Eugen Soloviov
Trading-systems engineer
2017-жылдан бери ботторду куруп келе жаткан соода системаларынын инженери: биржалар аралык арбитраж (30га чейин аянтчага туташкан), спот жана фьючерстер боюнча коинтеграцияга негизделген жуптук арбитраж, скальпинг, жаңылыктарга жана маанайга негизделген стратегиялар, тренд алгоритмдери, ошондой эле портфелди башкаруу жана тең салмактоо алгоритмдери. Ошондой эле миллисекунддан төмөн буйрутма аткарууну, чоң маалымат кампаларын, бэктестинг кыймылдаткычтарын, AI агенттерин жана соода интерфейстерин (анын ичинде ачык булак profitmaker.cc) курат. Стек: JS/TS, Python, Rust/Zig/Go, DevOps, backend, frontend, архитектура.
Articles
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.
Jim Simons: From Differential Geometry to the Most Profitable Algo-Fund in History
Biography, math insights, and systematic secrets behind Renaissance Technologies and its elusive Medallion Fund.
Complex Manifolds in Algorithmic Trading: The Geometry of Financial Markets
Multidimensional surfaces that deform over time, and Renaissance-style pattern discovery in high-dimensional spaces
CCXT: How WebSocket Orderbook Methods Really Work
Detailed breakdown of CCXT WebSocket methods for orderbooks: watchOrderBook, watchBidsAsks, watchOrderBookForSymbols. Real tests on 75+ exchanges.