Set out what backtesting an investment strategy aims to achieve
The idea
A backtest applies a rules-based strategy to past data as if it had been run in real time. Its objectives are to estimate the strategy's return and risk, compare it with benchmarks and alternatives, understand when and why it works, measure drawdowns and turnover, and see how sensitive results are to parameter choices. A backtest is a necessary filter for an idea but not proof: good historical performance may reflect luck, data mining or a regime that has passed. The trap is treating a strong backtest as evidence that the strategy will deliver the same results in future.
In the app: 2 practice questions on this outcome, a narrated walkthrough with diagrams, spaced review that brings it back before you forget it.
Take the free diagnostic →More in Backtesting and Simulation
- Set out and contrast the stages and procedures involved when a strategy is backtested
- Interpret the metrics and charts that a strategy backtest reports
- Spot flaws in the backtest of a strategy
- Assess and interpret an analysis based on historical scenarios
- Contrast the Monte Carlo approach to simulation with the historical approach
- Explain the inputs and choices a simulation involves and interpret what it produces
- Show how sensitivity analysis is applied