Hands-On Machine Learning for Algorithmic Trading

Lasso regression using sklearn

The lasso implementation looks very similar to the ridge model we just ran. The main difference is that lasso needs to arrive at a solution using iterative coordinate descent whereas ridge can rely on a closed-form solution:

nfolds = 250alphas = np.logspace(-8, -2, 13)scaler = StandardScaler()lasso_results, lasso_coeffs = pd.DataFrame(), pd.DataFrame()for i, alpha in enumerate(alphas):    coeffs, test_results = [], []    lr_lasso = Lasso(alpha=alpha)    for i, (train_dates, test_dates) in enumerate(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_lasso.fit(X=scaler.fit_transform(X_train), y=y_train) X_test = model_data.loc[idx[test_dates] ...