u/david19790

took me years of reviews to find this because nothing in the review flags it. a stop thats too tight produces a loss that looks exactly like a normal loss. entry, adverse move, stopped, next. you just have a slightly worse win rate than you should, forever, and you blame the market.

the tell isnt in the losers. its in the winners that almost werent.

kept a column i should have had from day one: how far each trade went against me before it resolved. then looked at winners only. on one instrument, half my winners were touching 80% or more of the stop distance and coming back. the stop was sitting inside the range the trade needed to breathe, and every loss i took there was partly a stop that was never wide enough.

widened it and cut size to keep dollar risk the same. win rate moved more than any entry filter id ever built. same entries, same everything, just stopped getting shaken out of trades that were about to work.

the reason it hides for so long is that a stop thats too tight and a stop thats correct produce identical losers. the information only exists in the mae of the trades that won.

the scan flags any instrument where winners regularly touch most of the stop. it only needs mae per trade and a win loss flag. on my profile.

anyone else found this in their own data or is it just me and one instrument

did this after my third account and it changed how i trade more than any indicator ever did.

the setup was dumb simple. every time i closed a trade at a loss i put a tag on whatever i opened next. didnt change anything about how i traded, just labelled it. did that for about a year.

end of it i had roughly 90 tagged trades out of a few hundred total. win rate on the tagged group was somewhere in the 30s. everything else was mid 50s. same setups, same instrument, same me. only difference was what had happened twenty minutes earlier.

what surprised me wasnt the gap, it was that i couldnt feel it happening. in the moment those tagged trades felt like normal trades. felt like i was being decisive, taking the setup that was there. going back through them later half of them werent setups at all, they were me wanting to be in something.

the size thing was worse honestly. i wasnt doubling up or anything dramatic but the tagged trades averaged maybe 30% bigger than my normal. thirty percent. on a funded account with a daily limit thats the difference between a bad day and a dead account and i had no idea i was doing it.

theres actually academic work on this if anyone wants to feel less alone about it. coval and shumway, journal of finance, 2005. they looked at chicago pit traders, guys trading their own money full time, and found the ones who lost in the morning took above average risk in the afternoon way more often than the ones who won. professionals. decades in. same pattern.

what i do now is boring. after a stop out i close the platform for thirty minutes. not because im so disciplined, because i know im not. the rule has to exist before the loss or it doesnt exist at all.

anyone else actually measured this on their own log or is everyone just vibes about it

i have every fill going back years. entry, exit, mae, timestamps, exit reason. thought that was the valuable data. it isnt on its own, because i cant tell you which version of the rules was live for any given stretch of it.

the rules being the regime definition, the exit policy, the thresholds, the cost assumptions. all the stuff that decides what an experiment even means. mine lived inside the runner script, so every time i touched the script i was quietly editing the definition of the thing i was measuring. results kept looking consistent, which is exactly the problem. a broken statistic gives you a number that eventually looks wrong. a definition that drifted gives you clean output forever.

found it when i went back to check whether a filter had helped and realised the before and after periods werent testing the same filter. two changes in between with no record.

what i do now:

spec lives in its own file, not the runner

runner imports it

hash the spec file, stamp the hash on every result row

changing the spec means a new file with a date on it, not an edit

the hash is the bit that earns its keep. it means i can ask which spec produced this without trusting memory, and it caught two cases where the spec changed and results didnt, which meant something was cached that shouldnt have been.

still cant version cost assumptions properly. half spec, half whatever the broker did that month.

the logger is about 30 lines of python and its on my profile. the hashing bit is the only part that matters, the rest is preference.

what do people here do about this. feels like an area where anyone from a real research shop is going to tell me im reinventing a solved problem badly

took me two years to see this properly. everyone knows spread and commission cost something. what i missed is the cost is fixed in pips and the risk unit isnt, so the same broker charges you a completely different share depending on where your stop sits.

