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Spying on Your Trading Future

In my past posts, we’ve explored a different way of looking at trading — one that’s less about “being right” on every trade and more about seeing the bigger picture.

We talked about expected value as the trader’s true “laser vision” — the ability to project the future of your account rather than the price of a single asset. We also saw how looking at results in blocks, rather than one trade at a time, helps remove the emotional weight that comes from obsessing over each outcome.

Now, we’re ready to take this one step further.

It’s one thing to calculate an average from past trades — it’s another to use that number to imagine and measure the possible futures of your system. This is where probability and a few simple tools can turn a set of trading rules into a system you can test, stress, and trust.

Over the next two posts, we’ll keep things intuitive, but we’ll open the toolbox a bit wider: First, we’ll talk about expectancy in action: how the average result grows more reliable over time, and how Monte Carlo simulations can show us the range of possible equity paths — including the unlucky ones.

Then, we’ll look at risk management as the engine that makes expectancy work in real life. We’ll explore how much to risk per trade, why betting more isn’t always better, and how to find the sweet spot between growth and survival.

Think of this as moving from reading a map to running real simulations of the journey ahead. You’ll see your trading system not as a mystery box but as a process you can measure, adjust, and manage — all without turning into a mathematician overnight.

I promise you that in the next two posts, I’m going to turn these technical ideas into practical, actionable lessons. You’ll see exactly how to apply them to your own trading — and once you do, you’ll never look at a trading system the same way again.

And of course, I’ll share the visual examples and a downloadable Python notebook so you can experiment with your own data.


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