Diepgaande verkenningen van AI-handel, marktanalyse en de toekomst van DeFi.
Browse collections →Nothing found. Try a different query.
Door de PDE's van Heston, free-boundary en jump-diffusion pricing in een neuraal netwerkverlies te stoppen - het log-prijsresidu, de mixed-partial autograd-truc, en wat nog moet worden gemeten.
Hoe de tweetraps MCI-test van PCMCI gerichte causale verbanden tussen crypto-activa herstelt waar correlatie en bivariate Granger dat niet kunnen: de constructie, de tigramietpijplijn en het onderzoek naar echte gegevens die het nog steeds nodig heeft.
Turning raw book updates into a signed flow quantity: the CKS event decomposition, multi-level OFI with PCA reduction, and Lee-Ready trade classification — plus an honest accounting of what the headline R-squared actually measures.
Neural ODEs, Neural SDEs and continuous normalizing flows for irregularly-sampled market data — and the one ablation that decides whether continuous dynamics are worth their solver cost.
Does jointly predicting return, volume, and volatility actually help? Measuring loss-balancing schemes and diagnosing negative transfer through gradient cosine similarity — with a classical baseline and purged walk-forward folds.
Magnitude and structured pruning, the Lottery Ticket Hypothesis, movement pruning, distillation and 2:4 sparsity — the methods behind shrinking a trading model, and what still has to be measured before any of it ships.
Our VWAP article shipped a static, weekly-refit volume curve and called the forecaster the weakest link. This is the follow-up: a Bayesian intraday updater run against the same 500-parent BTCUSDT harness, with the IS delta conditioned on realized curve error.