Backtesting (/trade/backtest)
Test strategies with historical data — and the pitfalls
/trade/backtest runs five pre-built strategies — MA(50/200) crossover, RSI(14) mean-reversion, Bollinger reversion, MACD crossover, Donchian breakout — against real historical bars over a 2-to-10-year window you choose. Backtesting is the most-misused tool in quantitative analysis: when done correctly, it reveals real edges; when done incorrectly, it manufactures false confidence in strategies that will fail in production. The discipline matters more than the technology.
A backtest runs a strategy through historical data to estimate how it would have performed in the past. The output is typically: cumulative returns, Sharpe ratio, max drawdown, win rate, average win vs. average loss.
The implicit assumption — that historical performance is informative about future performance — is true ONLY when the backtest methodology is rigorous. Most amateur backtests violate one or more methodological requirements and produce wildly optimistic results that don't survive contact with live trading.
That is the opening. Finishing a lesson is where it stops being interesting and starts being useful: the full lesson runs to 5 sections and ends with 3 practice questions. A free account is what opens the rest, and the other 255 lessons in the Academy with it. No card.
What this lesson covers
- 1Survivorship bias and how it inflates backtests
- 2The backtest validation hierarchy
- 3The 'overfitting paradox' — why high in-sample Sharpe is bad news
- 4Where to see this on the platform
- 5Summary