Max Schulz entered the World Cup Trading Championships for the first time and came third, with a 112% return for the year. The story of how he came up with the idea to enter is, in his own words, “very funny.”
He was in Phuket, Thailand, staying for seven months with his wife and a group of trader friends. One evening on the beach, drinking coconuts, they started talking about Larry Williams and his legendary performance in the competition. Someone asked: “What do you think – can we do like Larry Williams?” The group said yes. Max transferred his money to the broker that night. The next day, his friends said it was just a joke and they wanted to enjoy the beach.
Max entered anyway. Alone.
It’s easy to laugh at the story. But what it actually reveals – and what this interview goes deeper on – is what was already in place before that evening on the beach. Max didn’t get to 112% on a party idea. He got there because of years of serious research, a willingness to test and robustness-check his work, and the intellectual honesty to recognize when a strategy wasn’t working and go find better tools.
The Strategy Behind the Competition Result
Max’s background was in futures trading based on the Commitments of Traders (COT) Report – a methodology he’d learned from Larry Williams years earlier, where he specialized in grain markets. He was genuinely successful with that approach. But COT data doesn’t work well on stock index futures like the S&P 500 or NASDAQ-100, and his friends were getting good results trading indexes.
He had some S&P 500 strategies, but they had two problems:
- To keep drawdown small, he could only trade two or three very robust price patterns – which meant only one or two trades per month. Not enough frequency to generate meaningful returns.
- If he added more price patterns to increase trade frequency, drawdown increased significantly – but the extra return wasn’t proportional. More trades, more drawdown, but only 50% more return for 100% more risk. Not acceptable.
That’s the fundamental tension in index strategy development: frequency versus drawdown. More signals means more noise. The challenge was finding a filter that could separate the high-probability signals from the false ones.
How Market Internals Changed the Numbers
Max found the solution through the Trading Market Internals course. He used the TICK indicator on the S&P 500 as a divergence filter. The S&P 500, he noted, is a pullback market – in a strong uptrend it pulls down periodically before continuing higher. The challenge was identifying when the pullback was ending and the uptrend was resuming.
Market Internals gave him that signal.
The before-and-after numbers are worth stating precisely, because they illustrate what a well-chosen filter can actually do:
| Metric | Before Market Internals | After Market Internals |
|---|---|---|
| 10-year return (end-of-day, S&P 500) | ~$80,000 | ~$120,000 |
| Maximum drawdown | ~$12,000 | ~$5,500 |
| Return-to-drawdown ratio | ~6-7x | ~24x |
The drawdown dropped by more than half. The return increased by 50%. The return-to-drawdown ratio went from 6-7 to 24. That’s not a marginal improvement – it’s a fundamentally different strategy profile.
What made this result credible wasn’t just the backtest. Max stress-tested the strategy by keeping the parameters identical and changing only the market – running the same logic on NASDAQ-100, e-mini Dow, and e-mini S&P midcap. The equity curves on all of them looked good. That consistency across related but distinct markets told him the edge came from Market Internals itself, not from curve-fitting to the S&P 500’s specific historical data.
The Role of Confidence in Strategy Execution
Max started the competition year not fully trusting his own results. The performance in the first half looked too good. He thought he’d made a mistake somewhere. As a result, he didn’t take all the trades the strategy signaled.
His estimate: if he had taken every signal, the year-end return would have been approximately 150% instead of 112%. That 38-percentage-point gap is entirely attributable to doubt – doubt that evaporated as the out-of-sample results continued to match the backtest performance.
This is a pattern that comes up repeatedly with traders who build their own strategies from scratch. Confidence in execution doesn’t come from the backtest alone. It comes from watching the strategy perform in forward testing, building what Max calls a “track record of proof” between the strategy’s predictions and its actual results. You can’t shortcut that process – but you can build strategies that make it easier.
When strategies have a solid robustness framework behind them, confidence comes faster because the out-of-sample behavior matches what you’d expect. When they don’t – when they’re overfit or poorly tested – you’re always second-guessing, and you’ll second-guess yourself out of trades at exactly the wrong time.
Why You Can’t Copy Someone Else’s Strategy
After the competition, Max started getting messages from traders wanting to copy his signals or buy his exact strategy. He said no – not out of secrecy, but because he understood what they were actually asking for.
You cannot trade a strategy you don’t understand. The first time it goes into drawdown – and every strategy goes into drawdown – you need the internal scaffolding to hold your position: the knowledge of why the strategy works, what its historical worst-case scenarios look like, and why a current losing streak is within expected parameters rather than a sign of permanent failure.
If you just copied the entry and exit rules from someone else, you don’t have that scaffolding. The first significant drawdown will feel like evidence the strategy is broken, even if it’s performing exactly as it should. And you’ll exit. That’s not a character flaw – it’s a predictable consequence of trading without understanding.
Max made a clear distinction: hard work in strategy research, testing, and robustness validation is not optional overhead that experienced traders get to skip. It’s the thing that creates the confidence to hold through drawdowns and keep executing when the account is temporarily down. There is no shortcut around it.
The trading education market is full of people selling Ferraris and claiming they became millionaires in a year. What they never show you is the research work behind the visible results – or they don’t have any, which is exactly the problem. For algo traders, the methodology visible at this guide to algo trading for non-programmers gives a realistic picture of what the actual work looks like.
Building a Breakout Portfolio: Max’s Next Step
After the competition, Max was working through the Breakout Strategies Masterclass and applying it to his index trading. His stated goal: build a portfolio of multiple robust breakout strategies on the S&P 500 to reduce portfolio-level drawdown through diversification within the same market.
He also made an observation about education as investment rather than expense – a framing that stuck. He’d paid for the course and made $11,000 in the competition with that knowledge in his first year applying it. But more importantly, the improvement in strategy quality compounds over time. Improving your process this year makes every year after it better. The interest accrues indefinitely.
The contrast he drew was with the early part of his trading career, when he’d bought courses that made him worse rather than better – taking money without providing the genuine technical depth needed to build real edge. Finding the right framework was what he described as his second turning point in trading, after discovering the COT methodology years earlier.
Key Takeaways from Max’s Story
- A good filter can transform an ordinary strategy. Market Internals cut Max’s drawdown by more than half while increasing returns by 50% – turning a 6-7x return-to-drawdown ratio into a 24x ratio.
- Confidence must be earned through forward testing. Doubt costs real returns. Max left approximately 38% on the table by not taking all signals in the first half of the year.
- Robustness testing across related markets validates edge. Running the same parameters on NASDAQ-100, Dow, and mid-cap indices confirmed the signal came from the Market Internals filter, not curve-fitting.
- You can’t execute what you don’t understand. Copying strategies from others removes the knowledge backbone needed to hold through drawdowns.
- Education is compounding investment. Improvements made now improve every year that follows. The earlier you build solid process, the more years it compounds over.
To start building breakout strategies with genuine robustness testing – the kind that creates real confidence in live trading – see how I build breakout strategies that actually work.
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