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.
ノンパラメトリック価格モデリングのためのガウス過程
財務時系列のカーネル設計 - マザーン粗さ、局所的な周期的組成、スペクトル混合 - および検証セットを必要としない正則化器としての周辺尤度。さらに、取引可能になる前にまだ測定する必要があるものの正直なリストも含まれています。
PDE ベースの財務モデリング用のフーリエ ニューラル演算子
演算子の学習は、点ではなく関数空間全体をマップします。フーリエニューラル演算子が周波数空間で偏微分方程式解演算子をどのようにパラメータ化するか、オプション価格設定のために何を購入するか、実際のハードウェアでの測定がまだ必要なクレームはどれかを示します。
1 日のコンテキストは 10 分間のコンテキストに勝りますか?フラッシュ アテンションとシーケンス長の問題
Flash アテンションは、タイリング、オンライン ソフトマックス、N^2 d^2 / M の IO 境界など、23,400 ステップの取引コンテキストを計算的に自由にします。コンテキストが長くなるとモデルがより良くなるかどうかは別の問題であり、それは主張するのではなく測定する必要があります。
アンサンブル手法: 弱い学習器を組み合わせて堅牢なアルファを実現する
アンサンブル取引に関するこのブログで取り上げていない 3 つのこと: モデル数ではなく共分散項がアンサンブル誤差の大半を占める理由、フォールド外のメタ特徴を備えたシグナル組み合わせレイヤーとしてスタッキングがどのように機能するか、多くのシグナルにわたるターンオーバーネッティングが実際に実行コストを削減するかどうかです。