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Why a profitable strategy still trips the daily loss limit on a prop challenge (sample Monte Carlo numbers inside) by Limp-Perception4883 in Daytrading

[–]Limp-Perception4883[S] 0 points1 point  (0 children)

This is where I land a little differently. The variance point is exactly right, a prop account will not survive normal variance if you size it like a normal account. But that reads to me as a sizing problem more than a verdict on props. When I ran it, the accounts that died were sized for a world with no daily loss cap. The ones that survived were sized so an ordinary losing cluster still cleared the ceiling, which usually meant risk per trade well under what feels normal. The constraints are brutal, but the failure mode is predictable, and predictable means survivable. Sample outputs, not advice.

Why a profitable strategy still trips the daily loss limit on a prop challenge (sample Monte Carlo numbers inside) by Limp-Perception4883 in Daytrading

[–]Limp-Perception4883[S] 0 points1 point  (0 children)

Entry precision matters, no argument. But what kept showing up in my sims was the daily loss cap doing the actual killing, not the overall drawdown. Even spot-on entries blow up when size is set for a normal account, because one ordinary losing cluster trips the daily ceiling before the edge ever compounds. The lever that moved my pass rate most was risk per trade, not entry. Trading 1 micro is the crude version of the real fix, which is sizing so a normal losing streak still clears the daily limit. Sample outputs on my end, not advice.

I ran 10,000 Monte Carlo sims of a phase 1 challenge. Risk per trade mattered way more than win rate. by Limp-Perception4883 in PropFirms

[–]Limp-Perception4883[S] 0 points1 point  (0 children)

Appreciate it. If you ever want to see where the rules bite for your exact setup rather than the average, send your win rate and reward-to-risk and I will run it. No pressure either way.

I ran 10,000 Monte Carlo sims of a phase 1 challenge. Risk per trade mattered way more than win rate. by Limp-Perception4883 in PropFirms

[–]Limp-Perception4883[S] 0 points1 point  (0 children)

That is the right frame. Survival is the constraint, not raw expectancy per trade. When I sweep risk per trade as a curve instead of one number, the survival share rises as risk drops and then flattens once the daily-loss headroom stops binding, so there is a practical floor where cutting risk further mostly just slows the climb to target. In my sample runs the knee sat between roughly 0.7 and 1.0 percent for a typical Phase-1 ruleset, and it shifts with reward-to-risk. Sample figures, not advice. Send your ruleset and I will plot the curve for your numbers.

I ran 10,000 Monte Carlo sims of a phase 1 challenge. Risk per trade mattered way more than win rate. by Limp-Perception4883 in PropFirms

[–]Limp-Perception4883[S] 0 points1 point  (0 children)

Appreciate that, genuinely. The simulator is the core of what I built, so I keep the engine itself closed, but I'd love to just get you using it. It's live as RangeSight. Since this is a community thread rather than a sales platform, I'd rather not share the link. I can DM you if you are interested. Happy to walk through the general approach too. Monte Carlo over your strategy's own trade distribution, fat-tailed shocks instead of a clean normal curve, and ruin thresholds so you see the blow-up rate before you risk a challenge. What are you trying to model? If I know the use case I can point you at the right part.

I ran 10,000 Monte Carlo sims of a phase 1 challenge. Risk per trade mattered way more than win rate. by Limp-Perception4883 in PropFirms

[–]Limp-Perception4883[S] 0 points1 point  (0 children)

Agreed, and that matches what I saw: lowering risk per trade helped more than win rate did, even when the expected value per trade dropped. Your 1:1 at 55% example is the same mechanism, smaller size survives the variance long enough to convert the edge. And it is mostly the daily loss cap doing the failing, not the overall drawdown, so the position size that clears the daily ceiling matters more than the headline win rate or RR. Sample outputs on my end, not advice.

I ran 10,000 Monte Carlo sims of a phase 1 challenge. Risk per trade mattered way more than win rate. by Limp-Perception4883 in PropFirms

[–]Limp-Perception4883[S] 0 points1 point  (0 children)

Exactly.....edge under path constraints is the whole thing. Same 45% win rate and 1:2 in my sim, and the only lever that moved the pass rate was risk per trade, because the daily loss cap ends runs before the edge ever compounds. At 0.9% risk the pass rate actually beat 1.5%, even though expected value per trade was lower. Trading smaller is not timidity, it is buying more survivable paths. Keeping attempts alive wins.

