Deep dives into AI trading, market analysis, and the future of DeFi.
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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.
TWAP, VWAP and POV are bets on a volume forecast. We dissect each scheduler's hidden assumptions, build crypto intraday volume curves, and run all three head-to-head on replayed L2 data.
Full derivation of the Almgren-Chriss optimal execution model: linear impact, the sinh/cosh trajectory, the efficient frontier, and working Python for calibrating eta, gamma, and sigma from Binance L2 and trade data.
A GARCH volatility forecast is only worth something if it improves a trading decision. We build a volatility-targeted crypto strategy, evaluate forecast quality honestly, and compare GARCH against realized-vol and EWMA baselines in a walk-forward backtest.
Crypto correlations are not constant — they spike toward 1 in every drawdown. DCC-GARCH models a time-varying correlation matrix, giving you dynamic hedge ratios for pairs trading and honest time-varying portfolio risk.
Plain GARCH(1,1) treats good and bad news the same and assumes Gaussian shocks. EGARCH, GJR-GARCH, and Student-t/skew-t innovations fix both — and give you honest VaR and Expected Shortfall for crypto.
How the GARCH(1,1) model captures volatility clustering in crypto, how to fit it by maximum likelihood with the arch library, and how to turn conditional-variance forecasts into position sizing and dynamic stops.