Research series
Three studies identify the forecastable parts of BTCUSDT before comparing compact and foundation-model systems.
Series summary
Three studies move from representation diagnostics to a pre-registered signal scan and then a walk-forward comparison of compact and foundation-model forecasters.
The arc
Three studies in sequence, each feeding the next: groundwork that redirected the objective from direction to scale, a scan that located where the signal lives, and the comparison staged on what it found. Each entry opens its full report.
Head-and-loss research on cached frozen Moirai-MoE embeddings of BTCUSDT, prompted by a production head that settled on a near-constant median. Its finding shapes everything after it: the predictable structure is scale rather than direction, and permutation controls place that skill in the causal volatility regime rather than in the backbone.
Read reportA pre-registered scan of the full BTCUSDT store asking where learnable signal exists at all. Verdict: the signal is distributional — excursion quantiles and volatility, not direction — strongest at fine timeframes and decaying with horizon. Its verdicts set the parent study's targets, scales, and horizons.
Read reportThe completed walk-forward comparison staged on the scan's verdicts: a two-million-parameter from-scratch multi-resolution encoder against frozen and LoRA-fine-tuned Chronos-T5 and Moirai-MoE lanes. The small scratch encoder is the quality-versus-cost frontier, and fine-tuning helps exactly in proportion to a frozen representation's deficit.
Read report