Kosta Ouzas explains a common hidden problem in algorithmic trading: strategies that appear to work on small accounts (e.g., $5k–$100k) often fail when scaled to larger sizes (e.g., $200k–$1.2M). He argues small account "forgiveness" masks poor risk management, creating an illusion of consistency that collapses under real-size positions where positions cannot simply be closed.
Now, here's a problem that most people don't see with algorithms or bots. And everyone thinks that their bots work until they try to scale it. And that's why I want to show you a position. $1.2 million, because that's one of real size. You see, it's easy to run a bot on a $5,000 account like most people do on YouTube, and kind of feel like you've cracked the code because you can close a position manually. If it gets too uncomfortable, you can simply add more funds if it moves against you. Drawdowns feel small, risk feels manageable. And because of that size, risk management is almost optional. And that's why people expect 10 or even 30% a month, which is absolutely insane. It's because they're not managing risk, they're just managing smallness. And smallness can forgive every single mistake. That illusion shatters the moment you try to scale. A bot that feels safe at 5k becomes unstable at 50k. One that feels consistent at 20k falls apart at 200k. And the strategy that look bulletproof at 100k can get destroyed at a million dollars. Because its size, you can't just close the position.
