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우린 $Nado를 파밍하는게 아니고 크라켄의 L2 코인 $INK 를 파밍하는겁니다
글구 지금 xag 대상으로 4배 포인트 부스트를 하고 있습니다 저번주 하루 100K정도 거래했는데 100포인트 정도 들어왔네요.
베어케이스로 포인트당 1달러로 가정해도 손해보는 장사가 아닙니다
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UnD
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Silver (XAG) perpetuals now carry a 4x point multiplier through this week's snapshot. Points are earned through trading and market making. After this snapshot, the multiplier steps to 3x for takers and 4x for makers into the following week.
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UnD retweetledi
Pair trading remains one of the more underutilized strategies in crypto.
Your exposure becomes the spread between two assets rather than the direction of the market.
In one case study our analyst examined, adding the short leg took the Sharpe from 0.35 to 1.45. Max drawdown dropped from 64% to 45%. Volatility declined by over 20%.
The pair sat at all time highs while the long leg alone was down 40% from its peak.
Pair trading allows you to express a relative view without taking on full directional exposure. You can profit while both assets decline, as long as your outperformance thesis holds.
The wider the gap between winners and losers in crypto, the more valuable this approach becomes.
flip@trevor_flipper
"HYPE will go up" Most traders on CT have the same conviction structure: "I think A outperforms B." But almost nobody actually trades it that way. They take the naked long on A, eat 104% annualized vol, get stopped out on a wick that had nothing to do with their thesis, and blame timing or excess leverage (the leverage part might be true). The structure of their bet was their problem. The value add from pair trading has become increasingly obvious to me over the last few months as crypto has traded lower. For fun, I've spent the last few days building out the case for pair trading in crypto, using HYPE/SOL as the case study because it's a trade half of CT has had on in some form over the past year. The results honestly surprised me. Same thesis, same conviction, just adding a short leg on SOL against the HYPE long, and the Sharpe goes from 0.35 to 1.45, and your trade is ATH right now while $HYPE is down ~40% off its ATH. The pair is sitting at its all-time high today. Neither HYPE nor SOL are. If you were right about @HyperliquidX being a fundamentally better bet than Solana, the pair captured that and only that. It didn't care whether BTC was at 60k or 120k. I have changed my view on portfolio positioning over the last few months given the bear market and the inability to "hide" from drawdowns in naked single asset positions. I was frustrated that I could be right on my thesis but get absolutely worked over by the market. And this is largely why I have been pair trading more. A pair trader can profit even during severe drawdown because the relative thesis (HYPE outperforms SOL) holds regardless of market direction. Over the course of almost a year, the HYPE/ SOL pair delivered a Sharpe ratio of 1.45, institutional quality, while cutting max drawdown from -64% to -45% and volatility by 21%. The directional trader with the correct fundamental view earned a Sharpe of 0.35. The pair trader earned 4x better risk-adjusted returns by adding a single short leg. For those with this position, the thesis never changed and you were right and mitigated vol. Pair trading isolates the alpha you actually have (or the alpha you think you have) and discards the market noise you don't want. As crypto matures, garbage assets go to zero and high-quality operators build trillion-dollar businesses. In that world, pair trading becomes even more obvious and advantageous. You can place these trades on your preferred perp dex or use an onchain OEMS. However, with basket trading, an OEMS helps out a lot. In a forthcoming companion piece, we extend this framework to multi-asset baskets, demonstrating that the same variance cancellation principles can compound with additional legs, reducing volatility from 104% (naked) to 82% (pair) to 57% (basket), with a Sharpe of 1.80.
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UnD retweetledi
Bittensor (TAO) is shaping up to be the “golden child” of the Crypto x AI narrative
At ~$3B, with supply side emissions collapsing post-halving and subnet demand accelerating, the asymmetry is becoming hard to ignore
With so much noise around @opentensor, it’s easy to miss what actually matters; here’s a clean segmentation of the ecosystem and the subnets I’m paying attention to:
a) Decentralized Compute:
Subnet (SN) 64 Chutes: @chutes_ai Distributed Serverless AI Compute Platform. Compute is already becoming a bottleneck in traditional AI, so it's logical decentralized solutions will more than supplement the shortfalls
it launched confidential compute last month with its Trusted Execution Environment (TEE); strong candidate to perform as Privacy AI takes off in 2026
SN120 Affine: @affine_io is an incentivized Reinforcement Learning (RL) arena for “reasoning”: miners compete to make measurable improvements on tasks like coding
b) AI Training:
SN 3 Templar: @tplr_ai providing the decentralized solution to AI training, and recently cited by the Anthropic Co-founder as the largest decentralized model (they've observed) when discussing compute constraints
c) AI Agent
SN 62 Ridges: @ridges_ai enable AI Software Engineers with the intention of fully replacing human coders.