1.2 pips round trip:

5 pip stop: 24% of R

10 pip: 12%

20 pip: 6%

50 pip: 2%

run a system with a real +0.15R gross edge through that. at 50 pips you net +0.13. at 20 you net +0.09. at 10 youre at +0.03. at 5 youre at -0.09 and its a losing system. nothing about the entries changed in any row.

the reason its hard to catch is you cant see it in results. spotting a 0.09R shift against normal trade noise needs around 950 trades, so a tight system thats actually negative hands you a couple of green months first and you spend them tuning entries.

what id do before evaluating anything is work out cost as a share of your average stop. it tells you what gross edge you need to reach zero, and for tight stops thats a much higher bar than people assume. and stop comparing brokers on raw spread, compare on spread divided by your stop. 0.3 pips is nothing at 50 and decides it at 5.

i put it in a sheet. spread, commission, average stop in, break even gross edge out. on my profile.

anyone running sub 10 pip stops profitably long term, genuinely asking. i couldnt make anything under about 15 work after costs and im still not sure if thats the maths or me

this is the number that made me stop trusting my own research process.

simulated 24 strategy variants with exactly zero edge, 100 trades each, and picked the best. the winner averaged +0.20R a trade and looked profitable in 100% of runs. every time. there is no version of this where you try 24 things and the survivor is unremarkable.

scaled it up. 2000 zero edge strategies, 250 trades each. best t stat 4.27, 49 of them clearing t=2. at normal thresholds youd expect around 100 false positives from that many tests. the search manufactures them.

the reason its hard to police is the count never gets written down. i remember the ten variants i tested deliberately. i dont remember the forty i tweaked and reran to see. so every strategy i keep is the survivor of a selection i cant reconstruct.

what fixed it wasnt a better threshold. its a second sample the search never touched. rerank the same candidates on held out data. a zero edge strategy that topped the first ranking stays top 2% of the second about 3% of the time. surviving twice is evidence. surviving once is arithmetic.

two things worth logging from day one because neither can be rebuilt later. the trial count, incremented by the code not by memory. and the spec, versioned separately from the runner.

the counter is on my profile. it increments itself so you cant lie about it, which is the whole point.

how many variants did you try before the one you trade now. real answer, not the one you tell people

was 40 trades below my last equity high and convinced something had broken. ran this before changing anything and its the first time i had an actual answer instead of a feeling.

the idea is simple. take your closed trades, shuffle the order a few thousand times, and for each shuffle record the longest run spent below a previous equity high. that distribution is what your own edge produces from pure sequence luck. no decay, no regime change, just the same trades in a different order.

mine, +0.32R a trade over the sample:

median longest run 22 trades

p90 38

p99 56

worst of 6000 shuffles was 74

so 40 was ugly and sat around the 92nd percentile. not an outlier, just the bad end of normal. would have rewritten the exit rule that week if i hadnt looked.

the limit is worth knowing before you trust it. i halved the edge and reshuffled that. the median run went to 32 but only 1.3% of those runs exceeded the original systems worst case. so a system running at half strength almost never trips the test. it just sits in the upper half of your distribution looking bad and normal at the same time.

meaning it catches dead and misses halved, and halved is what decay actually looks like. still better than the raw number and it uses your data instead of assumed win rates.

the script takes a csv of closed trades with a pnl column and spits out the distribution and where your current run sits. its on my profile, takes about a minute to run on yours.

whats the longest youve sat under a high on something that turned out fine

https://preview.redd.it/iz0axia4xcnh1.png?width=1400&format=png&auto=webp&s=556b5ea961f00af2c98006ca5cfb688d65276d72

took me two years to work this out properly and it applies whatever youre trading.

everyone knows costs eat something. what i missed is that the cost is a fixed amount and your risk unit isnt, so the same broker charges you completely different percentages depending on where your stop goes.

