Mastering Python for Finance - Second Edition

Reconstructing the Dow index with PCA

By default, the KernelPCA is instantiated with the n_components=None parameter, which constructs a kernel PCA with non-zero components. We can also create a PCA index with five components:

In [ ]:    import numpy as np    kernel_pca = KernelPCA(n_components=5).fit(df_z_components)    pca_5 = kernel_pca.transform(-daily_df_components)    weights = fn_weighted_avg(kernel_pca.lambdas_)    reconstructed_values = np.dot(pca_5, weights)    # Combine DJIA and PCA index for comparison    df_combined = djia_2017.copy()    df_combined['pca_5'] = reconstructed_values    df_combined = df_combined.apply(fn_z_score)    df_combined.plot(figsize=(12, 8));

With the fit() method, we fitted the normalized dataset using the linear kernel PCA function ...