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
Tiempo irregular en modelos de ticks: codificaciones de tiempo continuo versus incrustaciones posicionales simples
Los modelos de secuencia alimentados con datos de ticks aún suponen un espaciado regular. Tres formas de decirle a un Transformer cuándo ocurrió realmente un tic (codificación continua en escala de tiempo que se puede aprender, estado latente ODE-RNN y delta_t como característica simple) y la ablación que decide entre ellas.
Synthetic Control Methods for Evaluating Trading Strategies
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.
Puntuación de pronósticos probabilísticos: CRPS, calibración PIT y DeepAR
Cómo evaluar honestamente una distribución predictiva: CRPS como regla de puntuación adecuada, el histograma PIT como diagnóstico de calibración y muestreo DeepAR en GluonTS.
Redes neuronales basadas en la física para la fijación de precios de opciones
Poner las PDE de fijación de precios de Heston, de límite libre y de difusión de salto en una pérdida de red neuronal: el residuo del precio logarítmico, el truco de la autograduación parcial mixta y lo que aún necesita medirse.
PCMCI: Causal Discovery in Multivariate Crypto Time Series
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.
Order Flow Imbalance: The Cont-Kukanov-Stoikov Event Decomposition
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: Does Continuous Time Beat a Delta-Time Feature?
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.
Multi-Task Learning for Simultaneous Price, Volume, and Volatility Prediction
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.
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.