AIトレーディング、市場分析、そしてDeFiの未来への深い洞察。
How understanding your place in the queue at a price level transforms scalping from guesswork into an engineering problem
Why raw annual PnL is a poor metric for comparing strategies with different trading time. How to calculate effective return, why you need fill_efficiency, and why a strategy with 27% PnL can outperform one with 300%.
Every algorithm leaves a unique fingerprint. Learn to read it — and you will know who is on the other side of your trade.
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.