Description: Backtesting refers to the process of evaluating a predictive model or trading strategy using historical data. This approach allows researchers and developers to analyze how a model would have performed in past situations, which is crucial for validating its effectiveness before deploying it in a real-world environment. Through backtesting, patterns, trends, and potential errors in the model’s predictions can be identified, helping to optimize its performance. This process involves the use of datasets that have been previously collected, allowing analysts to simulate decisions and outcomes based on information that is already known. Backtesting is particularly relevant in fields such as artificial intelligence and machine learning, where the ability to generalize from historical data is fundamental to the success of models. Moreover, this method is not only applied to predictive models but also to trading strategies, where investors can assess the viability of their tactics based on how they would have performed in the past. In summary, backtesting is an essential tool for the validation and improvement of models, providing a solid foundation for informed decision-making.
