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.
非參數價格建模的高斯過程
金融時間序列的核設計——母體粗糙度、局部週期成分、光譜混合——以及作為不需要驗證集的正則化器的邊際似然。加上在任何東西可以交易之前仍然需要衡量的誠實清單。
基於偏微分方程的金融建模的傅立葉神經算子
算子學習映射整個函數空間，而不是點——傅立葉神經算子如何在頻率空間中參數化 PDE 解算子，為選擇權定價購買什麼，以及它的哪些主張仍然需要在真實硬體上測量。
一整天的內容能勝過十分鐘的內容嗎？ Flash 注意力與序列長度問題
Flash Attention 讓 23,400 步驟的交易上下文無需計算——平鋪、線上 softmax 和 N^2 d^2 / M 的 IO 界限。更長的上下文是否使模型更好是一個單獨的問題，它必須被測量，而不是斷言。
整合方法：結合弱學習者以獲得穩健的 Alpha
本部落格未涵蓋有關交易整合的三件事：為什麼協方差項（而不是模型計數）在整合誤差中占主導地位，堆疊如何作為具有折疊元特徵的訊號組合層工作，以及跨多個訊號的周轉淨額是否真正降低了執行成本。