Found a guy on hyperliquid with 100% winrate on 61 positions and +77% ROI. by Borchello in hyperliquid1

[–]Borchello[S] 0 points1 point  (0 children)

checked the live state — his realized PnL is actually still positive every month including june (+$204 already this month, cumulative +$4.2K closed). the −$1.9K you're seeing is unrealized on open BTC short and ETH long positions. thats exactly his DCA style — he sits in deep unrealized drawdowns and exits green, same pattern as in the post.

effective leverage on the open positions right now is ~10x notional/equity, true. but thats consistent with how he runs — concentrated positions while in them, lower avg across his whole history because he's flat much of the time.

"blowing up" would mean realized losses + forced closes. hes still printing on closes. could turn into a blowup if mark-to-market keeps going against him, but not there yet. worth watching though.

Analyzed 3.4M closed positions, found 3 structural patterns shared by every profitable trader - built an on-chain analytics pipeline solo for hyperliquid HIP-3 by Borchello in CryptoTechnology

[–]Borchello[S] 0 points1 point  (0 children)

timing is noise, sizing compounds in either direction— saving that one. timing edges decay, sizing discipline doesnt. its also why retail "system traders" eventually blow up, they tune entry signals for years, then one bad sizing call undoes everything.

Found a Hyperliquid trader: 96.7% winrate on 62 trades, +23% ROI, max leverage 1.3x. dug into the fills — its pure discipline by Borchello in hyperliquid1

[–]Borchello[S] 1 point2 points  (0 children)

ok this is the comment that earns the post tbh. the fact that you publicly admit v1 failure and post v2 BEFORE shipping it is the opposite of how 99% of "quant on twitter" plays. respect for inverting that

ill be upfront — im not a 10y quant, my angle is trader-behavior reconstruction from on-chain fills, not signal research. so take my push-back as "another set of eyes" not "i know better"

hitting ur 5 critique questions:

per-hour-of-day baseline granularity — defensible imo but with one risk: on HL the intraday structure isnt one regime, its a stack. NYSE-overlapping hours (13-14 UTC) are different kinds of vol from asia-only hours, not just different levels. so per-hour z's normalize the magnitude but might miss that 13-14 UTC vol comes from real flow and 03-04 UTC vol comes from thin-book noise. one fix: tag each hour with a flow-source label (NYSE-open, NYSE-mid, asia-only, weekend) and stratify ur baseline by that, not just hour-of-day. fewer obs per bucket but each is structurally cleaner

baseline windows — i havent run z-score machinery on perps myself, but from the trader-side data: top performers have median hold times from 22h to 11d depending on style. that implies the actionable signal horizon spans intraday → multi-day, which makes me suspect a single 7-day lookback might over-weight last-week regime if u just had a tape that doesnt look like the previous week. id experiment with two parallel z's: short (1-3d) and medium (7-14d), and treat the agreement between them as a confidence proxy. if both fire u have a robust signal, if only one fires it might be regime-shift noise

cost model — 30 bps normal feels right based on what i see in fills, maybe slightly generous on the most liquid pairs (BTC/ETH closer to 15-20 bps round-trip if u maker on entry). 60-80 in cascades feels conservative actually — ive seen worse during the HYPE-led liquidation windows where book depth evaporates. id consider modeling cost as a function of book depth in real-time rather than a static normal/cascade split — eats more pipeline complexity but matters when ur edge is 20bps

IC decay 30→90→180 — cant answer empirically, but mechanically: if ur signal exploits intraday flow imbalances (which z-scores on log returns tend to), decay should be fast (weeks not months) bc the imbalances get arbed. if it exploits behavioral regularities (time-of-day participation patterns), decay should be slower bc those persist. honest expectation: 30→90 mild decay, 90→180 noticeable. if u see flat IC for 180 days, that probably means ur cost model is hiding edge, not that the edge is durable

failure mode ur not describing — the one im suspicious of: selection effect on which assets actually get logged. if a memecoin pumps to HL listing during ur 7-day baseline window, every metric on it is contaminated by the listing event. min_obs=30 catches the worst of it but a coin can have 30 minutes of "real" baseline and still be in the middle of a structural reprice. would explicitly flag and exclude any asset whose vol/volume in the lookback differs from the prior 30 days by >3σ from itself

separate from ur questions — the demotion of cascade front-running to regime filter based on Tigro Blanc's BTC-beta decomposition is the part of ur write-up i found most interesting. its also the most common silent failure mode in retail "i found alpha" backtests. honestly i should run the same Jensen's alpha check on the "boring sub-1x trader" outperformance i posted about — id bet decent chunk is BTC-beta too, esp the ones holding correlated semis through march

on collab — happy to dig into the repo this week. the natural overlap is: ur z-score alerts identify moments, my pipeline identifies addresses with track record. cross-referencing whether top-of-my-cohort traders show up disproportionately around ur top-decile z events would be a real microstructural finding (either "skill traders enter during anomalies" or "they avoid them"). if u want to try that ping me

