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
+702% on SOL. Where Is Jev in This Backtest?
We ran a trend strategy across 15 crypto pairs, checked costs and access to future candles, and tested Jev separately. Where did profits appear, and why can't we promise them in live trading yet?
XGBoost for Return Direction: Class Imbalance and Decision Thresholds
Return-direction classifiers are imbalanced problems, and 0.5 is the wrong decision threshold. Comparing scale_pos_weight, focal loss, and precision-constrained threshold optimization on crypto data — plus the engineering differences between XGBoost, LightGBM, and CatBoost.
Transfer Entropy: Which Way Does Information Flow Between Crypto Assets?
DCC-GARCH tells you when crypto dependence tightens. Transfer entropy tells you which way it points. A directed information-flow measure, its null calibration, and an honest account of what it does and does not add over average pairwise correlation.
Trade Classification When You Have No Side Flag: Tick, Quote, Lee-Ready, BVC
Reconstructing trade direction from OHLCV, bar history, or venues without an aggressor flag — the classic rules, the stale-quote problem, bulk volume classification, and how to measure them against crypto's free ground truth.
Unregelmäßige Zeit in Tick-Modellen: zeitkontinuierliche Codierungen vs. einfache Positionseinbettungen
Sequenzmodelle, die mit Tick-Daten gefüttert werden, gehen immer noch von regelmäßigen Abständen aus. Drei Möglichkeiten, einem Transformer mitzuteilen, wann tatsächlich ein Tick aufgetreten ist – kontinuierliche Codierung auf erlernbarer Zeitskala, latenter ODE-RNN-Zustand und delta_t als einfaches Merkmal – und die Ablation, die zwischen ihnen entscheidet.
Synthetische Kontrollmethoden zur Bewertung von Handelsstrategien
Diese Serie hat den Preis für den Auswahlweg falsch bewertet – DSR bewertet den Gewinner, PBO bewertet die Suche. Beides ist nicht verwirrend: die Strategie, die Geld verdiente, weil sich die Volatilität in der Woche, in der Sie sie einsetzten, verdoppelte. Die synthetische Kontrollmethode erstellt ein gewichtetes Kontrafaktum aus einem Spenderpool unberührter Instrumente und liefert Ihnen ein Falsifikationskriterium und einen Placebo-p-Wert.
Scoring Probabilistic Forecasts: CRPS, PIT Calibration, and DeepAR
How to evaluate a predictive distribution honestly — CRPS as a proper scoring rule, the PIT histogram as a calibration diagnostic, and DeepAR sampling in GluonTS.
Physikinformierte neuronale Netze für die Preisgestaltung von Optionen
Einsetzen der Heston-, Free-Boundary- und Jump-Diffusion-Pricing-PDEs in einen neuronalen Netzwerkverlust – das Log-Price-Residuum, der Mixed-Partial-Autograd-Trick und was noch gemessen werden muss.
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.