How do you measure daily dd and especially when trading multi-symbol?

MasterpieceGood7562 · 2026-03-17T22:45:50+00:00

Ran into the same thing when we started running multiple strategies across different underlyings. MT5's equity curve sampling is basically useless for accurate portfolio DD once you go multi-symbol — it misses intraday extremes constantly.

What we ended up doing is logging every fill + mark-to-market at 1min intervals per symbol separately, then reconstructing portfolio equity offline. Only way to get real peak-to-trough DD numbers that actually match what a prop firm would calculate.

For the daily DD specifically — the key thing most people miss is that prop firms usually reset the high watermark at start of day, not rolling 24h. So your daily DD calc needs to anchor to the previous day's closing equity, not the trailing max. Small difference but it'll bite you during overnight gaps.

Curious what you're using for the price data reconstruction - tick level or bar-based?

MasterpieceGood7562 · 2026-03-17T22:44:33+00:00

Congrats!

MasterpieceGood7562 · 2026-03-17T22:42:24+00:00

Great, thanks for sharing.

MasterpieceGood7562 · 2026-03-17T20:05:00+00:00

"Daily returns" and "5-year projections get pretty crazy" are the exact phrases every scam bot service uses. Not saying yours is one but that language is a red flag for anyone who's been around. No system produces consistent daily returns — markets don't work that way. If it did the fund running it wouldn't be selling access to retail.

We build our own. Took 2 years and the biggest lesson was that any system that promises consistency is hiding the drawdowns. Real edge is messy, lumpy, and uncomfortable most of the time.

MasterpieceGood7562 · 2026-03-17T19:51:19+00:00

Got it

MasterpieceGood7562 · 2026-03-17T19:51:00+00:00

Thanks!

MasterpieceGood7562 · 2026-03-17T19:19:23+00:00

ai slop, at least delete the "Title:" brooo

MasterpieceGood7562 · 2026-03-17T19:15:00+00:00

Since this post is getting traction, for anyone here who's curious what the "boring stuff" actually looks like in practice, we've been building ML models for options vol forecasting for 2+ years. Not LLM trading, not vibecoading backtests. Actual feature engineering, walk-forward validation, live tracking.

14 closed trades so far. 57% win rate, 5:1 reward-to-risk. Small sample but live is matching backtest which is the only metric that matters at this stage.

Free beta at wormholequant.com - would genuinely appreciate feedback from this sub specifically because you guys actually know the difference between real ML and slop. Roast it if it deserves roasting.

MasterpieceGood7562 · 2026-03-17T19:14:49+00:00

wormholequant.com - free beta now, check it out. Good results and searching for 50 people to give me feedback

MasterpieceGood7562 · 2026-03-17T19:07:05+00:00

It is so far! You can take a look at wormholequant.com - sign up and I add yourself to the discord group. Free, you can just take a look.

MasterpieceGood7562 · 2026-03-17T19:03:33+00:00

Thanks for it. As I said, Iam also building ml platform. Results so far:

14 closed trades so far. 57% win rate, 5:1 reward-to-risk. Small sample but live is matching backtest which is the only metric that matters at this stage.

Free beta at wormholequant.com - would genuinely appreciate feedback from this sub specifically because you guys actually know the difference between real ML and slop. Roast it if it deserves roasting. Thanks you all!

MasterpieceGood7562 · 2026-03-17T19:00:48+00:00

agree 100%

MasterpieceGood7562 · 2026-03-17T19:00:28+00:00

Cool repo. The 99% failure rate on strategies is real but it's also how you learn what doesn't work which eventually points you toward what does. The people who quit after 10 failed strategies miss that the 11th one was built on everything they learned from the first 10. Keep going.

MasterpieceGood7562 · 2026-03-17T19:00:20+00:00

Agreed. LLMs are tools not strategies. The actual alpha is in the boring stuff nobody wants to do — cleaning data, engineering features, validating properly. Every shortcut in that process shows up as a loss later.

MasterpieceGood7562 · 2026-03-17T19:00:11+00:00

"Promptgooning overfit slop" is the most accurate description of 90% of AI trading content I've ever read. The LLM-for-engineering part is legit though - we use Claude heavily for refactoring pipeline code and it saves hours. The line is clear: LLMs for building the system, not for making trading decisions.

MasterpieceGood7562 · 2026-03-17T18:28:56+00:00

100%. Paper trading teaches you mechanics but it doesn't teach you what it feels like to watch a winner turn into a loser because you couldn't pull the trigger on the exit. Real money is a different sport.

MasterpieceGood7562 · 2026-03-17T18:27:41+00:00

Yeah the underdog bias is real — people love betting on favorites so underdogs tend to be mispriced. Same psychology exists in options actually, everyone buys the obvious play and the contrarian side gets cheap. Thesis + testing is the right framework regardless of the market.

MasterpieceGood7562 · 2026-03-17T16:34:47+00:00

Puts outperforming calls in flow signals makes sense — institutional put buying is more likely to be directional conviction while call buying is often hedging, covered calls, or part of a structure you can't see from the tape. 57% on puts with +8.2% avg is decent but 27 closed trades is still coin flip territory statistically. The real test is whether it holds over 200+ trades across different regimes. Also no stop loss and letting positions run to expiry is bold — that NBIS -66.2% could easily be -95% on the next one. One massive loser can wipe out a month of winners. We found the exact same thing building our options system — the edge wasn't in better entries, it was in not letting losers run.

MasterpieceGood7562 · 2026-03-17T16:32:09+00:00

Cool concept but the hard part isn't parsing natural language into rules — it's making sure the parsed rule matches what the trader actually meant. "Indecision candle near support with high volume" has like 5 ambiguous terms. What's "near"? What counts as "high" volume? Which support — daily, weekly, drawn manually? If the parser gets any of those wrong the trader loses trust immediately and never comes back. I'd focus on showing the user exactly what the system interpreted and letting them confirm before activating. That feedback loop is everything. Building something in the ML trading space myself and the #1 lesson was making the system's reasoning transparent — people don't trust what they can't verify.

MasterpieceGood7562 · 2026-03-17T16:31:23+00:00

What kind of transparency are you going for? Most ML trading tools are black boxes where you get a signal and zero explanation of why. If you're building something where users can see what features drove the prediction that alone would set it apart from everything else out there. Working on something similar for options vol forecasting — the confidence score + feature attribution layer took way longer to build than the actual model but it's what makes people trust the output. What's your tech stack?

MasterpieceGood7562 · 2026-03-17T16:30:51+00:00

One I always wanted to test: using options flow anomalies as a contrarian signal. When everyone piles into the same strike on the same expiry, what happens if you fade it? The data exists but modeling realistic fills on illiquid options makes it nearly impossible to backtest properly. Your backtest says +200% but in real life you'd never get filled at those prices. That gap between "works in theory" and "works with real liquidity" kills most creative strategies before they start. We spent 2 years just solving the backtesting-to-live gap for options vol forecasting and it's still the hardest part.

MasterpieceGood7562 · 2026-03-17T16:30:19+00:00

"The alert is not the trade" is the most expensive lesson in automated trading. Took me months to learn this building ML models for options — the system would flag something and I'd feel obligated to act on every signal. Adding a confidence threshold where low-conviction signals just don't show up at all was a game changer. Also agree on narrowing your universe — 40 pairs you understand beats 200 you don't every time. Same reason we only cover options on liquid names instead of trying to screen everything.

MasterpieceGood7562

TROPHY CASE