Backtesting

A step-by-step guide to AI quantitative trading covering five core building processes: data collection & cleaning, strategy design, backtesting, out-of-sample validation (OOS), and deployment & monitoring. Explains three key advantages of AI over manual trading - emotional stability, processing speed, and consistency - and shares essential techniques to bridge the gap between backtesting and live performance.

From a raw RSI idea to real-money live trading — where AI genuinely accelerated our workflow, and where it hit a wall completely. A look at the real development process and mental hurdles behind our 5-line architecture.

87% annualized returns in backtesting? Congrats, but live trading is a completely different world. This article documents our quant system’s first month of real trading and the things backtests will never tell you.

The Numbers Lie Our paper trading system ran for a month, and the numbers looked beautiful on a spreadsheet: 87.5% win rate, 7 wins, 1 loss, 2 breaks even When most people see those numbers, their reaction is: “Awesome! Time to trade real money!” Our reaction was: “Hold on. Let the math speak first.” What is Z-Score? At its core, Z-score is asking one simple question: How much better are you than just flipping a coin? ...

We developed a mean reversion strategy. Backtesting showed 3 out of 8 combinations hitting 100% win rate. Then we ran Out-of-Sample validation, and 100% crashed to 25%. Here’s what happened.

Why single-strategy systems are doomed to fail, how our four-strategy system auto-switches based on market regime, and why WFO validation is the quality gate you can’t skip.

We spent two weeks building a complete quantitative trading system from scratch — four strategies, eight Walk-Forward validations, Z-score statistical tests, Paper Trading. This article documents the entire process, including the biggest pitfalls we encountered.