Hands-On Machine Learning for Algorithmic Trading

Tuning the regularization parameters using cross-validation

We then proceed to cross-validate the hyperparameter values again using 250 folds as follows:

nfolds = 250alphas = np.logspace(-5, 5, 21)scaler = StandardScaler()ridge_result, ridge_coeffs = pd.DataFrame(), pd.DataFrame()for i, alpha in enumerate(alphas):    coeffs, test_results = [], []    lr_ridge = Ridge(alpha=alpha)    for train_dates, test_dates in time_series_split(dates, nfolds=nfolds):        X_train = model_data.loc[idx[train_dates], features]        y_train = model_data.loc[idx[train_dates], target]        lr_ridge.fit(X=scaler.fit_transform(X_train), y=y_train)        coeffs.append(lr_ridge.coef_)        X_test = model_data.loc[idx[test_dates], features]        y_test = model_data.loc[idx[test_dates], target] y_pred = ...