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August 12, 2026
#mathematics

Koopman Operators and DMD: Do Market Modes Survive Out-of-Sample?

Dynamic Mode Decomposition fits a linear operator to nonlinear market dynamics. The only question that matters: do the fitted modes persist from one window to the next, and does the rolling spectral radius lead realised volatility? Here is the measurement protocol and the code to run it.

#mathematics#Koopman#dynamical-systems
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August 11, 2026
#model-compression

Knowledge Distillation: Compressing Trading Models for Low-Latency Deployment

The blog's standing answer to the accuracy-vs-latency tension is a two-stage fast/slow split. Distillation is a different answer: train one small model to mimic the ensemble. The KD loss, temperature, born-again nets, early exits for a variable latency budget, and the distill-to-FPGA pipeline — plus the measurements that would decide whether it beats the two-stage split.

#model-compression#distillation#latency
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August 9, 2026
#microstructure

Proses Hawkes untuk Pemodelan Kedatangan Pesanan dan Peristiwa Pasar

Menyesuaikan proses titik yang menarik dengan rekaman perdagangan kripto nyata: dari mana tiga angka (mu, alfa, beta) berasal, cara memperkirakan rasio percabangan n, apakah kernel eksponensial bertahan dalam uji kesesuaian, dan berapa banyak n bergerak saat Anda mengubah jendela estimasi.

#microstructure#Hawkes-process#point-process
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August 7, 2026
#causal-inference

Toda-Yamamoto vs Granger yang Berbeda: Apakah Lead-Lag BTC Bertahan?

Kausalitas Granger pada harga kripto dilakukan dengan dua cara — perbedaan pengembalian dan level Toda-Yamamoto — dengan implementasi Wald yang benar, matriks kausalitas terkoreksi-N yang efektif, dan uji stabilitas bergulir untuk mengetahui apakah lag dapat diperdagangkan atau tidak.

#causal-inference#Granger-causality#lead-lag
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