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NEX-02

Algorithmic & AI Trading

Execution algorithms, trading agents and the mechanics of ordering.

Recent

  1. Research noteBT-2026-0226

    A ranking loss and a profit threshold

    A convolutional model scores 0.6316 on area under the curve for a seven-day Bitcoin move — and the paper is more interesting for how it is judged.

    2 minSource: arXiv

  2. Research noteBT-2026-0225

    When going first is worth something

    Three authors work out when a constant-function market maker is at rest, and when the order of two traders decides who gets the better fill.

    2 minSource: arXiv

  3. Research noteBT-2026-0224

    When to move the range, and what it costs

    Five authors model concentrated liquidity provision as impulse control and learn the rebalancing policy, reporting a compressed lower tail rather than a higher mean.

    2 minSource: arXiv

  4. Research noteBT-2026-0223

    Quoting at several levels at once

    A reinforcement learning market maker places orders across the book rather than at the touch, and is tested against three kinds of counterparty.

    2 minSource: arXiv

  5. Research noteBT-2026-0222

    An agent that reads the chain and the news

    CryptoTrade pairs on-chain metrics with off-chain signals and reflects on its own past trades before deciding the next one.

    2 minSource: arXiv

  6. Research noteBT-2026-0221

    Two in a thousand graduate, eight times more with a channel

    A survival analysis of 832,941 token launches puts a number on something the market treats as folklore.

    2 minSource: arXiv

  7. Research noteBT-2026-0220

    Generating order books in ten solver steps

    A flow-matching model produces limit order book trajectories at a fraction of the sampling cost of diffusion, and transfers to instruments it never saw.

    2 minSource: arXiv

  8. BenchmarkBT-2026-0219

    Language models are poor traders and useful analysts

    Given execution authority they underperform. Given a research task they save hours. The distinction is not subtle.

    SpansAISECFIN

    10 min

  9. AnalysisBT-2026-0212

    The ordering supply chain professionalised

    Extraction moved from opportunistic scripts to an industry with tiers and contracts.

    SpansAISECFIN

    7 min

  10. Research noteBT-2026-0205

    Backtests on model-driven strategies leak more than usual

    The model was trained on the period you are testing. That is not a detail.

    SpansAISECFIN

    6 min

  11. Field reportBT-2026-0198

    Latency arbitrage moved to inference time

    When two desks run similar models, the edge is who answers first.

    SpansAISECFIN

    8 min