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From candidate features to validated trading models
Wrapper-based feature selection evaluates predictive inputs through the exact model and validation framework intended for live deployment. Unlike statistical filters that judge variables in isolation, wrappers score candidate subsets by repeatedly training the target algorithm under purged cross-validation. This structure ties feature value directly to net economic performance, accounting for turnover, execution fees, and collinearity risks.
What’s inside:
Defining the wrapper paradigm: The framework wraps a search algorithm around a target model, scoring subsets on out-of-sample performance to capture joint feature interactions that univariate statistical filters miss.
Accounting for market frictions: Objective functions must penalize execution friction, turnover, and subset dimension, ensuring statistical predictive power translates into net strategy returns.
Sequential Forward Selection: Building compact feature sets from zero limits latency and operational pipeline fragility, though greedy forward steps risk missing variables that only add value in specific pairs.
Sequential Backward Selection: Starting with the complete feature matrix preserves joint interaction structures, but early pruning steps require regularization to manage matrix instability from collinear market inputs.
Exhaustive Feature Selection: Evaluating all possible subsets guarantees locating the global in-sample optimum, but exponential search spaces create massive computational costs and high selection bias under noise.
Sequential Floating Selection: Integrating conditional backward passes into forward search paths dynamically purges early variable additions that become redundant as complementary features enter the model.
Recursive Feature Elimination: Pruning variables based on standardized coefficients or block-permutation scores directly leverages internal model weights, requiring grouped evaluations when features share underlying market drivers.
