What is the first method of optimization described?

Whole sample optimization is the first method of optimization discussed and involves using the entire dataset to fine-tune the parameters of a trading model. This approach relies on analyzing all available historical data to identify the best parameters that maximize performance metrics, such as return or Sharpe ratio, without separating the data into distinct sets.

The advantage of whole sample optimization lies in its use of all historical information, which can provide a more comprehensive view of the underlying patterns in the data. However, it's important to note that this method can lead to overfitting, where the optimized parameters perform well on historical data but poorly on unseen data. This is because the model may capture noise rather than the true signal in the data.

Understanding the strengths and limitations of whole sample optimization is crucial for traders and analysts, as it lays the foundation for more sophisticated methods, such as Walk Forward optimization and In-Sample, Out-of-Sample testing, which aim to mitigate overfitting and enhance the robustness of trading strategies.