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NeuPortal Quant Lab

AIエージェントの研究、市場予測のバックテストと監査、プラットフォームのアーキテクチャ。

すべての記事 · 132

  1. AIビジネス

    AI Agents vs Chatbots: What Changes When Software Can Act

    An AI agent runs a loop - observe, decide, act - and a chatbot does not. What that difference demands in scope, records and stop controls, where agents genuinely win, and four questions that evaluate one faster than any demo.

    読了時間 5分6セクション

  2. モデルの落とし穴

    Why AI Models Break When Conditions Change, and How to Notice Early

    Regime shift is the most common way machine learning fails in production, and backtests average it away. A worked example with real numbers from our own board, and four habits that catch it before the results do.

    読了時間 5分6セクション

  3. AIビジネス

    How AI Actually Changes Your Finances, and How It Does Not

    For most people AI changes the cost side of a household long before the income side. What genuinely moves - fees, comparison, gated expertise - what it puts at risk, and why "let AI trade for you" is the weakest version of the claim.

    読了時間 5分6セクション

  4. AIビジネス

    Can AI Make Life Simpler? One Test That Sorts Most Cases

    AI removes steps for tasks where being wrong is cheap and visible, and adds a verification tax where being wrong is expensive and invisible. A single question sorts almost every case correctly.

    読了時間 5分7セクション

  5. 暗号資産と市場

    AI and MEV: What Machine Learning Can and Cannot See in the Mempool

    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.

    読了時間 6分6セクション

  6. 暗号資産と市場

    AI and Stablecoins: What the Peg Actually Tells You

    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.

    読了時間 6分6セクション

  7. 暗号資産と市場

    AI and Crypto Derivatives: Reading Funding Rates and Open Interest

    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.

    読了時間 6分6セクション

  8. 暗号資産と市場

    AI and On-Chain Analysis: What the Blockchain Really Tells You

    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.

    読了時間 7分7セクション

  9. 予測の採点

    AI and Calibration: Why a Good 70% Forecast Is Wrong Three Times in Ten

    The instinct to grade a forecast by whether it came true is the wrong test. Calibration - when a model says 70% it happens about 70% of the time - is the property that separates a real forecaster from a confident guesser, and it is measurable.

    読了時間 7分7セクション

  10. 暗号資産と市場

    AI and Volatility: Forecasting How Much a Market Moves, Not Which Way

    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.

    読了時間 7分7セクション

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