This Strategy Beat Bitcoin DCA by 20x transcript

Since 2014, dollar cost averaging into Bitcoin produced a 4,000% return, but the price of earning it, however, was sitting through multiple 80% drawdowns. So, in this video, I'll show you one of our top strategies that delivered an 80,000% return, but with the maximum drawdown of just 42% along the way. So, let's get straight into it. Now, before we get into the data, let me address the most obvious question. This isn't just a backtest that's been optimized perfection.

It's a rule-based strategy that my subscribers and I have been using in real time, tested inside the strategy lab, where the goal isn't to maximize returns, but to try and break the strategy itself and just see if it survives. So, by the end of this video, you'll be able to recreate the numbers for yourself in the strategy lab and decide whether they stand up to the scrutiny. Now, what we have on screen is one of my favorite statistical indicators, which is the Z-score probability waves.

And this here is just an advanced mean reversion tool that adapts across cycles. And the standard deviation of that result helps answer a very simple question. How unusual is today relative to how this asset has actually been behaving? And a reading of plus two means that the price is two standard deviations above its normal baseline, and a reading of minus two means it's two standard deviations below. And the reason this matters so much for Bitcoin specifically is that the baseline moves.

Bitcoin in 2015 and Bitcoin in 2025 are not the same asset. The volatility is compressed and the market cap has grown by orders of magnitude. Now, looking at a signal and trading a signal are two very different things, and that gap is where most people actually lose their money. So, what we're going to do today is look at a way to actually trade this. Now, the strategy we're going to be using here is something called the trading cross, and it takes the probability waves on the shorter time frame and trades the cross up and the cross down.

And it's very simple in its methodology. It buys whenever the cross is above one into the positive and then sells when we cross back below zero. So, essentially it buys momentum when it's confirmed to the upside, but then immediately exits as soon as any negative downside has occurred. And you'll notice how deliberately simple but asymmetric that is. The entry demands proof. You're not buying into strength. You're buying into strength that's already pushed a full standard deviation above its own baseline, which filters out those enormous amounts of noise and chop that you see.

But the exit demands nothing else. The moment price slips back to merely average, you're flat and you're not waiting for a confirmation of weakness. You're not giving it any room. And that asymmetry is what produces the drawdown profile that we're about to look at. And it's literally the mathematical opposite of how most retail traders behave, which is to enter early on hope and then exit late on hope. Now, here we can see the equity curve for each strategy.

And the pink line is the trading cross and the white line is simply dollar cost averaging into Bitcoin. And then the yellow and blue lines are the equivalent strategies for gold and the S&P 500. All starting with that same $10,000. DCA into Bitcoin grew to about $446,000, gold to roughly $32,000, and the S&P 500 to about $40,000. The trading cross, however, finished at over $8 million. And this lets you compare the strategy not just against holding Bitcoin, but against the two of the benchmarks that most of the capital in the world is actually measured against.

Now, from a simple raw returns perspective, you can see that it delivered an 80,000% return, which is a 72% compound annual growth rate versus dollar cost averaging's 4,000% return or a 36% compound annual growth rate. So, the strategy roughly doubles the annualized rate of compounding. And when you double a compounding rate over a decade, the gap becomes absurd. And that's the entire lesson of compounding in one screenshot.

It's not that the strategy made twice the money, it made 20 times as much money, but the advantage was applied again and again to a growing base. And here we can see that you also do that with much less of a drawdown profile, which is the part that actually makes it investable in real life. Again, the pink line here is the trading cross, which has a maximum drawdown of 42.9% while DCA drew down by 83% during that time.

And I want to be honest about what an 80% drawdown actually feels like because it's very easy to look at that chart in hindsight. An 83% drawdown means that for every $100,000 that you had, you're looking at having $17,000 at the end of it. And then to simply return to back where you started, you'd need a 490% return. And that is the moment where pretty much everybody sells and it's why so few people actually capture those brilliant 4,000% returns that DCA has actually presented us with.

And nothing here explains this trade-off better than something called the Calmar ratio, which is essentially the compound annual growth rate divided by the maximum drawdown. So here, the higher numbers are better because it means that you're getting a better return per unit of pain that you've actually endured. And the trading cross strategy had a Calmar ratio of 1.68 while DCA was 0.44. Now, if we think about what those two numbers are saying, a Calmar of 0.44 means that for every 1% of peak-to-trough pain that you had to absorb, you were compensated with 0.44% of annual growth.

And a Calmar of 1.68 means that 1% of pain bought you 1.68% of annual growth. So that's nearly four times the compensation for the same unit of suffering. So even though Bitcoin is one of the best risk-adjusted trades of all time, a Calmar ratio of 0.44 is actually pretty poor because the drawdown is so extreme. And anything above one is considered absolutely excellent. So 1.68 is pretty outstanding here. But a single backtest here is just one sequence of events.

It's one path through history. So, we ran a quick Monte Carlo simulation of 10,000 simulations. And this takes the strategy's actual daily returns, and then randomly reshuffles their order thousands of times, and then creates thousands of alternative histories. And the question here is simple. Was the performance dependent on getting lucky with the order of the returns, or was the edge genuinely robust? And the answer here is pretty clear.

Only a tiny fraction of simulations underperformed Bitcoin DCA. And even then, it was often by just a few thousand dollars. In fact, the fifth percentile outcome still finished at $463,000. So, in other words, if you run the strategy 100 different times under equally realistic market conditions, only around five runs would have produced a lower return. And yet, the worst 5% outcome still actually beat dollar cost averaging.

The other 95 runs performed even better, and often by quite a significant margin. And the reason that's such a powerful test is that it destroys the luck of ordering. If your headline number only exists because three enormous winners happened to land on a particular sequence, then resequencing them will expose that immediately, and your median outcome will collapse far below your back-tested one. So, that here confirms that this final figure is actually very close to the median outcome, which tells you the edge is from the distribution of returns itself, and not just from a fortunate arrangement of them.

So, really here, this is just a quick taster. This is the whole point of the strategy lab for me. Not to give you a number to believe in, but to give you every tool that you need to actually disbelieve it, and then show you what's left standing afterwards. So, if you'd like to try it yourself, then go head over to onchainmind.io, where you'll find two other strategies that are based on completely different signals of mine.

And there's many more being built into the lab very soon. So, anyway, I hope you all found this useful, and as always, I'll catch you in the next one. >> [music] >> Onchain Mind Onchain Mind >> [music] >> Call it.