上記の前記事で行ったグリッドサーチの結果から、インサンプルのシャープレシオを計算する。
　シャープレシオは、グリッドサーチで見つけられた最適パラメータでのCVの平均スコアとその分散で与えられるから、グリッドサーチの結果を使って次のように実装する。

def getSharpeRatioIS(clf):
    
    best = np.argmin(clf.cv_results_['rank_test_score'])
    mean_score = clf.cv_results_['mean_test_score'][best]
    std_score = clf.cv_results_['std_test_score'][best]
    if mean_score < 0:
        return -mean_score / std_score
    else:
        return mean_score / std_score

Grid SearchCV neg_log_loss

clf = GridSearchCV(estimator = svm, 
                   param_grid = param_grid,
                   scoring='neg_log_loss', 
                   n_jobs=-1, 
                   refit=True,
                   cv= cv, 
                   verbose=10)
clf.fit(X,y['bin'],sample_weight = None)
print(f'Sharpe ratio: {getSharpeRatioIS(clf)}')

Grid SearchCV accuracy

clf = GridSearchCV(estimator = svm, 
                   param_grid = param_grid,
                   scoring='accuracy', 
                   n_jobs=-1, 
                   refit=True,
                   cv= cv, 
                   verbose=10)
clf.fit(X,y['bin'],sample_weight = None)
print(f'Sharpe ratio: {getSharpeRatioIS(clf)}')

RadomizedSearchCV with neg_log_loss

param_distributions = {'C':loguniform(a = 1e-2, b= 1e2),
                      'gamma':loguniform(a = 1e-2, b= 1e2)}

Rclf = RandomizedSearchCV(estimator = svm, 
                          param_distributions = param_distributions,
                          n_iter = 25, 
                          scoring='neg_log_loss', 
                          n_jobs=None, 
                          refit=True,
                          cv= cv, 
                          verbose=10)

Rclf.fit(X,y['bin'],sample_weight = None)
print(f'Sharpe ratio: {getSharpeRatioIS(Rclf)}')

RadomizedSearchCV with accuracy

Rclf = RandomizedSearchCV(estimator = svm, 
                          param_distributions = param_distributions,
                          n_iter = 25, 
                          scoring='accuracy', 
                          n_jobs=None, 
                          refit=True,
                          cv= cv,
                          verbose=10)

Rclf.fit(X,y['bin'],sample_weight = None)
print(f'Sharpe ratio: {getSharpeRatioIS(Rclf)}')