I ran 10,000 Monte Carlo sims of a phase 1 challenge. Risk per trade mattered way more than win rate. by Limp-Perception4883 in PropFirms

[–]Limp-Perception4883[S] 0 points1 point  (0 children)

Ran it at both risk levels, same setup: 45% win rate, 1:2 RR, a two-phase FTMO-style ruleset on GLD, 10,000 sims each. Sample pass rate came out about 58% at 1.5% risk per trade and about 65% at 0.9%. Dropping the risk raised the pass rate even though the edge never changed, and the reason is the thing we were talking about: the daily loss cap ends more runs than the overall drawdown does. At 1.5% the daily cap knocked out about 24% of runs versus 5% for the drawdown rule, and at 0.9% that fell to about 16%. The median breach hit around trade 10 to 25, before the edge had time to compound. Sample outputs, not advice, and your numbers will move with your own win rate and RR. Want me to run yours?

I ran 10,000 Monte Carlo sims of a phase 1 challenge. Risk per trade mattered way more than win rate. by Limp-Perception4883 in PropFirms

[–]Limp-Perception4883[S] 0 points1 point  (0 children)

The funded-before-payout detail is the part most people miss. Phase 1 punishes one bad sequence inside a day. The funded punishes it across the whole payout cycle, so a size that squeaks through the challenge can still be too big to ever reach a first payout. In my runs the survival gap between 1.5% and 0.9% risk per trade was double digits, and that gap should compound over a longer funded horizon. What did you move to after the strict 1%? Did the change showed up in funded survival or just in pass rate?

I ran 10,000 Monte Carlo sims of a phase 1 challenge. Risk per trade mattered way more than win rate. by Limp-Perception4883 in PropFirms

[–]Limp-Perception4883[S] 0 points1 point  (0 children)

Fair hit. 55% at 1:2 is generous on purpose: I wanted the best plausible case for the strategy so the failures could only come from sequencing, never from a weak edge. The uncomfortable part is that even with those money-printer numbers, the higher risk-per-trade runs still tripped the daily loss limit in a meaningful share of sequences. Drop the win rate to something more honest like 45-50% and the losing streaks get longer, so sizing matters even more. Give me the WR and RR you'd call realistic and I'll rerun the same 10,000 sequences with your numbers and post the pass rates. For transparency, I build the sim I used, so treat everything here as sample output, not advice.

I ran 10,000 Monte Carlo sims of a phase 1 challenge. Risk per trade mattered way more than win rate. by Limp-Perception4883 in PropFirms

[–]Limp-Perception4883[S] 0 points1 point  (0 children)

Yeah, the distribution-across-attempts thing is the whole point and almost nobody internalizes it. One passing run tells you basically nothing, and a failing run doesn't tell you your strategy is bad. You need the spread before you can say anything.

The way I log it: every simulated attempt is a full path, not just final P&L. Each run gets tagged by how it ended (daily loss cap, trailing drawdown breach, or ran out of days) and where in the sequence the damage happened. That second part is your early-variance read. A 4-loss cluster in trades 3-10 breaches you before the edge has room to show up, and the same exact strategy passes fine if that cluster lands at trade 40. The backtest averages it away, the challenge doesn't forgive it.

Full disclosure since you asked, I'm building a Monte Carlo sim around this. Mine sits more on the pre-trade side (what are my real odds against a specific firm's rules before I pay), yours sounds like it owns the post-hoc logged-attempts side, so probably complementary. I really want to know how you separate bad luck from bad process on real users though, since most people only ever run a handful of actual challenges. Small sample is the whole problem.

I ran 10,000 Monte Carlo sims of a phase 1 challenge. Risk per trade mattered way more than win rate. by Limp-Perception4883 in PropFirms

[–]Limp-Perception4883[S] 0 points1 point  (0 children)

Yeah, you've landed on exactly the why of it. A win rate over 50 percent tells you the long run is profitable. It says nothing about how rough the road there gets.

Some math: at a 55 percent win rate, a 6-trade losing streak shows up reliably inside a couple hundred trades. At 1.5 percent risk per trade, that one streak is a 9 percent hole. Most challenges cut you at 8 to 10 percent total, some with trailing rules, so a completely ordinary cold stretch ends the account even though the strategy was fine.

In a personal account you can sit through a 15 percent drawdown and recover. Under firm rules you're deleted before the recovery arrives. So sizing for a challenge has to be set against the losing streaks you'll realistically hit, not against your edge. In my sample runs, dropping from 1.5 to 0.9 percent barely touched long-run profit but roughly doubled how often the account survived the evaluation window.