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수익률 방향 분류는 불균형 문제이며 0.5는 잘못된 의사결정 임계값이다. 암호화폐 데이터에서 scale_pos_weight, 초점 손실, 정밀도 제약 임계값 최적화를 비교하고 XGBoost, LightGBM, CatBoost의 엔지니어링 차이도 살펴본다.
DCC-GARCH는 암호화폐 의존성이 언제 강화되는지 알려줍니다. 전이 엔트로피는 그 방향을 알려줍니다. 방향성 정보 흐름의 측정법과 귀무 보정, 평균 쌍별 상관관계에 비해 무엇을 더하고 더하지 못하는지에 대한 정직한 설명입니다.
OHLCV, 바 기록 또는 공격자 플래그가 없는 거래소에서 거래 방향을 재구성하는 방법 — 고전적 규칙, 오래된 호가 문제, 대량 거래량 분류, 그리고 암호화폐가 무료로 제공하는 실제 정답과 비교해 측정하는 방법을 다룬다.
Sequence models fed tick data still assume regular spacing. Three ways to tell a Transformer when a tick actually happened — learnable-timescale continuous encoding, ODE-RNN latent state, and delta_t as a plain feature — and the ablation that decides between them.
This series has priced the selection route to a false edge — DSR prices the winner, PBO prices the search. Neither touches confounding: the strategy that made money because volatility doubled the week you deployed it. The Synthetic Control Method builds a weighted counterfactual from a donor pool of untouched instruments and gives you a falsification criterion and a placebo p-value.
How to evaluate a predictive distribution honestly — CRPS as a proper scoring rule, the PIT histogram as a calibration diagnostic, and DeepAR sampling in GluonTS.
Putting the Heston, free-boundary, and jump-diffusion pricing PDEs into a neural network loss — the log-price residual, the mixed-partial autograd trick, and what still needs measuring.
How PCMCI's two-stage MCI test recovers directed causal links between crypto assets where correlation and bivariate Granger cannot — the construction, the tigramite pipeline, and the real-data study it still needs.
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.
