Eugen Soloviov
Trading-systems engineer
Trading-systems engineer building bots since 2017: cross-exchange arbitrage (connected up to 30 venues), cointegration-based pairs arbitrage across spot and futures, scalping, news and sentiment-driven strategies, trend algorithms, and portfolio management and balancing algorithms. Also builds sub-millisecond order execution, big-data warehouses, backtesting engines, AI agents, and trading interfaces (incl. open-source profitmaker.cc). Stack: JS/TS, Python, Rust/Zig/Go, DevOps, backend, frontend, architecture.
Articles
Model Pruning for Low-Latency Trading Inference
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.
Updating the Volume Curve Intraday: Does Adaptive Forecasting Actually Help?
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.
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.
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.
The Other Way a Regression Lies: Endogeneity, 2SLS, and the Gamma Calibration Problem
Selection bias in the search is not the only way a regression fools you. When the regressor is correlated with the error, more data makes the estimate more confidently wrong. Instrumental variables applied to the one endogeneity problem this blog has already left open: permanent impact from net taker flow.
Hawkes Processes for Order Arrival and Market Event Modeling
Fitting a self-exciting point process to real crypto trade tape: where the three numbers (mu, alpha, beta) come from, how to estimate the branching ratio n, whether the exponential kernel survives a goodness-of-fit test, and how much n moves when you change the estimation window.
Hamiltonian Neural Networks: Does a Financial System Conserve Anything?
Hamiltonian Neural Networks are provably stable — but stability is worthless if the conserved quantity does not exist. Learning a scalar H via autograd, symplectic integration, and the falsification test that decides whether a financial (q, p) pair is canonical at all.
Toda-Yamamoto vs Differenced Granger: Does the BTC Lead-Lag Survive?
Granger causality on crypto prices done two ways — differenced returns and Toda-Yamamoto on levels — with a correct Wald implementation, an effective-N corrected causality matrix, and a rolling-stability test of whether the lag is tradeable at all.
Parametrik bo'lmagan narxlarni modellashtirish uchun Gauss jarayonlari
Moliyaviy vaqt seriyalari uchun yadro dizayni - onaning pürüzlülüğü, mahalliy davriy tarkibi, spektral aralashmalar - va hech qanday tekshirish to'plamiga muhtoj bo'lmagan tartibga soluvchi sifatida chekli ehtimollik. Bundan tashqari, biron bir sotilishidan oldin o'lchanishi kerak bo'lgan narsalarning halol ro'yxati.
PDE-ga asoslangan moliyaviy modellashtirish uchun Furye neyron operatori
Operatorni o'rganish nuqtalarni emas, balki butun funktsiya bo'shliqlarini xaritalaydi - Furye Neyron Operatori chastotalar bo'shlig'ida PDE yechim operatorini qanday parametrlashtirgani, opsion narxlari uchun nimani sotib olishi va uning da'volaridan qaysi biri hali ham haqiqiy uskunada o'lchashga muhtoj.
To'liq kunlik kontekst o'n daqiqani uradimi? Diqqat va ketma-ketlik bo'yicha savol
Flash Attention 23 400 bosqichli savdo kontekstini hisoblash uchun bepul qiladi — plitka qo‘yish, onlayn softmax va IO chegarasi N^2 d^2/M. Bu uzoqroq kontekst modelni yaxshiroq qiladimi yoki yo‘qmi, bu alohida savol va uni tasdiqlash emas, o‘lchash kerak.
Ansambl usullari: mustahkam alfa uchun zaif o'quvchilarni birlashtirish
Savdo ansambllari haqida uchta narsa bu blogda yoritilmagan: nega modellar soni emas, kovariatsiya atamasi ansambl xatosida ustunlik qiladi, stacking oʻta meta-xususiyatlarga ega boʻlgan signal-kombinatsiya qatlami sifatida qanday ishlaydi va koʻplab signallar boʻylab aylanma tarmogʻi amalda bajarish narxini pasaytiradimi yoki yoʻqmi.