Eugen Soloviov
Trading-systems engineer
Ingeniero de sistemas de trading que construye bots desde 2017: arbitraje entre exchanges (conectado hasta 30 plataformas), arbitraje de pares basado en cointegración entre spot y futuros, scalping, estrategias basadas en noticias y sentimiento, algoritmos de tendencia y algoritmos de gestión y balanceo de carteras. También construye ejecución de órdenes en submilisegundos, almacenes de big data, motores de backtesting, agentes de IA e interfaces de trading (incluido el open-source profitmaker.cc). Stack: JS/TS, Python, Rust/Zig/Go, DevOps, backend, frontend, arquitectura.
Articles
Backtest-live parity: why your bot trades differently from the backtest
Complete taxonomy of divergences between backtesting and live trading: from slippage and partial fills to codebase desynchronization. Architectural patterns for achieving parity, Python examples of a shared core module, and a production monitoring checklist.
Monte Carlo Bootstrap: How to Get Confidence Intervals for a Backtest in 10 Lines of Code
Why a single-point estimate from a backtest is a dangerous illusion. How Monte Carlo bootstrap in 2 seconds of computation gives you a 95% confidence interval for PnL and MaxDD, and why this is a mandatory step before launching a strategy in production.
Funding Rate Arbitrage Across Exchanges: How to Profit from Rate Differences
How funding rate arbitrage works across crypto exchanges, why rates differ on Binance, Bybit, OKX and dYdX, and how to build a monitoring and execution system to extract profit from these discrepancies.
QuestDB for Algorithmic Trading: SQL Extensions That Change the Game
Deep dive into QuestDB's time-series SQL extensions: SAMPLE BY, ASOF JOIN, HORIZON JOIN, WINDOW JOIN, LATEST ON, and real-world trading query patterns.
QuestDB for Algorithmic Trading: From Order Books to Production Architecture
Materialized views, 2D array order book analytics, and reference architecture for a QuestDB-powered algorithmic trading platform.
QuestDB for Algorithmic Trading: Architecture That Speaks the Language of Markets
Deep dive into QuestDB's three-tier storage architecture — WAL, columnar storage, and Parquet on object storage — and schema design principles for algorithmic trading systems.
Data Communication in Algo Trading Systems: A Technology Overview
We analyze communication technologies at all levels of an algorithmic trading platform: from exchange connectivity protocols (REST, WebSocket, FIX) to internal IPC, message brokers, and data stores.
Loss-Profit Asymmetry: The Math That Kills Your Deposit
Why losing 50% requires 100% growth to recover, how volatility drag destroys capital even in sideways markets, and which formulas every algo trader must know for building risk management.
Complex Arbitrage Execution in Rust: From Nanoseconds to Atomic Multi-Legs
How to squeeze maximum performance out of Rust for multi-leg arbitrage execution: io_uring, lock-free order books, LMAX Disruptor, SIMD, type-state machines, and arena allocators.
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.