Let me ask you something. You've been trading for a while now. You know your winrate. You know roughly how much you make on a good trade and how much you lose on a bad one. But have you ever thought about what happens if you keep doing exactly this for the next 50, 100, or 200 trades?
Not "I hope my portfolio goes up." Not "I think I'll be fine." Actual math. What does your trading behavior statistically lead to?
That's what the Monte Carlo simulator does. And it doesn't guess. It uses your real data.
What Is a Monte Carlo Simulation?
The name sounds fancy, but the concept is dead simple. You take someone's trading stats — their winrate, average win size, average loss size, how much they allocate per trade, how often they use stop-losses — and you ask: "If this person keeps trading like this, what are the possible outcomes?"
Then you run that scenario 100 times. Each run randomly generates a sequence of trades based on your actual statistical profile. Not your exact past trades in order — that would just replay history. Instead, it generates new trade sequences that are statistically identical to your behavior.
Think of it like this: if you flip a coin that lands heads 60% of the time, and you flip it 50 times, you'll get a different sequence every time. Sometimes 35 heads, sometimes 28. But on average, you'll get around 30. The Monte Carlo does the same thing with your trades.
How It Actually Works (Under the Hood)
Here's what happens when you load the equity simulation on your dashboard:
Step 1: We Study Your Trading Profile
The simulator pulls every non-skip trade you've made and builds a statistical model. Not just your winrate — way more than that:
Win distribution: Your average winning trade (as a portfolio-level percentage), plus how much that varies. Some traders have consistent small wins. Others have wild swings. The simulator captures both the average and the spread.
Loss distribution WITH stop-loss: When you set a stop-loss and it triggers, how much do you typically lose? This is usually smaller than losses without protection — which is exactly the point of a stop-loss.
Loss distribution WITHOUT stop-loss: When you don't use a stop-loss (or it doesn't trigger), how bad does it get? The simulator tracks this separately because the difference matters a lot.
Allocation impact: Here's something most people miss. If you make 10% on a trade but you only allocated 30% of your portfolio, your portfolio only moved 3%. The simulator accounts for your actual allocation habits, not just the trade-level returns.
Step 2: We Simulate 100 Futures
For each simulation run, the system generates a full sequence of trades:
- Roll for win or loss — based on your exact winrate
- If win — sample a return from your win distribution (with natural variance)
- If loss — first determine if you'd use a stop-loss (based on how often you actually do), then sample from the appropriate loss distribution
- Apply to portfolio — compound the return, respecting your allocation habits
Each of the 100 runs produces a different equity curve. Some get lucky streaks. Some hit rough patches. Most land somewhere in between.
Step 3: We Show You the Range
From those 100 curves, we extract:
- The average path — where you'd end up "on average"
- The 10th-90th percentile band — 80% of simulations fall in this range
- The best case — the luckiest run
- The worst case — the unluckiest run
Why This Matters More Than You Think
Here's the thing about trading: most people focus on individual trades. "Did I win this one? Did I lose that one?" But that's not what determines whether you'll be profitable in the long run. What matters is the distribution of outcomes over many trades.
You might have a 55% winrate. Sounds okay, right? But if your average loss is twice your average win, you're actually bleeding money over time. The Monte Carlo makes this painfully obvious — your equity curve will trend downward across most simulations.
On the flip side, you might only win 45% of your trades but your winners are 3x your losers. The simulator will show you that despite losing more often than winning, your portfolio actually grows over time. That's the power of a good risk-reward ratio.
The Stop-Loss Effect
One of the most interesting things the simulator reveals is the impact of stop-losses. Because we track your losses with and without stop-loss protection separately, you can literally see the difference in your worst-case scenarios.
A trader who consistently uses stop-losses will have a tighter loss distribution. Their worst simulation runs won't be as catastrophic because their downside is capped more often. A trader who yolos without protection? Their worst-case paths can get ugly.
If you're not using stop-losses and wondering why your worst simulations look scary — that's your answer.
The Scenario Tester
Beyond the standard Monte Carlo, the dashboard also lets you test specific scenarios:
"What if I lose 10 in a row?" — This isn't about being pessimistic. Losing streaks happen to everyone. The question is: with your current average loss size and allocation, can your portfolio survive it? If 10 losses wipes you out, your position sizing needs work.
"What if I win 10 in a row?" — Shows you the upside potential of a hot streak with your actual win sizes. Nice for motivation, but don't count on it.
"Win 5, lose 5, win 5" — A realistic volatile stretch. Does your portfolio recover from the losses, or do the losses eat more than the wins generate? This scenario exposes bad risk-reward ratios instantly.
What the Simulator Won't Tell You
Let's be honest about the limitations:
It assumes your future trading matches your past. If you're actively improving (or getting worse), the simulator doesn't know that. It works with what you've done, not what you might do.
It doesn't predict the market. The simulator doesn't care if Bitcoin is about to crash or if Tesla is going to moon. It only models your behavior — how you trade regardless of what the market does.
100 simulations is a lot, but not infinite. The best and worst paths are outliers. Your real future will almost certainly land somewhere in the middle band. Don't fixate on the extremes.
How to Actually Use This
Here's what I'd recommend:
Look at your average path first. Is it going up or down? If it's flat or declining, your trading system has a problem — and no amount of "next trade will be different" thinking will fix it.
Check the band width. A narrow band means your outcomes are predictable. A wide band means high variance — you might do great or you might do terrible. Narrow and upward is the dream.
Compare ranked vs singleplayer. If your ranked simulations look worse than singleplayer, you might be making different (worse) decisions under competitive pressure. Good to know.
Test the losing streak scenario. If 10 losses in a row would destroy your portfolio, you're allocating too much per trade. Period. Reduce your position sizes until a losing streak is survivable.
Reroll a few times. Each Monte Carlo run is different. If your average path is consistently positive across multiple rerolls, that's a strong signal. If it bounces between positive and negative, your edge is thin.
The Monte Carlo simulator isn't a crystal ball. It's a mirror. It shows you the mathematical consequences of how you trade — stripped of emotion, hope, and hindsight. And sometimes, that's exactly what you need to see.