MNQ with a tick of spread and normal commission is about $1.70 round trip. against a 5 point stop thats 17% of your R. against a 40 point stop its 2%. ES works out the same shape, roughly 16% at a 2 point stop and under 2% at 20, because the tick is $12.50 and it doesnt care how wide you set your stop.

so take a system with a real +0.15R gross edge, which is decent. at 2% costs you net +0.13. at 6% you net +0.09. at 12% youre scraping +0.03. at 24% youre at -0.09 and its a losing system. same entries in every one of those.

the reason its hard to catch is you cant see it in your results. spotting a 0.09R shift against normal trade noise needs somewhere around 950 trades, so a tight system thats actually negative hands you a couple of green months first and youll spend them tuning entries.

what id do different, work out your round trip cost as a share of your average stop before evaluating anything. it tells you what gross edge you need just to break even, and for scalping thats a much higher bar than people assume. also stop comparing brokers on raw commission, compare on commission divided by your stop, its meaningless wide and decisive tight.

anyone scalping profitably long term? asking properly, i couldnt make anything with a stop under about 15 points on MNQ work after costs and im not sure whether thats the maths or me

after getting burned twice early on i built a dumb little routine that takes maybe ten minutes and has saved me from every bad purchase since. sharing because the question comes up here weekly.

one. find the publication dates. not the website, the platform. if a vendor sells tradingview scripts, every publication has a timestamp the vendor can't edit. compare the claimed track record against those dates. "profitable since 2023" with a first publication in june 2026 tells you everything. i keep a little script that diffs a vendor's claimed history against publication timestamps, takes seconds and it's embarrassing how often it flags something.

two. open the public tester. a serious vendor lets the platform show the strategy report on the publication page, where they can't photoshop it. if all you get is screenshots in a discord, that's not evidence, that's marketing. and if the tester is hidden entirely, well, that is also an answer.

three. ask where the red months are. every real record has them. i ask this one directly in dms and the reaction is the whole test. defensiveness or subject change, i walk. a straight answer with specifics, they stay on the list. one vendor i eventually bought from had the red month sitting right on their public chart with a note next to it, which honestly did more than any winning screenshot could.

bonus: check what actually ships after purchase. roadmaps are promises. publication pace is data. a vendor who published four things in the last six months will probably keep publishing. a vendor with a beautiful roadmap and no recent timestamps probably won't.

none of this tells you the strategy is good. it just filters out the sellers who are lying about the basics, which in this niche removes most of the field.

what's your filter? genuinely curious what other people check first.

looked at duration under the last peak instead of max dd after a stretch where i was sure the system had broken. ran it on my book first, then on a few others. the share of time underwater was consistent enough that i stopped treating it as a regime change.setup: 400-trade windows, three payoff shapes, similar expectancy, all +ev.60% wr, 1R:1R — median max dd 9R, longest underwater run 49 trades, below prior peak on 66% of trades

40% wr, 2R:1R — 16R, 77 trades, 81%

28% wr, 4R:1R — 19R, 58 trades, 82%max dd is what people constrain. duration is what makes you kill a live book. from inside the run it just looks like the last high is sitting there and nothing is working. i rewrote parameters four times in 2024 inside these stretches and still cant separate “edge decay” from “this is the default path.”added one field to the log: bars/trades since equity high. next time the curve goes stale i can check whether the current underwater length is in the historical distribution or actually an outlier.if you track this, whats the longest underwater run youve seen on a system that was still +ev after?

after getting burned twice early on i built a dumb little routine that takes maybe ten minutes and has saved me from every bad purchase since. sharing because the question comes up here weekly.

one. find the publication dates. not the website, the platform. if a vendor sells tradingview scripts, every publication has a timestamp the vendor can't edit. compare the claimed track record against those dates. "profitable since 2023" with a first publication in june 2026 tells you everything. i keep a little script that diffs a vendor's claimed history against publication timestamps, takes seconds and it's embarrassing how often it flags something.