This is inevitable; AI will become more competent than humans at code, when you have AI coders reviewing and iterating on existing coding using reinforcement learning the software creation will evolve at a parabolic rate
d) AI Trading:
SN 15 Bitquant: @BitQuantAI is the "World’s first crypto AI quant" to automate trading. Most of institutional trading is automated so profitability will come down to who has the best AI quant strategies
SN 8 Vanta: @vantaSN8 crowdsources trading strategies and produces market signals, rewarding the best performers
SN 50 Synth: @SynthdataCo is a price prediction subnet that iniitally focused on Bitcoin price forecast analysis, and has since bridged to more assets
e) Confidential Compute:
SN 4 Targon: @TargonCompute provides secure cloud with high-performance GPUs and CPUs; something that's fast becoming in short supply
f) Data curation:
SN 33 Ready AI: @ReadyAI_ has Harvard incentive designers figuring out the subnet economics for emissions, while making the world's data accessible to AI
g) Vision Models / AI:
SN 44 Score: @webuildscore uses Vision AI to enable businesses to operate more effectively. Recently partnered with @ReadingFC, has a large commercial contract with a European petroleum supplier and many more in the pipeline
SN 72 (NATIX) Streetvison: @NATIXNetwork are partnered with @grabid (Uber of SE Asia)
They recently revealed their "World Vision" tech; autonomous vehicles are going to become more prevalent as geospatial awareness improves. SN72 Streetvision leans into Vision Language Action (VLA) models to capture edge case data sets which will be invaluable for autonomous vehicles
h) Quantum Computing:
qBit labs SN 48 + SN 63: @qBitTensorLabs publicly launched @OpenQuantum_ at Q2B Silicon Valley in front of the leading names in quantum gathering decent attention from big names in quantum
I) AI safety:
SN 60 Bitsec: @bitsecai AI powered code vulnerability detection; something that'll become increasibly important as cybersecurity becomes an increased attack vector
J) DeSci:
SN 68 Metanova: @metanova_labs is an AI drug discovery company developing therapies to reprogram body and mind
SN 76 Safe Scan: @SAFESCAN_AI recently launched a skin scan app to help battle cancer, and has been accepted into the EU-supported UNICORN HUB accelerator
k) Other:
- SN 18 Zeus: @zeussubnet weather prediction systems; invaluable for agriculture and many other industries
- SN 16 Bit Ad: @bitads_ai Advertising leveraging AI systems; "Social Dilemma" vibes returning to the forefront of attention here
- SN 34 Bitmind: @BitMindAI helps with Deep fake detection; something that's going to become increasingly valuable in a world where AI proliferates fake news
Ultimately how I think about the inevitability of a subnet comes down to just a few things:
1) Which subnets are solving for the largest problems; data constraints etc?
2) Can the subnet team execute on their vision?
3) What are the sinks for the alpha token (buybacks from existing commercial arrangements / revenue) and will that outperform TAO?
If you're looking for a more comprehensive analysis on the ecosystem, follow @KhalaResearch and turn notifications on for a new report in the coming weeks
Let me know in the comments on any other subnets tackling lofty problems
0xSammy@0xSammy
Robotics requires massive multi-modal data & continuous fine-tuning Bittensor (TAO) provides a market for intelligence, letting robots pay for the data they need As part of our inaugural @KhalaResearch report I've mapped several subnets to where they 'could' plug into the machine economy This week, Jensen Huang alluded to bitcoin mining being stored as a currency, with striking similarities to the Bittensor economic model There's been several discussions around agentic/robotics task markets and auction models recently. This extract offers a glimpse of what they may spend their funds on, offering the opportunity to position accordingly Would a deeper dive into the TAO eco be useful for a deeper understanding of the imminent machine economy?
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UnD retweetledi
🚨 New from Yuma:
In our latest report, the Yuma team looks at the fundamental value drivers for Bittensor subnet tokens, their role as OpEx replacements, and a structured method to determine their value.
hubs.ly/Q03_LdKx0
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UnD retweetledi
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