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How to identify the current market regime (bull, bear, sideways) using Hidden Markov Models and automatically switch trading strategies. With Python code and backtests.
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%.
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.