What a backtest is
A backtest takes a set of rules — when to buy, when to sell — and applies them to historical market data to produce the result those rules would have generated in the past. The output is usually an equity curve showing how an opening amount would have grown, alongside measures such as return, volatility and the Sharpe ratio.
It lets you see the consequences of an idea without risking any money, and it lets you change one rule, run it again, and see what happened. That fast loop is where a lot of understanding gets built.
What a backtest is good for
A backtest is the right tool for turning a vague idea into something concrete enough to examine. It answers questions like how often the strategy traded, how large its worst fall was, and how it behaved during a market crash that actually happened.
It is also how strategies are compared. Run two ideas over the same period with the same costs, and the backtest shows which one earned its return more efficiently.
What a backtest cannot tell you
The past is not a promise about the future, and a good-looking backtest can be misleading for reasons that were present all along: too little history to measure anything reliably, trading costs left out of the calculation, or rules adjusted until one stretch of data happened to look good.
That last problem has a name — overfitting — and it is the most common way a backtest deceives the person who built it. A chart that looks excellent is not, on its own, evidence that a strategy works.
From backtest to trusted strategy
Because of this, a backtest is a starting point, not a verdict. Before a strategy can be trusted, its backtest has to survive a series of further tests designed to catch the ways it might be an illusion — testing it on data it was never tuned on, and checking that its results are stable across different periods.
That process is what the QS Lab validation system does, and learning to run it is the core of the free course.