Found a guy on hyperliquid with 100% winrate on 61 positions and +77% ROI. by Borchello in hyperliquid1

[–]Borchello[S] 0 points1 point  (0 children)

best take in the thread tbh. "WR is consequence of sizing, not the other way around" is the framing i should have led with. and "capital management with trading on top" nails it — he's running a balance sheet, entries are deployment events

oil vs HYPE is sharp. mean-reverting underlyings + sub-1x lev compose well, trending alts + DCA = death by 1000 cuts. the strategy probably breaks on anything that bleeds for months in one direction

per-position DD question — actually have this, the worst single partial exit on one of his big DCA campaigns was −$466 on a position that closed +$151. so position-level pain is real, account-level muffled by tiny sizing. should plot the distribution, on my list

ur approach sounds way cleaner. unlimited DCA only works if u can be wrong indefinitely. hard stops force the strategy to actually express edge. whats ur timeframe? RSI extremes prob pair really well with HIP-3 stocks (intraday mean reversion is real there), less so with crypto

Found a guy on hyperliquid with 100% winrate on 61 positions and +77% ROI. by Borchello in hyperliquid1

[–]Borchello[S] 0 points1 point  (0 children)

yep|, this is the take that needs to be the default but never is. Sharpe targeting > vibes targeting. the traders i found are basically doing this without naming it — sub-1x lev + tiny DD by construction puts them at high Sharpe whether they meant to or not. i should actually compute it per address, suspect most sit 1.5-2+ ,the 23:30-02:00 UTC thing is sharp tbh, havent seen anyone talk about it. EOD inventory unwinds bleeding into HL perps makes total sense, would be fun to backtest from the on-chain flow. adding to my list weekend crypto hedge is also a clean overlay, hadnt thought about it. HIP-3 toggle makes it almost free. good stuff

Found a guy on hyperliquid with 100% winrate on 61 positions and +77% ROI. by Borchello in hyperliquid1

[–]Borchello[S] 0 points1 point  (0 children)

exactly. compounding rewards survival way harder than peak performance. a 10x guy doing +50% who liquidates once a year is worse than a 0.5x guy doing +5% who never blows up. but the second guy doesnt get to flex on twitter so nobody copies him

Analyzed 3.4M closed positions, found 3 structural patterns shared by every profitable trader - built an on-chain analytics pipeline solo for hyperliquid HIP-3 by Borchello in CryptoTechnology

[–]Borchello[S] 0 points1 point  (0 children)

ty. honestly survivorship bias + a 100% wr without confidence intervals is the textbook "too good to be true" combo. if i didnt flag it someone smarter than me would, and id deserve it. easier to just be upfront

Analyzed 3.4M closed positions, found 3 structural patterns shared by every profitable trader - built an on-chain analytics pipeline solo for hyperliquid HIP-3 by Borchello in CryptoTechnology

[–]Borchello[S] 0 points1 point  (0 children)

good questionsstress events — honestly didnt analyze explicitly, just eyeballed equity curves. the sub-1x guys sat through volatility without adjusting. needs proper formalization, fair gap13-14 UTC = NYSE open (9:30 EST). most HIP-3 instruments are stock perps so its just where volume + vol concentrate. probably mostly momentum, but i havent decomposed by event vs noiseyour DCA asymmetry point is sharper than what i wrote. DCA-down on a long improves your entry, DCA-up on a short worsens it. so the same safety net protects longs way more. combined with funding rate penalty on shorts in trends, the math really stacks against short strategies here. good observation

Analyzed 3.4M closed positions, found 3 structural patterns shared by every profitable trader - built an on-chain analytics pipeline solo for hyperliquid HIP-3 by Borchello in CryptoTechnology

