Set out and contrast the stages and procedures involved when a strategy is backtested
The idea
Backtesting follows three broad steps. First, specify the strategy, including the universe, signals, rebalancing frequency and benchmark. Second, gather point-in-time data, and third, run the simulation to produce performance and risk statistics. The usual design is a rolling window: at each rebalancing date the model is estimated only on data available up to that date, the portfolio is formed, and its return over the following period is recorded, before the window moves forward. This mimics real-time investing and gives out-of-sample results. Data must reflect realistic reporting lags, for example using annual accounts only after they are published. The trap is estimating signals over the whole sample and then testing them on the same period, which builds knowledge of the future into the result.
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 what backtesting an investment strategy aims to achieve
- 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