Backtesting and Validation
A backtest applies a strategy's rules to historical data to see how it would have performed. It is a useful tool and an easy one to misuse. This section explains how to build a backtest that reflects real trading conditions, including transaction costs, slippage, and the occasional missing tick, and how to recognize when a backtest is telling you more about the past than about the future. We also cover walk-forward testing, out-of-sample validation, and the specific ways overfitting sneaks into a system that looks great on a chart and falls apart in live conditions. Performance measurement lives here too, since a backtest without the right metrics can hide as much as it reveals.
Related: Trading Risk Management · Trading Platforms and Software
Algorithmic Trading Backtesting: How to Test a Strategy Without Fooling Yourself
Why most backtests lie to their creators, and the checks that catch it before real money is on the line.
Read more →Overfitting: Why a Perfect Backtest Should Worry You
The warning signs that a strategy was fit to noise, not to a pattern.
Read more →Walk-Forward Testing Explained
A step-by-step look at validating a strategy on data it hasn't seen.
Read more →Performance Metrics That Matter Beyond Total Return
Sharpe ratio, drawdown, and win rate, and what each one actually tells you.
Read more →Quant trading research and strategy write-ups.
Systematic and quantitative trading commentary.
Academic-backed trading strategy summaries.