L1 Lasso Penalty

The L1 Lasso penalty is a regularization technique that adds the absolute value of the magnitude of coefficients to the loss function. This has the unique effect of shrinking some coefficients to exactly zero, effectively performing feature selection by eliminating less important variables.

In financial models with many noisy indicators, this helps identify the most influential factors driving price movements. By reducing the number of active features, it simplifies the model and reduces the risk of overfitting.

It is highly effective in high-dimensional settings common in modern algorithmic trading. The resulting model is often more interpretable, as it highlights the key drivers of the strategy.

It is a powerful tool for balancing model complexity with predictive performance.