Every strategy we've killed.

“Just copy the winning wallets” is one of the most-pitched strategies in crypto. We mirrored 205 real trades, then re-ran the test removing one wallet at a time. The entire edge came from a single lucky wallet: drop it and a winning record turns into a loss. A widely-believed edge, falsified on real data.

Hyperliquid's stock perps don't use a live market price directly, they rebuild it from an oracle and a lagging formula. The idea was to exploit that lag three ways: the weekend catch-up jump, the split-second the perp trails the index during the day, and the gap between two versions of the same stock. We simulated all three from the venue's own published formulas. Even in the best case the edge was smaller than the fee to trade it, so every version lost money after costs. A one-hour simulation settled it, before any weeks of data collection.

We bet prices would bounce off the day’s high and low; they didn’t. The setup is rare, the move was tiny, and it went the opposite way to the bet.

“Build a bot that trends HYPE” is the exact pitch in a thousand videos. We ran ours live, on real capital, against a bar set in advance: beat buy-and-hold. Over one week it returned −31.9% while holding lost 11.5%. No config rescued it; there was no skill in it, just leverage.

We assumed a price gap would close within half an hour; across 5,800 cases it usually didn’t, the gap tends to stick around, not snap back.

We trained machine-learning models to guess HYPE's next move from the live order book. Up close the moves were too small to beat trading fees; further out there were too few real examples to trust. No version came close to paying for itself.

An upgraded copy-trading bot made only two trades in the test window, far too few to prove anything, so we can’t call it a win.

Copying the single best-performing trader made money on paper, but 92% of it came from one trade; over three months he’d have lost to just holding.

We built a filter to improve the copy-trading bot, but the bot was shut down before the filter ever saw enough trades to be judged.

The “free” price gap between exchanges was never once wide enough to cover trading costs, not in a single hour out of 4,216.

A Bitcoin-linked stock doesn’t predict Bitcoin; it just moves alongside it after the fact, so there’s nothing to trade ahead of.

We wanted an AI to read price charts like a human, but the only trader it could have learned from traded coins we don’t touch, so we stopped before spending on it.

We tried to profit from the price gap between tokenized US stocks and Hyperliquid's version of the same stocks. The gap is tiny and professional traders already keep it closed, so there was nothing left to capture.

A pitch said you could farm the GRVT exchange for a fat trading rebate and a 10% yield. Against GRVT's own fee page, the rebate was a hundred times smaller than claimed and the yield needs millions in trading volume. It only works on paper.

Reading the order book to call the next tick is the dream of every screen-watching trader. We ran five classic patterns over 7.8 million ticks. The signals are real and rock-solid, and still far too small to cover the cost of the trade. Real, measured, and not bankable.

The plan was to farm the Lighter exchange for its token giveaway, like an earlier farm that worked. But the token had already launched and the giveaway was paid out months before the idea reached us. Nothing left to farm.

Yesterday’s funding rate tells you nothing useful about tomorrow’s price direction, and even if it did, the edge is too small to cover costs.

The volatility around the 8-hour funding reset is real, but it’s spread evenly through the cycle, there’s no special moment to time a trade to.

Trading the speed-up in volatility, rather than its level, added nothing, once you account for the level, the acceleration actually hurts.

Bitcoin and Solana volatility move together at the exact same moment, so there’s no head-start to trade on.

The bot’s own “good day” filter actually picked worse days than the ones it skipped, the filter points the wrong way.

Betting that forced sell-offs keep falling worked in old data but fell apart on fresh data, the effect shrank tenfold and flipped direction.

The trade needed HYPE funding data from Binance and Bybit, but HYPE doesn’t trade there, so there was literally nothing to measure.

Liquidation cascades bounce back rather than keep running, so riding them as they speed up loses money.

“Ride the liquidation cascade” is the classic momentum play. We measured 15,992 cascades and sorted them by intensity; the most violent ones snapped back the hardest, the exact opposite of continuing. The detector works, the trade is backwards. Killed.

HYPE’s buy/sell gap is too thin to earn the rebate for posting both orders, the spread disappears before both can fill.

When several exchanges agreed on funding direction, that agreement still didn’t predict anything you could trade.

Betting against “overstretched” moves looked great on a chart but lost money on every single coin we tested it on.

This pattern made a tiny profit, but over only eight trades, far too few to tell skill from luck.

There’s no reliable reversal at the Asian market open; the result was indistinguishable from random noise.

We tested about ninety chart patterns at once; after correcting for the fact that a few always look good by chance, not one held up.

A score meant to flag a fragile order book worked on old data but couldn’t tell fragile from stable when shown fresh data.

We checked other exchanges for an edge Hyperliquid didn’t already have; none offered one, so we stayed put.