25% Return in 65 Days (Watch What Happens Next)

So what does a 25% return in 65 days really mean when you're trading.

At scale, not just with pocket change?

So I'm going to take you inside the numbers, reveal the hidden risks and realities, and show you why big returns at size are never as simple as they look on a spreadsheet. So on a spreadsheet it looks impressive. But once real capital is involved, the numbers stop being abstract and they start carrying a lot of weight. And that's why today isn't about celebrating a percentage return. It's about going inside the numbers and understanding what they actually represent in capital terms and being honest about the risks that sit underneath results like this. So the testimonial itself is straightforward. Over a 65 day period, one of my clients using our system produced roughly a 25% return, translating to over $71,000 in net profit. So when you reverse engineer that figure, you're looking at an account size in the region of around $280,000. Now, this isn't a small account getting lucky, and this is meaningful size. And at this level, you don't get to ignore how the returns were generated or what would happen if conditions change. So it's also important to say this plainly. A 25% return in any time period using any system is a good run. There's no argument there. But a good run doesn't automatically mean a robust system. It simply tells you what happened during a specific stretch of market behavior. The mistake people make is assuming that favorable conditions will persist, or worse than that, performance alone is proof of safety. So the real question I'd be asking at this point isn't whether the system can make money, because it clearly can. The question is whether it can deploy capital safely if the market turns violent. If we see a true drawdown or a regime shift or a black swan event, can the system continue operating through a 50, 60, even 70% correction without forcing bad decisions? And that's the test that matters at scale. And that's where survivability lives.

So.

So think of performance like this as a building. From the outside, a structure can look impressive. Clean lines, height, symmetry. But none of that tells you whether it will still be standing after an earthquake. The only thing that matters then is what you don't see. And that's the foundation, the load bearing, columns. The way stress is distributed when pressure hits a 25% return is the facade. And the facade can look good. It tells you the building is, is working under calm conditions. And what determines whether it's investable at scale. Is whether the structure underneath can absorb that force without collapsing. And that's what stress testing a system really is. Not chasing higher floors, but making sure the foundation can carry the weight when the ground moves underneath it. So instead of asking how the system performed at the prices we already know, what I do is I test it at much higher levels, deliberately placing entries in less forgiving conditions and, and then watch how it behaves as price moves against it. So I'm going to dive in and show you that right now. All right, so what we're going to do now is we're going to build $100,000 bot and we're going to stress.

Test it and see exactly what that looks like. So as you can see, I've got the wallet size there of 100,000, I've got my position sizes there, and it's going to be engineered and executed through this architecture here. What I've got right now is a liquidation level around 42,000. So what I'm using at the moment is, is the Power Law and the Power Law support. We can see the price right now is 89,368 and the power law support is sitting at around 53,000. And that's going to be the first line of defense for this strategy. And as you can see here, if we come back, we're at 42,000. So there's plenty of room for us to act if we start to breach those levels. And beyond that, this is just going to perform amazing. But what happens if the price gets to 150, 160 and 200? Well, there's some things we have to consider. So if this was me and I was getting that 25% returns, I would start to ask the question, because that is an amazing return very, very quickly, is this going to work as the price goes up? So remember, we want to be below that 53,000 mark. We can see we're at the 42. So if we go simply and change the custom price to let's say 160,000, so it's almost double where we are right now.

We can see that the liquidation price.

Now has crept up. And this is the danger that a lot of systems face because they always optimizing for the present moment and they're not thinking about the future. So I can see now that if the price was to go up and I wasn't to have made any changes, this could potentially could be stress because now we're sitting above that red line of support. So to get beyond that, I would have to reduce the position size. And if I dropped it around 30 to 40%, see, if I put that in, bang, we drop directly to it. So now this is safe at a much higher level. So, as you can see, I've thought about that ahead of time, and I've made. I can make some changes to account for that. And lastly is that I also built into our algorithms a start and stop, which basically means that we can have a price level that we do not trade beyond. So in this case, I can just stop at 160,000, because you saw that the engineering needs to be done at those higher price points. If the price gets to 180, 200,000. This hasn't been engineered, so I want to stop it to give myself time to go in and make the changes if I have to, so I don't get caught out. So, you know, we built this, this algorithm so we have all that functionality within it. And this all comes as part of the Minotaur trading system.

If you want to see how this thinking scales even further, the next video is the right place to go. In it, I walk through a 1.2 million doll, not to impress you with size, but to show what changes when capital gets large, how entries are structured, how exposure is controlled, and why execution discipline matters more than returns ever could. At that level, there's no room for improvisation, no room for emotional decisions, and no tolerance for systems that only work when conditions are perfect.