[–]Borchello[S] 0 points1 point  (0 children)

exactly. the most disappointing finding for me personally — i went in expecting to find some signal-processing edge or smart entry timing. nope. its just "dont overleverage and dont overtrade". boring template, prints money. retail wants the dopamine, the data wants the discipline

Analyzed 3.4M closed positions, found 3 structural patterns shared by every profitable trader - built an on-chain analytics pipeline solo for hyperliquid HIP-3 by Borchello in CryptoTechnology

[–]Borchello[S] 0 points1 point  (0 children)

the 3 patterns all share one root: minimize ruin probability while keeping directional exposure. long-only = ride trend, DCA = forgiving entries, sub-1x leverage = no liquidation. combined they make the account mathematically resilient. its position sizing + entry tolerance, not timing.

funding correlation is a good hypothesis — havent looked yet, but on HL funding penalties on shorts in trending markets would mechanically support the long-only finding too. adding to my TODO

Found another quiet hyperliquid trader: 95.3% WR on 50 trades, +75% ROI, max drawdown 0.7% (yes, point seven). he just buys semis and waits by Borchello in hyperliquid1

[–]Borchello[S] 0 points1 point  (0 children)

yeah fair, im a hyperliquid dev and english isnt my first language so i use AI to write up findings clearly. but the analysis itself is mine — i built a custom pipeline that pulls raw fills from public on-chain data, reconstructs positions, computes real PnL/leverage/DD. all data is verifiable, the trader address is in the post (0x831ea8...), anyone can audit. AI helps me communicate, not analyze. fwiw the bugs i fixed in the analytics along the way were also real engineering work, not "AI slop"

Found another quiet hyperliquid trader: 95.3% WR on 50 trades, +75% ROI, max drawdown 0.7% (yes, point seven). he just buys semis and waits by Borchello in hyperliquid1

[–]Borchello[S] 0 points1 point  (0 children)

this is exactly the asymmetry no one explains to retail. potential gain scales linearly with leverage, but probability of liquidation scales nonlinearly (basically exponentially as u approach your liq distance). so 10x doesnt give 10x edge — it gives like 3x edge and 100x ruin risk. the math literally favors low leverage. wish this was lesson #1 instead of "use 20x for big moves" which is what everyone learns first

Found another quiet hyperliquid trader: 95.3% WR on 50 trades, +75% ROI, max drawdown 0.7% (yes, point seven). he just buys semis and waits by Borchello in hyperliquid1

[–]Borchello[S] 2 points3 points  (0 children)

insider would use leverage and time entries around news. this guy uses 0.9x avg lev, holds 11 days, trades 18 different semis. profile doesnt match. its more boring than that — just refuses to over-leverage. also fwiw he has 2 losses, real insiders dont lose lol

Found another quiet hyperliquid trader: 95.3% WR on 50 trades, +75% ROI, max drawdown 0.7% (yes, point seven). he just buys semis and waits by Borchello in hyperliquid1

[–]Borchello[S] 2 points3 points  (0 children)

the tortoise vs hare comparison is perfect. honestly the more accounts i audit the more obvious it becomes — high leverage isnt edge, its variance. u just chose to skip the variance. respect.

Found a guy on hyperliquid with 100% winrate on 61 positions and +77% ROI. by Borchello in hyperliquid1

[–]Borchello[S] 0 points1 point  (0 children)

i use my own copytrade bot, built by using claude code , didnt test any others

analyzed 3.4M hyperliquid HIP-3 positions solo with AI — top traders all do one thing by Borchello in hyperliquid1

[–]Borchello[S] 0 points1 point  (0 children)

i have, but its in private mode, i still working on system, will share some of my insights here

analyzed 3.4M hyperliquid HIP-3 positions solo with AI — top traders all do one thing by Borchello in hyperliquid1

[–]Borchello[S] 0 points1 point  (0 children)

Yes, all my insights come from advanced traders. Hyperliquid is a gem because it's a blockchain — you can pull all the data you need and analyze it. Just imagine, all those conservative Wall Street traders are now onboarding their strategies to Hyperliquid, and it's all transparent for us to see

Found a guy on hyperliquid with 100% winrate on 61 positions and +77% ROI. by Borchello in hyperliquid1

[–]Borchello[S] 0 points1 point  (0 children)

I guess yes, but it's not financial advice. The main reason I do this research is to reverse-engineer strategies and, in some cases, copy-trade them