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?
用於報酬方向的 XGBoost：類別不平衡與決策閾值
報酬方向分類器是不平衡問題，而 0.5 是錯誤的決策閾值。比較加密貨幣資料上的 scale_pos_weight、焦點損失與精確率約束閾值最佳化，以及 XGBoost、LightGBM 和 CatBoost 之間的工程差異。
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.
沒有買賣方標記時的交易分類：Tick、Quote、Lee-Ready、BVC
從 OHLCV、棒歷史資料或沒有攻擊方標記的交易場所重建交易方向——經典規則、過時報價問題、批量成交量分類，以及如何用加密市場免費提供的真實標籤測量它們。
逐筆資料模型中的不規則時間：連續時間編碼與純位置嵌入
餵入逐筆資料的序列模型仍假設間隔規則。本文介紹三種告訴 Transformer 某筆成交實際發生時間的方法——可學習時間尺度的連續編碼、ODE-RNN 潛在狀態，以及把 delta_t 作為一般特徵——並以消融實驗決定它們之間的取捨。
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.
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.
Physics-Informed Neural Networks for Options Pricing
Putting the Heston, free-boundary, and jump-diffusion pricing PDEs into a neural network loss — the log-price residual, the mixed-partial autograd trick, and what still needs measuring.
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.