Pembahasan mendalam tentang AI trading, analisis pasar, dan masa depan DeFi.
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How understanding your place in the queue at a price level transforms scalping from guesswork into an engineering problem
How to use large language models to extract trading signals from investor calls, reports, and news. Chain-of-thought prompting, structured extraction, signal backtesting.
A complete guide to statistical arbitrage for crypto markets. Cointegration, Kalman filter, basis strategies, cross-exchange arbitrage. With backtests and Python code.
Every algorithm leaves a unique fingerprint. Learn to read it — and you will know who is on the other side of your trade.
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%.
Bagaimana granularitas data adaptif mempercepat backtest dan menghemat penyimpanan: drill-down dari 1m ke 1s, 100ms, dan trade mentah hanya di tempat harga bergerak signifikan atau volume melonjak, bukan di sepanjang seluruh deret historis.
How to precompute timeframes and indicators from minute candles, save them to parquet, and use them for mass strategy testing without redundant recalculations.