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
GNN, Transformers, and RL for Arbitrage: When Neural Networks Learn to Trade
How graph neural networks find arbitrage chains in 78 ms, why RL agents show 142% annual returns against 12% for rule-based bots, and how to build an integrated system in Rust.
Matrices, Tensors, and Tropical Algebra: Linear Algebra for Arbitrage Detection
How the matrix of exchange rates, eigenvalues, tropical algebra, and tensor decompositions turn cryptocurrency market chaos into clear arbitrage signals.
Vine Copulas for Arbitrage: Modeling High-Dimensional Dependencies
How to use Vine Copulas to identify hidden dependencies between dozens of crypto assets and build robust, high-dimensional statistical arbitrage strategies.
Futures-Spot Arbitrage: From Cash-and-Carry to DeFi-CeFi
How funding rates, basis, and the convergence of decentralized and centralized markets create risk-free opportunities for capital in the crypto market.
Graph Algorithms for Arbitrage Detection: From Bellman-Ford to RICH
How negative cycles, multi-asset graphs, and the RICH algorithm identify arbitrage opportunities in the deep cryptocurrency market with sub-millisecond precision.
The Black-Scholes Formula: Option Mathematics and the Holy Grail of Trading
Breaking down the most famous formula in finance. How a heat equation from physics enabled option pricing and changed Wall Street forever, with Python examples.
Anomaly Detection for Trading Bot Protection: From Z-Score to Transformer
Which anomaly detection methods actually work in crypto algo trading, how to build a cascading protection architecture, and why this is the foundation without which algo trading becomes gambling.
Markowitz Portfolio Theory for Crypto: From Zero to Hero
Building optimal crypto portfolios with Python - because YOLO isn't a strategy. Learn how to apply Nobel Prize-winning portfolio theory to crypto investments with practical Python code examples.
From Turbulence to Trading: How Navier-Stokes Equations Revolutionize Algorithmic Trading
Part 2. Practical application of hydrodynamics in algorithmic trading. How quantum hedge funds use fluid physics to predict markets, model liquidity, and manage risks.
The Navier-Stokes Problem: Why Your Coffee Cup Could Run Doom
How fluid dynamics equations became one of mathematics' greatest unsolved problems. Turing-completeness of turbulence, Clay Institute's million-dollar prize, and why AI still can't predict your morning coffee.
Trading Signals: Why It Doesn't Work the Way You Think
Breaking down the mechanics of trading signals and copy trading: why these tools more often enrich their creators rather than subscribers.
Diffusion Models vs Cryptocurrency Anarchy: Why DDPM Can Predict Bitcoin Crashes Better Than Your Astrologist
How diffusion models are revolutionizing cryptocurrency forecasting, overcoming volatility chaos and creating new opportunities for algorithmic trading.