William Gann: The Important Role of Feature Scaling and Normalization in Trading Models

Feature scaling is a important preprocessing step in many machine learning algorithms. It involves transforming the features to be on a similar scale. This is important because many algorithms, such as Support Vector Machines (SVMs) and models with L1 or L2 regularization, are sensitive to the scale of the features.

Scikit-Learn provides several scaling techniques, each with its own advantages and disadvantages.

StandardScaler

StandardScaler scales the features to have a mean of 0 and a standard deviation of 1. This is the most common scaling technique and is a good default choice.

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

MinMaxScaler

MinMaxScaler scales the features to be between a given minimum and maximum value, typically 0 and 1.

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)

RobustScaler

RobustScaler is similar to StandardScaler but is more robust to outliers. It scales the data according to the quantile range.

from sklearn.preprocessing import RobustScaler

scaler = RobustScaler()
X_scaled = scaler.fit_transform(X)

from sklearn.preprocessing import RobustScaler

scaler = RobustScaler()
X_scaled = scaler.fit_transform(X)

When to Use Each Scaler

Mathematical Formulation: Standardization

The formula for standardization is:

z = rac{x - \mu}{\sigma}

Where:

• $x$ is the original feature vector.
• $\mu$ is the mean of the feature vector.
• $\sigma$ is the standard deviation of the feature vector.

By scaling your features appropriately, you can significantly improve the performance and stability of your trading models.