Backtesting simulates a trading or decision strategy on historical data. It applies the strategy's rules to past observations and calculates outcomes such as returns, losses, hit rate, drawdown and the number of opportunities that met the entry conditions.
A backtest is only as reliable as its data and assumptions. Look-ahead bias, overfitting, ignored fees, unrealistic fills and repeated experimentation can produce misleading results, so robust workflows add walk-forward validation and out-of-sample testing.
ELI5
Backtesting applies a trading or decision strategy to historical data to see how its rules would have behaved in the past. It is a simulation, not a record of money actually earned.
For example, a rule might buy after a stated price move and sell after a fixed period. The backtest can calculate past returns and losses, but realistic fees, timing and unseen data are needed because a strategy can look excellent when it was accidentally tuned to the same history used to test it.
