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
Queue Inside the Wall: Analyzing Order Position in Order Book Density
How understanding your place in the queue at a price level transforms scalping from guesswork into an engineering problem
LLM Alpha Mining: daromad bo'yicha qo'ng'iroqlar va moliyaviy hujjatlardan treyding signallarini qanday olish mumkin
Investorlar bilan suhbatlar, hisobotlar va yangiliklardan treyding signallarini olish uchun katta til modellaridan qanday foydalanish mumkin. Chain-of-thought promptlash, tuzilgan ekstraksiya, signallarni bektestlash.
Statistical Arbitrage and Pairs Trading in Crypto Markets: From Cointegration to the Kalman Filter
A complete guide to statistical arbitrage for crypto markets. Cointegration, Kalman filter, basis strategies, cross-exchange arbitrage. With backtests and Python code.
Treyderning raqamli barmoq izi: bozor yaratuvchisini order book xatti-harakati orqali qanday aniqlash mumkin
Har bir algoritm o'ziga xos barmoq izini qoldiradi. Uni o'qishni o'rganing — va savdongizning narigi tomonida kim turganini bilib olasiz.
Faol vaqt bo'yicha PnL: strategiyalar reytingini o'zgartiradigan ko'rsatkich
Nima uchun xom yillik PnL turli savdo vaqtiga ega strategiyalarni solishtirish uchun yomon ko'rsatkich hisoblanadi. Samarali daromadni qanday hisoblash kerak, nega fill_efficiency zarur va nega 27% PnL bilan strategiya 300% bilan strategiyadan ustun bo'lishi mumkin.
Adaptiv Drill-Down: Daqiqalardan xom savdolargacha o'zgaruvchan granulyarlikda backtest
Adaptiv ma'lumotlar granulyarligi backtestlarni qanday tezlashtiradi va saqlash joyini qanday tejaydi: 1m dan 1s, 100ms va xom savdolargacha drill-down faqat narx sezilarli o'zgargan yoki hajm keskin oshgan joyda, butun tarixiy qator bo'ylab emas.
Yig'ilgan Parquet Kesh: Ko'p Vaqt Oralig'idagi Backtestlarni Yuzlab Marta Tezlashtirish
Daqiqalik sham (candle) ma'lumotlaridan vaqt oraliqlari va indikatorlarni oldindan qanday hisoblash, ularni parquet formatida saqlash va ortiqcha qayta hisoblashlarsiz strategiyalarni ommaviy sinovdan o'tkazish uchun qanday foydalanish.
Walk-Forward Optimization: The Only Honest Strategy Test
Why a single train/test split does not protect against overfitting, how walk-forward optimization systematically verifies parameter robustness, and why a strategy with +3342% PnL@ML on 21 parameters is a ticking time bomb without WFO.
Signal Correlation: How Many Pairs to Monitor
Why 10 crypto pairs don't provide 10x diversification, how to calculate effective_N via correlation_factor, and how many pairs you really need to monitor for 80-90% orchestrator slot utilization.
Polars vs Pandas for Algotrading: Benchmarks on Real Data
Detailed comparison of Polars and Pandas on algotrading tasks: benchmarks for filtering, aggregation, rolling signal computations, I/O, and memory consumption. Hybrid Polars + Numba architecture for maximum backtest performance.
Plato tahlili: barqaror optimumni overfitting-dan qanday ajratish kerak
Strategiyaning eng yaxshi parametrlarini topish nega ishning faqat yarmi. Barqaror platoni notinch cho'qqidan vizual va miqdoriy jihatdan qanday ajratish kerak, va optimallashtirilgan strategiyani productionga joriy etishdan oldin Optuna contour plot-lari nega majburiy qadam.
Koordinatali tushish va Bayes optimallashtirishi: qaysi biri yaxshiroq parametrlarni topadi
12+ parametr uchun to'liq qidiruv nima uchun mumkin emas, koordinatali tushish o'zaro ta'sirlarni qanday o'tkazib yuboradi va TPE sampleri bilan Optuna 500 iteratsiyada OAT 96 tada topa olmaydigan narsani qanday topadi. Amaliy kod misollari, samplerlarni solishtirish va ko'p maqsadli optimallashtirish.