15 QUANT STRATEGIES IN ONE TABLE, A 30.8K-VIEW POST CLAIMS. NO CODE, NO PUBLIC BACKTEST, JUST A COLUMN FOR WHEN EACH ONE STOPS WORKING
Edges do fade. That column ships with nothing anyone can rerun.
I read four posts from one account, took the pipeline apart and checked each number against public sources. They double as a trading-platform ad promising AI verdicts on strategies described in plain words.
→ INPUT: a strategy idea or an original paper, in plain words, no code
→ BUILDER: an AI tool builds the strategy and runs it on years of market data
→ TABLE: 15 widely cited strategies, last column naming the regime where each fails
→ HOLDOUT: the author says no chart counts until it's tested on unseen data
→ LIVE: survivors can go to a connected broker; execution details are never given
What survives a check
> claimed: Momentum on BTC +1,537% for 2020-2026, Sharpe 1.02, the author's own backtest
> claimed: Betting Against Beta dead at -7.78% a year while the market made +14.86%, same author
> checked: a 2016 Journal of Finance study of 97 published predictors finds returns 26% lower out of sample, 58% lower after publication
> checked: the momentum paper covers futures and forwards, not Bitcoin; a replication argues it fades after 2009-2013
> checked: the literature still argues over Betting Against Beta; critics tie its results to how the factor is built
> not public: code or data behind any row, or any source for the $500,000 Citadel PhD salary; published estimates say $275k-$475k
Decay is measured, and the biggest in-sample returns decay most.
So +1,537% on one asset in a bull-heavy window needs a public holdout.
Ask for code, sample dates and the holdout before any row touches broker keys.
↓ The terminal below is that table: 15 rows going dark, each tagged claimed.
STOP DOING YOUR OWN APP MARKETING.
I handed mine to Muse for 24 hours.
It wrote the posts.
Made the visuals.
Picked the timing.
Replied to comments.
1.1M+ views in one day.
I didn't touch a thing except approve.
Here's exactly how I set it up 👇
this should take a team and a month...
but REA with Opus 5.5 do it in one evening
the repo goes through the program section by section, leaving the function, the call and the string under each finding
Claude never touches the program itself, it only gets that trail:
> backed by the trail, so it's a statement you can act on
> reasonable but unbacked, so it's marked as a guess
> unobservable, parked beside the section that talks to a server
then a test fires back at the original and the broken version gets caught
the spec gets written from the backed part only, and the build never sees anything else
the install, the loop and the walls it runs into are in the article below
This is f*cking insane...
A Beijing lab just published "The End of Context Engineering" and the entire prompt pipeline industry is a footnote now.
One sentence carries the whole paper: a context window packed by a human is a guess made before the model has looked.
You pack what you would have needed. The model needs something else. Too much of the obvious, none of the surprising.
Their fix is almost rude in its simplicity. Give the model an index, one line per thing that exists, never the content. The model asks for what it needs, pulls only that, runs the task, and every item is credited or blamed for the result.
What did nothing gets dropped. What helped gets pinned for the next task of the same kind.
300 tasks later: score up 0.14, window 81% smaller, 212 items pinned, 4,970 thrown out.
A person cannot pack three hundred windows. A loop can score them.
So the curator leaves and one file stays: the index. You name what exists. The model picks what to read.
Prompt pipelines turn disposable. The shelf turns into the job.
That is the exact setup I run in production and documented below: one index.md, 300 Kimi K3 tasks, zero windows packed by hand.
If you remember one line: index the shelf, not the page ↓