This tutorial covers the five core steps of AI quantitative trading from scratch: data collection & cleaning, strategy design, backtesting, out-of-sample (OOS) validation, and deployment & monitoring. It explains three key advantages of AI over manual trading - emotional stability, processing speed, and consistency - and shares key techniques to avoid the backtest-to-live trading gap.
From an RSI idea to real money trading—where does AI actually speed things up, and where is it completely useless? Sharing our 5-line framework’s actual development journey and psychological hurdles.
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.
A paper trading strategy with 87.5% apparent win rate fails statistical validation—Z-score yields p=0.24, no significant difference from coin flipping. Using Bayesian adjustment and Overfitting Index (OFI) with 33 real trades to establish a strategy validation logic that avoids the small-sample high-win-rate trap.
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.