There’s something that 99% of developers don’t realize.

That’s also why many trading bots crash as soon as they go live.

All those statistical rules we learned assume by default that the market is “normal”.

But the real market isn’t like that at all—it’s far off.

Just this one wrong assumption means your “statistically significant” strategies keep failing in live trading.

Very few people have ever explained this clearly:

The standard statistical assumption is that returns follow a bell-shaped curve—most results cluster in the middle, and extreme cases almost never happen.

But the market doesn’t work that way.

The market has “fat tails”.

Those extreme swings—the days when everything collapses within one day—happen much more often than what a bell curve would predict.

When I was first building a cleaning bot, this nearly ruined me.

Later I patched it specifically for this problem. Now this bot’s profit and loss has reached $43,000.

Think about what that means for your backtest.

Every significance test you run is silently assuming a bell curve.

So when the test tells you “this result is unlikely to be due to luck,” it’s actually underestimating how likely these crazy events are to occur naturally.

Your strategy edge looks stronger than it really is, purely because the math was built for a world model that doesn’t exist.

That’s why the usual standard where t-values are greater than 2 is far too lax for trading.

You need to raise it to 3 or even higher, specifically to leave yourself a buffer to handle the fat tails that standard math ignores.

Making a big profit for one or two months explains nothing either.

In a fat-tail world, even a robot with no real edge could, at any time, be handed a few months that look amazingly good.

The market won’t follow the statistical textbook.

Tune your whole framework so it’s on the same wavelength as the world you actually trade.

Below, I’ve left a complete guide to help students build their first successful bot.

See you in the next section.

If you want to copy-trade on Polymarket
I recommend using Polycop:
#Polymarket