Deep dives into AI trading, market analysis, and the future of DeFi.
Why a single train/test split does not protect against overfitting, how walk-forward optimization systematically verifies parameter robustness, and why a strategy with +3342% PnL@ML on 21 parameters is a ticking time bomb without WFO.
Why 10 crypto pairs don't provide 10x diversification, how to calculate effective_N via correlation_factor, and how many pairs you really need to monitor for 80-90% orchestrator slot utilization.
Detailed comparison of Polars and Pandas on algotrading tasks: benchmarks for filtering, aggregation, rolling signal computations, I/O, and memory consumption. Hybrid Polars + Numba architecture for maximum backtest performance.
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.