two. open the public tester. a serious vendor lets the platform show the strategy report on the publication page, where they can't photoshop it. if all you get is screenshots in a discord, that's not evidence, that's marketing. and if the tester is hidden entirely, well, that is also an answer.

three. ask where the red months are. every real record has them. i ask this one directly in dms and the reaction is the whole test. defensiveness or subject change, i walk. a straight answer with specifics, they stay on the list. one vendor i eventually bought from had the red month sitting right on their public chart with a note next to it, which honestly did more than any winning screenshot could.

bonus: check what actually ships after purchase. roadmaps are promises. publication pace is data. a vendor who published four things in the last six months will probably keep publishing. a vendor with a beautiful roadmap and no recent timestamps probably won't.

none of this tells you the strategy is good. it just filters out the sellers who are lying about the basics, which in this niche removes most of the field.

what's your filter? genuinely curious what other people check first.

looked at duration under the last peak instead of max dd after a stretch where i was sure the system had broken. ran it on my book first, then on a few others. the share of time underwater was consistent enough that i stopped treating it as a regime change.setup: 400-trade windows, three payoff shapes, similar expectancy, all +ev.60% wr, 1R:1R — median max dd 9R, longest underwater run 49 trades, below prior peak on 66% of trades

40% wr, 2R:1R — 16R, 77 trades, 81%

28% wr, 4R:1R — 19R, 58 trades, 82%max dd is what people constrain. duration is what makes you kill a live book. from inside the run it just looks like the last high is sitting there and nothing is working. i rewrote parameters four times in 2024 inside these stretches and still cant separate “edge decay” from “this is the default path.”added one field to the log: bars/trades since equity high. next time the curve goes stale i can check whether the current underwater length is in the historical distribution or actually an outlier.if you track this, whats the longest underwater run youve seen on a system that was still +ev after?

been running a rules based setup for a while now, futures and fx. everything automated, exits written into the order at entry, no discretion after the trade goes on.

june was the deepest drawdown of the whole sample. not in the backtest years, on the live funded account with real money on it. and the thing i keep coming back to is that i had literally nothing to do. no stop to move, no position to trim, no "let me just reduce size until this settles down". the stop was already sitting there from the second the trade opened. the target too.

before this i traded discretionary for years and i know exactly what i would have done. i would have cut it somewhere in the middle, told myself i was managing risk, and then watched it come back without me. that was my actual leak for a long time, not entries. entries were fine. what killed me was intervening in the drawdowns that were already priced in.

quarter closed green, comfortably. not because i was disciplined that month, because there was nothing available for me to be undisciplined with. that distinction matters more than any indicator i ever tested.

anyway the honest caveat, one drawdown surviving proves nothing, i'm four months into live and that's a tiny sample. i wrote a little audit script that compares what my system would have done against what i actually did back when i was still touching things, and the intervention cost was uglier than i wanted to see.

curious how many of you have measured that on your own trades rather than just assuming you're the disciplined one.

something i wish someone had explained to me before i bought my first system. the number a vendor shows you and the number your funded account will actually produce are not the same animal, and the gap is not fraud, it's four specific things nobody deducts.

one. missed trades. no setup catches every fill. platform down, spread blows out, you're asleep, broker rejects. i model this as dropping a random quarter of trades from every simulated run. sounds brutal but if an edge dies from missing 25% of its trades it was never an edge, it was a sequence.

two. withdrawals. a backtest curve compounds forever because it never takes money out. your account takes money out, that's the entire point of being funded. every payout is capital that stops working for you.

three. the floor moves. this is the one that actually kills accounts. after every withdrawal your buffer above the drawdown limit shrinks. take money out aggressively and you spend your whole funded life a couple of normal losing days from termination. the strategy didn't fail, the withdrawal schedule did.

