for building a portfolio was logical: take the best-performing strategy from each of his seven markets (ranked by net profit to drawdown ratio), combine them, and you’d have a great portfolio. It didn’t work out that way.
When he ran the portfolio through portfolio management analysis tools, the three top performers – Crude Oil, Natural Gas, and EMD – turned out to be highly correlated. When he replaced some of them with lower-ranked strategies from different correlation clusters, the combined portfolio performed significantly better than the “all-star” portfolio.
The lesson: your intuition about which strategies are best is often wrong at the portfolio level. Individual strategy quality and portfolio contribution are different things. A strategy with mediocre standalone metrics that is genuinely uncorrelated to your existing cluster can be more valuable than a star performer in a market you already hold. This is a theme that appears repeatedly in our hedge fund research – and Al discovered it empirically in under two months.
He landed on a target of five to seven strategies for his account size, with a maximum drawdown tolerance of 20% at the portfolio level. Crude Oil was his anchor. He was moving toward adding Soybeans and other grains, mixing day trading and swing trading strategies to keep correlation down.
What He Learned About Exits
One of Al’s biggest takeaways from the Masterclass was the disproportionate impact of exits on strategy performance. He built a random entry system – genuinely random entries, no directional logic whatsoever – with carefully designed exits. The system was profitable. That’s not a magic trick. It’s a demonstration that exit logic has more potency than entry logic in determining the shape and profitability of a strategy’s equity curve.
The implication: most traders spend 90% of their creative energy on entries and almost none on exits. Al calls exits “the stuff people don’t talk about” where the best insights actually live. If you want to understand this in practical terms, see Masterclass Part 3 for how exits and risk management interact in the final validation stage.
The “Cheating” That Wasn’t Really Cheating
Al’s explanation for how he built 75 strategies so quickly: he went through every student case study in the Masterclass community and built a spreadsheet. Every time template used. Every market. Every Data 1 and Data 2 combination. Rather than starting from a blank page and reinventing the wheel, he used the configurations that previous students had already validated as starting points for his own testing.
He called it cheating. I disagree. That’s exactly the right approach. Working from proven starting points, validating them yourself, and then building modifications and improvements on top is how you move fast without sacrificing rigour. The students who came before him laid groundwork. He used it. That’s how knowledge compounds in a community.
His recommendation to every new Masterclass student: go back through the podcast archive and the student case studies before you start building. Every one of those case studies is a validated roadmap. Don’t reinvent the wheel.
Key Takeaways
- A methodical daily routine – even just two to three hours broken into well-defined phases – is enough to build a large strategy library in a reasonable timeframe
- Automation tools (BOSS, the Masterclass framework) are the multiplier that makes this possible for time-constrained traders
- Beautiful back-tests are worthless; robust validation across multiple levels is what separates real strategies from curve-fitted ones
- Portfolio construction requires different thinking than individual strategy evaluation – correlation matters more than individual performance rankings
- Exits have more leverage over performance than entries; don’t neglect them
- Use what others have already validated as your starting point – building on prior work isn’t cheating, it’s efficient
In Part 2 of this conversation, Al goes deeper on his work with Smashing False Breakouts techniques, dynamic position sizing, and the specific improvements he made to convert RL2 strategies to RL3. Continue reading in Episode 19.
Want to build 70+ robust strategies in weeks – even with a full-time job and almost no spare time? Check out Breakout Trading PRO. It’s a structured, visual strategy builder inside BreakoutOS, powered by the proven Mr. Breakouts Formula used by thousands of traders.