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