four. medians not best runs. one backtest is one path. if you simulate a thousand of them you get a distribution and the honest number to plan around is the middle of it, plus whatever the bottom decile says. anyone quoting you a single number is quoting the run that happened to look good.

when i stacked those four on my own numbers the realistic figure came out roughly 45% of the raw backtest. not because the strategy is bad, because the raw number was measuring a curve and i needed to measure an account. i ended up building a script that replays the whole lifecycle, eval to funded to payouts, so i could see how much of the damage was withdrawal timing versus the strategy itself. spoiler, withdrawal timing was most of it.

anyone here actually planning their withdrawals against their worst historical losing streak, or is everyone just taking money out when it's available?

took me two blown evals to work this out. both times my closed pnl was fine. i kept going back to the strategy because thats where you look, and the strategy wasnt the problem.

what killed it was heat. how far trades run against me before they work. on intraday trailing that peak against you eats the buffer even on trades that close green, so anything carrying 2R of open loss on a normal winner needs far more room than its results suggest. same trades on a static drawdown account would have gone through.

so you can be genuinely profitable and not sizeable at a given daily limit, and that isnt a quality problem. plenty of decent systems arent fundable at retail limits, and no amount of care during the eval changes it. nobody mentions this before you pay because the sales page is targets and fees.

what id do differently is check the fit first. pull worst excursion per trade from your export, take the median, work out what size survives it inside the drawdown, then see whether that size clears the target in a sane amount of time. if it doesnt, wrong account for the system rather than wrong system. i ended up putting that check in a script so it runs before funding rather than after breaching, mostly because doing it manually meant not doing it.

has anyone here actually picked a firm on drawdown type rather than price? curious whether people match it up front or find out on attempt two like i did

took me two blown evals to work this out. both times my closed pnl was fine. i kept going back to the strategy because thats where you look, and the strategy wasnt the problem.

what killed it was heat. how far trades run against me before they work. on intraday trailing that peak against you eats the buffer even on trades that close green, so anything carrying 2R of open loss on a normal winner needs far more room than its results suggest. same trades on a static drawdown account would have gone through.

so you can be genuinely profitable and not sizeable at a given daily limit, and that isnt a quality problem. plenty of decent systems arent fundable at retail limits, and no amount of care during the eval changes it. nobody mentions this before you pay because the sales page is targets and fees.

what id do differently is check the fit first. pull worst excursion per trade from your export, take the median, work out what size survives it inside the drawdown, then see whether that size clears the target in a sane amount of time. if it doesnt, wrong account for the system rather than wrong system. i ended up putting that check in a script so it runs before funding rather than after breaching, mostly because doing it manually meant not doing it.

has anyone here actually picked a firm on drawdown type rather than price? curious whether people match it up front or find out on attempt two like i did

went back through a year of fills for something unrelated and hit this. every calculation i had was built on planned risk, 1R, stop where the code puts it. but the realised average loser was well under that, because a decent share of trades never reached the stop at all. exited on a time rule, closed on an opposing signal, scratched out.

which means the loss distribution i sized off isnt the one im actually trading. same expectancy, different shape, and anything sensitive to shape rather than mean is quietly wrong. streak math, ruin probability, any daily or session level constraint. those all take a distribution as input and i was feeding them a two point one, win or full stop, when the real thing has a fat middle.

the part i hadnt considered is that it cuts both ways. capital wise im better off than the planned risk version suggested, more losers are partial. but per session it runs the other way, more trades fit before any limit binds, so the day lasts longer and i take entries the pessimistic version would never have got to. one of those errors is comfortable and the other one costs money.

the fix is boring. pull mae per trade and compare it to where the stop actually sat, then rebuild the loss distribution from realised exits rather than from the parameter. i wrote it into a script that runs monthly because doing it by hand meant doing it when i was already suspicious, which is the wrong time.

anyone here sizing off realised loss distribution rather than planned R? and if you are, did anything downstream change much or was it noise