게시물
AI 에이전트 연구, 시장 예측의 백테스트와 감사, 플랫폼 아키텍처.
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The 20 biggest AI and chip stocks lost over $1 trillion in one week. Hedge funds dumped AI for five sessions straight while retail poured a record $3.9B into 3x chip ETFs.
5분 분량섹션 6개
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MEV is one of the few places where machine learning has a defensible job, because the task is simulation and classification rather than price prediction. But private order flow makes every public-data study a lower bound of unknown tightness.
6분 분량섹션 6개
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Most depegs that matter are plumbing wearing the costume of solvency. What machine learning can genuinely do with stablecoin data, why depeg prediction breaks on rare events, and how supply works as a regime descriptor rather than a signal.
6분 분량섹션 6개
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Funding rates and open interest describe how levered a market is and what that position costs to hold. They forecast the size of coming moves, not the direction - and the data is full of traps that quietly wreck studies. What AI can honestly extract.
6분 분량섹션 6개
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On-chain data is the only complete dataset in markets - no survivorship bias, no vendor filter. It is also built on unvalidated address labels and a handful of independent cycles. What AI can honestly do with a blockchain, and what it cannot.
7분 분량섹션 7개
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Direction is the AI forecast everyone wants and a liquid market defeats. How far an asset can move is the one machine learning can make - and prove. Why sigma-root-t is wrong in both directions, and what coverage really tests.
7분 분량섹션 7개
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Everyone chases AI that predicts price. The place machine learning genuinely earns its keep is risk management — volatility, position sizing, drawdown, liquidation. Measurable, unglamorous, honest.
9분 분량섹션 7개
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Retail sees price and volume. Machine learning reads liquidity — depth, order-flow imbalance, market impact, hidden orders — the short-lived information beneath a single price. And its honest limit.
9분 분량섹션 7개
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Measured over the full Binance history: the root-t band is 20% too wide at one day, converges by thirty, and crosses over for ETH. Plus the overlapping-window correction almost everyone skips.
7분 분량섹션 7개
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Why Ethereum is a different forecasting problem from Bitcoin — staking queues, fee burn, rollup migration, and the beta trap that makes weak ETH models look skilful.
9분 분량섹션 7개
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Where AI genuinely changes a crypto exchange: surveillance, risk engines, liquidity, AML, matching-engine ops — and where the label is mostly marketing.
9분 분량섹션 7개
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What AI can honestly do for Bitcoin price prediction, what is theatre, and why a timestamped, hashed, publicly scored forecast is the only real evidence.
7분 분량섹션 6개
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How AI detects crypto volatility regimes using realized volatility, clustering, and time-series models — and why the honest output is uncertainty, not price targets.
6분 분량섹션 7개
