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
The MEV supply chain: PBS, MEV-Boost, and who actually captures the value
MEV stopped being a lone bot and became an assembly line: searcher, builder, relay, proposer. A technical walk through proposer-builder separation, MEV-Boost's sealed-bid block auction, why the searcher's margin gets bid away, order-flow auctions as the new moat, and how Solana's Jito model differs.
On-chain arbitrage: atomic cycles, flash loans, and the auction you have to win
Finding an on-chain arbitrage cycle is the easy half. The hard half is winning the sealed-bid priority auction against every other searcher who found the same cycle. A technical breakdown of atomicity, flash loans, backrunning, and where arb profit actually survives — including non-atomic CEX-DEX.
Impermanent Loss and LVR: the real math of LP profitability
Impermanent loss, concentrated-liquidity leverage, and loss-versus-rebalancing derived from first principles. The closed forms, the σ²/8 result, per-block markout, and the exact fee-versus-volatility condition under which an LP actually beats holding.
Inside the slice: child-order tactics between your scheduler and the exchange
Almgren-Chriss and VWAP only set slice budgets — execution is won in the tactics layer. Escalation timers, maker-taker break-even math, amend vs cancel-replace queue semantics on Binance/OKX/CME, iceberg anti-signaling, and a Python per-slice state machine.
Slippage curves, not slippage constants: cost models that survive contact with live trading
Replace constant-bps slippage with size, volatility and liquidity-dependent cost curves: the square-root law, fitting curves from TCA fills or public data, regime stress multipliers, and why your strategy leaderboard reshuffles.
Smart Order Routing in Crypto: One Order, Twelve Venues, No NBBO
Why crypto SOR is harder than equities routing: no consolidated tape, phantom liquidity, prefunded capital. A convex routing optimization with math and Python, maker-aware tactics, and per-venue markout league tables from your own TCA.
The maker-taker decision: fee tiers, rebates, and the true cost of crossing the spread
Maker vs taker is an adverse-selection trade wearing a fee schedule. Break-even math via Glosten-Milgrom, real crypto fee tiers and token discounts, rebate mining history, queue-dependent rebate value, and modeling tiered fees in a backtest.
Implementation shortfall and DIY TCA: measuring what execution actually costs you
Perold's implementation shortfall turned into a working TCA pipeline for crypto bots: arrival-price decomposition, markout curves at t+1s/10s/60s, ~200 lines of Python over your own fills, and feeding the results back into your backtest cost model.
Liquidations on-chain: Aave and Compound mechanics, and the bot business around them
How Aave v3 and Compound liquidations actually work: health factor math, close factors, liquidation bonuses, Chainlink oracle triggers, and what it takes to run a flash-loan liquidation bot in 2026.
Uniswap v3 for Quants: Concentrated Liquidity and Tick Math from First Principles
How Uniswap v3 really works under the hood: virtual reserves, liquidity L, sqrtPriceX96, tick math, feeGrowthInside, and why an LP position is a short-volatility payoff. With exact formulas and a worked numeric example.
MEV anatomy: sandwiches, frontrunning, and the dark forest of the mempool
Every swap you send to a public RPC is a visible limit order nobody has to honor. A technical breakdown of how MEV extraction works: mempool ordering, sandwich math on Uniswap v2, PGAs, Flashbots, and trader defenses.
Fill simulation: the ladder from close-price fantasy to queue-aware reality
Five rungs of fill simulation fidelity — from close-price fills to probabilistic queue-position models. Partial fills as a state machine, limit-fill probability bounds as a PnL bracket, and a calibration loop against live fills.