Deep dives into AI trading, market analysis, and the future of DeFi.
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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, 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.
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.
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.
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.
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.
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.