Deep dives into AI trading, market analysis, and the future of DeFi.
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How adaptive data granularity speeds up backtests and saves storage: drill-down from 1m to 1s, 100ms, and raw trades only where price moved significantly or volume spiked, not across the entire historical series.
How to precompute timeframes and indicators from minute candles, save them to parquet, and use them for mass strategy testing without redundant recalculations.
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.