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.
Articoli
+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.
Irregular Time in Tick Models: Continuous-Time Encodings vs. Plain Positional Embeddings
Sequence models fed tick data still assume regular spacing. Three ways to tell a Transformer when a tick actually happened — learnable-timescale continuous encoding, ODE-RNN latent state, and delta_t as a plain feature — and the ablation that decides between them.
Metodi di controllo sintetico per la valutazione delle strategie di trading
Questa serie ha valutato il percorso di selezione fino a un falso limite: il DSR valuta il vincitore, il PBO valuta la ricerca. Nessuno dei due tocca la confusione: la strategia che ha fatto soldi perché la volatilità è raddoppiata nella settimana in cui l'hai implementata. Il metodo di controllo sintetico crea un controfattuale ponderato da un pool di donatori di strumenti intatti e fornisce un criterio di falsificazione e un valore p placebo.
Punteggio delle previsioni probabilistiche: CRPS, calibrazione PIT e DeepAR
Come valutare onestamente una distribuzione predittiva: CRPS come regola di punteggio adeguata, l'istogramma PIT come diagnostica di calibrazione e campionamento DeepAR in GluonTS.
Reti neurali informate dalla fisica per i prezzi delle opzioni
Mettere le PDE dei prezzi Heston, a confine libero e con diffusione a salto in una perdita di rete neurale: il residuo del prezzo logaritmico, il trucco autogradante misto-parziale e ciò che deve ancora essere misurato.
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.