Rose
5.6K posts

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Rose retweetledi

Rose retweetledi

🧵 Thread: How Pros Take Losses and Sit on Their Hands
1/ Most retail traders lose money not because their ideas are bad — but because they can’t handle losses like professionals and refuse to sit on their hands.
The edge isn’t in predicting markets. It’s in emotional control and discipline. Let’s break it down.

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Most traders lose because they never review who they were last week.
Weekend journaling is where the real edge is built.
Here’s how to move forward into a new trading week after reviewing your trades 👇
1/
Your weekend review is NOT about beating yourself up.
It’s about pattern recognition.
The market already took your money if you made mistakes.
Don’t let it take the lesson too.
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1/ Been trading full-time for a few years, and if I’m honest, one of the smartest habits I ever built is sitting down every weekend to review my trades.
It’s not flashy, but it’s quietly one of the highest-ROI things I do. Small edges stack up, and staying sharp keeps the profits compounding.
Here’s why I record every trade and still journal by hand:

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I asked my agent to analyze the 100 most profitable $TAO Bittensor wallets by percentage return relative to wallet size.
What most of them had in common:
Around 10 buys and sells over 30 days.
Little to no allocation in Root.
And that’s it. No clear pattern in subnet similarity, allocation percentage, nothing.
Draw your conclusions.
Sometimes less is more.
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When de-registered subs came back online with a new team, liquidity was very thin at the start. This allowed small fish like me to load up in the first month - a ton of small buys - before anyone with real size moved in. They changed it to add liquidity at the start and bots now dive in and push price up 300%+.
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Thin liquidity edge gone after $tao changes. Sold subs today all in decent profit. 7x'd my $tao. Got an idea for a new edge.
low(44)🇨🇦 🐀@jlow7865
Have 6x'd my $tao stack. Closer to 7x at this point. All thru having low expectations. Not looking for the next 50x blue chip. 10-15 in portfolio to spread out the risk. Buy early + on the floor. Slow + steady 2-4x and I take profits. Been working pretty good.
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A 4B-parameter model on SN97 is now scoring 0.94 on HumanEval — beating its 35B-parameter teacher (0.872) by nearly 7 points on code generation.
8.75× smaller. Better at the actual task. On consumer hardware.
This is what distillation is supposed to do.
distil.arbos.life
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SN97 Arena v3 is live.
Instead of pretending miners won’t optimize the eval, we designed the eval so optimization becomes productive.
That is the Goodhart problem in reverse. If the benchmark is broad, procedural, adversarial, and tied to real model behavior, then “overfitting” starts to look a lot like building a better model.
KL still matters, but it is no longer the center of gravity.
SN97 is moving toward evals that reward durable capability, not leaderboard tricks.

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