Deep dives into AI trading, market analysis, and the future of DeFi.
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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.
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.
Complete taxonomy of divergences between backtesting and live trading: from slippage and partial fills to codebase desynchronization. Architectural patterns for achieving parity, Python examples of a shared core module, and a production monitoring checklist.
Why a single-point estimate from a backtest is a dangerous illusion. How Monte Carlo bootstrap in 2 seconds of computation gives you a 95% confidence interval for PnL and MaxDD, and why this is a mandatory step before launching a strategy in production.
How funding rate arbitrage works across crypto exchanges, why rates differ on Binance, Bybit, OKX and dYdX, and how to build a monitoring and execution system to extract profit from these discrepancies.
Deep dive into QuestDB's time-series SQL extensions: SAMPLE BY, ASOF JOIN, HORIZON JOIN, WINDOW JOIN, LATEST ON, and real-world trading query patterns.
Materialized views, 2D array order book analytics, and reference architecture for a QuestDB-powered algorithmic trading platform.