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
Look-Ahead Bias: How a One-Bar Mistake Manufactures a Sharpe of 15 From Pure Noise
A controlled study of the subtle look-ahead leaks that quietly inflate backtests. With zero real edge, a same-bar fill manufactures an annualized Sharpe of +14.8 out of pure noise; a one-bar indicator peek, +4.8. The taxonomy, the measured magnitudes, and how to detect each leak before it costs you money.
The Backtest Speed Ladder: 298x on a Laptop CPU, Identical PnL to the Last Trade
Five implementations of the same 80-combo parameter sweep, all verified to produce identical PnL: pandas rolling.apply takes 69.9 seconds, numpy 3.1, numba 2.0, parallel numba 0.23 — a measured 298x speedup on an Apple M2 Max with zero hardware changes, and still ~13x over a competent vectorized baseline. What each rung buys, why a GPU is not the missing piece, and where the real bottleneck in mass parameter search lives.
researcher: A Searchable Quant-Research Archive for Humans and AI Agents
How we built researcher.marketmaker.cc — a unified, full-text-searchable archive of quant research (arXiv papers, GitHub repos, quant blogs, TradingView Pine scripts) that humans browse and AI agents query over MCP.
algo-investor-skills: Claude Code Skills That Build a Scam-Proof Investor Proposal
A deep look at algo-investor-skills — a set of Claude Code skills that take an algotrading strategy from raw measured facts to an audited, honesty-forward investor proposal. Six composable skills, a financial-models engine, an independent-verification proof pack, and a mandatory skeptical-investor audit gate that never fabricates a number.
The Kelly Criterion for Strategies: How to Size Positions and Allocate Capital
A strategy with positive expected value can still blow up your account if you get the bet size wrong. We walk through the Kelly criterion from deriving the formula to a portfolio of strategies: why full Kelly is dangerous, how fractional Kelly captures 75% of the growth at half the volatility, and an interactive calculator that shows how the Kelly fraction moves return and risk.
Daily Stock Analysis: An AI System That Turns a Watchlist Into a Daily Decision Dashboard
A deep dive into daily_stock_analysis by ZhuLinsen — an open-source system that fetches market data across A-shares, HK, US, and more, runs technical and news analysis through an LLM, and pushes a structured 'decision dashboard' to your messenger every trading day. Architecture, data fallback, agent strategies, limitations.
Temporal Fusion Transformers for Multi-Horizon Portfolio Forecasting
How Google's Temporal Fusion Transformer brings interpretable multi-horizon forecasting to quantitative portfolio management, with attention-based variable selection, quantile outputs, and a worked pytorch-forecasting pipeline.
Conformal Prediction for Risk-Aware Position Sizing
Distribution-free prediction intervals with guaranteed coverage. We use split conformal, jackknife+, and adaptive conformal inference to calibrate trading risk and size positions without parametric assumptions.
Bid-Ask Spread Modeling and Prediction with Machine Learning
Decomposing and predicting bid-ask spreads with ML — from Roll's implicit estimator to gradient boosting and neural networks — with the units, leakage, and benchmarking pitfalls that bite in production.
DeepLOB: Deep Learning on Limit Order Books
How DeepLOB combines a CNN, an inception module, and an LSTM to predict mid-price moves from raw order book data — the architecture, the real FI-2010 numbers, and a working PyTorch reimplementation.
Inside Our House Algorithm: HRP + Long/Short + CVaR with Hull-White
A deep dive into Pipeline — the composite allocation algorithm we built on top of HRP. Hierarchical Risk Parity as the base, a long/short overlay driven by agent signals and confidence, and a final risk correction via CVaR with a Hull-White volatility adjustment. The full math from our spec, plus the actual Rust implementation.
12 Portfolio Optimization Algorithms, Compared: HRP, Black-Litterman, NCO and Beyond
One basket of crypto, twelve allocation algorithms, one honest comparison. We open-sourced a Rust portfolio optimizer that runs HRP, HERC, MVO, Black-Litterman, NCO, Entropy Pooling and more behind a single interface — here is how each one thinks and why no single winner exists.