Algorithmic-Trading

Why Your ML Trading Model Passes 442 Tests But Still Can’t Beat a Coin Flip TL;DR A developer built a complete Lopez de Prado–style machine learning pipeline in Rust — 442 tests passing, zero bugs — and still ended up with an out-of-sample AUC of 0.50, which is mathematically equivalent to random guessing. The post sparked 43 community replies on r/algotrading, suggesting this is a painfully familiar experience for quant developers. If you’ve ever wondered why a technically flawless ML pipeline produces useless trading signals, this one’s for you. The short answer: correctness and predictiveness are two completely different problems. ...

How One Algo Trader Built a Macro SPX Trading System Using Entirely Free Data TL;DR A developer on r/algotrading shared the architecture of a macro trading system for S&P 500 (SPX) built entirely on free, publicly available data sources. The system pulls from six major free APIs and data providers — including FRED, BLS, CBOE, and AAII — to construct a multi-signal macro view of the market. There’s no Bloomberg terminal, no expensive data subscription, and no proprietary feed required. If you’ve ever wondered whether institutional-grade macro signals are accessible to retail algo traders, this build suggests the answer is increasingly yes. ...