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
Digital Fingerprint of a Trader: How to Identify a Market Maker by Their Order Book Behavior
Every algorithm leaves a unique fingerprint. Learn to read it — and you will know who is on the other side of your trade.
PnL by Active Time: The Metric That Changes Strategy Rankings
Why raw annual PnL is a poor metric for comparing strategies with different trading time. How to calculate effective return, why you need fill_efficiency, and why a strategy with 27% PnL can outperform one with 300%.
Adaptive Drill-Down: Backtest with Variable Granularity from Minutes to Raw Trades
How adaptive data granularity speeds up backtests and saves storage: drill-down from 1m to 1s, 100ms, and raw trades only where price moved significantly or volume spiked, not across the entire historical series.
Aggregated Parquet Cache: How to Speed Up Multi-Timeframe Backtests by Hundreds of Times
How to precompute timeframes and indicators from minute candles, save them to parquet, and use them for mass strategy testing without redundant recalculations.
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.
Plateau Analysis: How to Distinguish a Robust Optimum from Overfitting
Why finding the best strategy parameters is only half the work. How to visually and quantitatively distinguish a stable plateau from a fragile peak, and why Optuna contour plots are a mandatory step before launching an optimized strategy into production.
Coordinate Descent vs Bayesian Optimization: Which Finds Better Parameters
Why exhaustive search is impossible for 12+ parameters, how coordinate descent misses interactions, and how Optuna with a TPE sampler finds in 500 iterations what OAT cannot find in 96. Practical code examples, sampler comparison, and multi-objective optimization.
Multi-Symbol Validation: Test Your Strategy on All Pairs
Why a strategy optimized on ETHUSDT may fail on altcoins. How to properly test across pair groups (blue chips, large caps, shitcoins) and what cross-symbol robustness score to consider sufficient.
Funding Rates Kill Your Leverage: Why PnL×50x Is a Fiction
How funding rates on Binance/Bybit turn beautiful high-leverage backtest results into guaranteed losses. Formulas, recalculation of real strategies, and the maximum leverage at which funding does not eat into profits.
Cascade Strategies: Priority Execution with Fallback Filling
Finale of the 'Backtests Without Illusions' series. How to build an orchestrator from N strategies x M pairs, implement cascade mode with priority and fallback filling, choose dual_size, and why strategy portfolios cannot be backtested by summing PnL.