🍀
Aug 18 • General discussion
MONTE CARLO FOR PROP RISK LEVEL DETERMINATION!
Okay, so this started as a napkin thought. How do you rinse prop firms? And the honest answer is that it's a simple equation. Time to pass, probability of passing, cost of evals. That's it. Three variables. But when you actually sit down and formalize those three variables, a whole system falls out of it, and that system is worth walking through properly. So that's what this post is.
THE FORMULA
Per attempt, the expected value of an eval is simple arithmetic:
EV per attempt = p x payout, minus cost
p is your probability of passing. payout is what the funded account is actually worth to you, and note that this is not one payout, it's the expected cumulative payouts before you eventually breach the account. cost is the eval fee.
But per attempt is the wrong lens. The number that actually matters is:
EV per day = (p x payout, minus cost) divided by T
where T is the time it takes to resolve an attempt. Pass or fail. And this one division changes everything about how you should think about evals, because your eval fee is small. Your time is the expensive input. The bottleneck on rinsing is not capital, it's throughput.
WHY SPEED WINS
Here's where almost everyone gets it wrong. The retail instinct is to maximize p. Highest win rate, safest pass, tiptoe to the target. Feels responsible, right?
But a pass rate means nothing on its own. What matters is what that pass rate earns you per day it takes to get there. A 70 percent probability that takes 40 days loses to a 45 percent probability that takes 12. Run the division yourself, it's not close. The riskier setting wins even though it fails more often, because it resolves three times faster and lets you go again.
Think of it like a card counter at a casino table. The counter doesn't optimize for winning every single hand, that's impossible anyway. The counter optimizes hourly rate. Hands per hour times edge per hand. The eval fee is your table buy-in, and EV per day is your hourly rate. Speed is not recklessness here. Speed is literally what the formula tells you to chase.
Of course there's a limit. Push risk too far and p collapses faster than T shrinks, and your EV per day goes negative. Which means somewhere in between there is an optimal risk level. Hold that thought.
TWO RISK LEVELS, NOT ONE
The second insight from this chat: your risk should not be one number. It should be two, because you're playing two completely different games.
Phase 1 is the eval. Here the account is not an asset yet, it's a lottery ticket you paid a fixed fee for. Your downside is capped at the fee. So you risk up, you pass fast, you get funded. Cap the attempt in time, not in caution.
Phase 2 is the funded account. Now the account IS the asset. The game flips completely. You throttle down, you bank the first payout, you build a buffer above the drawdown line so normal variance can't kill you. The funded account should run like a 1995 Toyota. Boring, slow, runs forever. Because the expected value of a funded account is mostly in how LONG it survives, and survival scales with running lower vol relative to the drawdown budget.
Sprint the eval. Protect the account. Two regimes, one system. Miss this and you either never get funded, or you get funded and blow it in month two doing the same thing that got you through the eval.
THE FLYWHEEL
Now watch what the speed actually unlocks. Pass fast, get funded fast, get paid fast. And that first payout is not just income, it's the eval fee for account number two. Then three. You're compounding accounts, not just returns.
FTMO even lets you put accounts on hold, so you can stagger evals and manage the book instead of babysitting five accounts at once. And once the process works on one firm, nothing stops you repeating it across every legitimate firm out there. propfirmmatch.com lists them, do your own due diligence on which ones actually pay.
One 100K account becomes a 400K book across firms, funded by the very fees you used to think of as a cost. And 1 percent a month on a 400K book is 4K a month, generated by rules, not by hours in front of a screen. That's the whole pitch in one number. The scaling limit is not the math, it's how many legitimate firms exist and how much operational bandwidth you have.
WHERE MONTE CARLO COMES IN
So back to the held thought. Somewhere between safe and reckless sits the risk level that maximizes EV per day, and here's the important part: that level is different for every strategy. It depends on your win rate, your payoff distribution, your trade frequency, how your drawdowns cluster. It is a property of YOUR system, not a rule of thumb someone can hand you.
Which is exactly what Monte Carlo simulation is for. You take your strategy's actual trade statistics, you simulate thousands of eval attempts at each risk level, and you read off which level maximizes EV per day. Not pass rate. EV per day. Your number, revealed instead of guessed.
Two honest footnotes on that, because we don't do overfitting here. One, when you gridsearch across risk levels you're running a selection process, so treat the output as an estimate, not gospel. Check that neighboring risk levels produce similar results.
If 1.5 percent risk looks amazing and 1.25 and 1.75 both look terrible, you found noise, not an optimum. Same sensitivity logic we always apply. Two, the simulation inherits every assumption in your trade stats. History is a probabilistic guide, never law.
THE HONEST CAVEAT
And now the filter, which matters more than everything above it.
None of this works without real edge underneath. The Monte Carlo will always hand you an optimal risk level, it's a very obedient tool. But optimal risk on a strategy with no edge is just the fastest, most efficient way to donate eval fees. The formula does not create edge. It scales it. Negative expectancy times a bigger multiplier is just a bigger negative number.
So before you optimize the rinse, build the thing worth rinsing with. Simple, systematic, evidence backed. The casino factor only works in your favor if the metrics survive the stress tests first.
HOW THE POST ITSELF GOT BUILT
Small bonus for the people here who also build content, because the process behind the carousel is a lesson in itself.
The math came first, exactly as above. Then the copy went through a value equation pass: dream outcome made concrete with real numbers instead of vague freedom talk, certainty sold through math instead of hype, and time compression as the actual pitch, since EV per day IS a time argument. The caveat about edge stayed in on purpose. Honesty is the trust mechanism that makes the rest land, and conveniently it's also true.
Then design. Matrix theme, phosphor green, digital rain, terminal headers, every number in monospace. And exactly one red element on all five slides: the call to action. The red pill. When everything else is green, the eye goes straight to the one red thing.
Restraint is what makes the accent work, same principle as the trading. One deliberate signal beats ten decorations.
If you want to build the edge side of this, the bootcamp is where that happens, start to finish. And if you want the Monte Carlo treatment for your own strategy's eval risk, drop a comment, if enough people want it I'll turn my notebook into a proper lecture.
1
2 comments
MONTE CARLO FOR PROP RISK LEVEL DETERMINATION!
powered by
skool.com/quantamentals-for-traders-5814
Beginners education under construction by Rick (Free)
Suggested communities
Powered by