Feature selection is the process of identifying the most important variables for model training in machine learning, be it for feature engineering or model evaluation and provides an opportunity to select and discard features that are unimportant or repetitive. There’s a strong need for feature selection when working with trading models, because there are usually many datasets available, which may contain a lot of market indicators, macroeconomic variables etc Which can include unnecessary data.
This article argues in favor of feature selection in trading, while also describing a few specific techniques used for this process.
Why Feature Selection is Important in Trading
Increased Accuracy of the Model: The existence of irrelevant features or redundant variables around can diminish the predictive power of the model. It is vital that only certain variables are used so as Model is capable of making precise predictions.
Minimized Chances of Overfitting: With simpler models with fewer features, these make the model perform much better for new data without overfitting it.
Improved Interpretability: For Now, it’s more comfortable to interpret the model with fewer features as it’s much less complicated than the alternative, this is very important for trading decisions.
Increased Speed: There is a benefit of increased speed in training and real time prediction as there are fewer features leading to less need of computation.
Computation of Feature Selection Techniques
1. Filtering Techniques
Filter approaches analyze features without taking into account the ML algorithm, this utilizes statistics and metrics to evaluate the dataset.
Approaches:
Correlation Analysis: This is more of a statistical analysis whereby the relationship between variables or features in this case, and the target feature is established.
Example: Potential importance in predicting a stock price, is indicated when the S&P 500 index and that stocks price has a high correlation.
Mutual Information: Shows how features are related with the target.
Example: It seems that volatility has a strong mutual relationship with price movement.
Variance Thresholding: Eliminates features that have slight change across samples since they do not contain significant information.
Advantages:
Quick and cheap in terms of computation.
It’s good enough during one of the initial stages of processing.
Limitations:
Not able to see effects that features have on each other.
2. Wrapper Methods
Wrapper methods estimate ranges of features using model training and testing and find the best range.
Techniques:
Forward Selection: Feature subset starts with nothing and features are added one by one which are the most significant.
Backward Elimination: Starting with all features, has all of them removed at every step the least important feature.
RFE: Features are assigned ranks based on their significance then the least significant is removed and the process is repeated recursively.
Advantages:
Accounts for the effects that features have on each other.
Produces subsets that are designed for use with specific algorithms.
Limitations:
Costs a lot of resources especially time for big datasets.
3. Embedded Methods
Embedded methods make selection of features as part of the training process of the model.
Techniques:
Lasso and Ridge Regression: Applies kudos to the models when they have a lot of less significant features.
Example: Of some coefficients, their values can be reduced to zero by performing a Lasso regression.
Random Forest, XGBoost: Focuses on important variables by using feature importance scores.
Advantages:
Saves time and is effective for specific algorithms.
Makes feature selection and model training methods work optimally.
Limitations:
Relies on an algorithm selected, which may not be valid for all the models.
4. Dimensionality Reduction
These techniques encode certain feature space into lower dimensions but maintaining essential information.
Techniques:
- Principal Component Analysis (PCA): Transforms features into principal components which capture greatest variance.
Example: PCA may, for example, blend moving average indicators which are highly correlated into fewer components.
- t-SNE and UMAP: High-dimensional data is visualized and represented into two or three discrete dimensions for easier interpretation.
Advantages:
Appreciably deals with features where multicollinearity is present.
Helps in cleansing the data from noise.
Limitations:
There is a loss of interpretability where the features that have been transformed into new ones are regarded as complex combinations.
Statistical Tests for Feature Relevance
Statistical tests assist in assessing the relationships that exist between features and the target variable.
Techniques:
ANOVA (Analysis of variance): This assesses if there are statistically significant differences between the means of three or more independent (unrelated) groups:
Example: Investigates whether the returns of a certain stock are statistically different among returns of stocks under different sectors.
Chi-Square Test: Determines whether a significant relationship exists between two categorical variables.
Example: Assesses the statistical significance of news sentiment indicator concerning price direction, where news is either positive or negative.
Advantages:
Does not require sophisticated systems for its implementation.
Estimates approximate statistics considerable enough to suggest that a particular feature ought to be selected to form a part of the model.
Limitations:
Applicable only to specific types of data, be it either numerical or categorical.
6. Specialized Knowledge and Understanding the Domain
For a trader, domain knowledge is critical when it comes to defining the features. Quantitative analysts and financial practitioners can effectively determine the influential variables which may not be generated through generic approaches.
Approach:
Rely on financial theories and empirical evidence in deciding the features to be included.
Example: Involving economic measures such as GDP movements or values of interest rates as a result of employing a broader economic perspective.
Empirically test the importance of the number and relevance of features by applying statistical methods with the aid of economic knowledge.
Advantages:
Makes use of human intelligence that algorithms may miss.
Reduces chances of over restating by concentrating on the observable features.
Limitations:
It is subjective and may have some degree of bias.
7. Feature Importance Using Machine Learning Models
Some algorithms provide features importance indices, which rank features according to their usefulness in enhancing predictions.
Examples:
XGBoost, LightGBM and random forest, Gradient boosting provide importance scores for each of the features.
The Permutation Importance feature identifies a drop in performance of a model whenever a feature is randomly shuffled.
The Advantages:
Allows for feature importance ranking for one given case model.
Limitations:
Does not switch work well in all the algorithms.
Best Practices for Feature Selection in Trading
Start with Filter Methods: basic truth sets should be employed to narrow the size of the feature field already introduced.
Combine Techniques: wrap, filter, and embedded techniques should not be neglected as they would assist in achieving accuracy.
Validate Features: Conduct suitable features backtesting on a regular basis in order to confirm whether their definitional relationship holds over any given period of time.
Focus on Interpretability: Refrain from building unnecessary abstraction layers by having an abundance of features that are not easy to comprehend.
Adapt to Changing Markets: With the changing market conditions, the relevance of different features also changes, so it is necessary to evaluate them infrequently.
Conclusion
In the development of high quality trading machine learning models feature selection remains one of the most important processes. A combination of statistical, algorithmic, and place-based practice enables quantitative traders to boost model performance while decreasing its complexity and improving interpretability. In a trading atmosphere where data is King and rules all, the right choice of features can be a big plus.
To avail our algo tools or for custom algo requirements, visit our parent site Bluechipalgos.com
Leave a Reply