Stock Prediction

This code compare several linear models from sklearn librairy in order to predict stock. /data: APPL.csv.

The training set is based on a window of N days, the target value is the following day.

Description

This code try several parameters changes to check impact on prediction. All combinations are checked for all models and only top10 (RMSE score) are displayed.

Results

Chart comparing the 10 best predictions.

Root Mean Squarred Error results (sorted: best first)

Samples:

• [1][model=LinearRegression s=10 frac=0.7fit_intercept=False normalize=True] score=3.329237228064044 ** BEST
• [2][model=LinearRegression s=10 frac=0.7fit_intercept=False normalize=False] score=3.329237228064044
• [3][model=Ridge s=10 frac=0.7fit_intercept=False alpha=1.0] score=3.3293174192861867
• [4][model=Ridge s=10 frac=0.7fit_intercept=False alpha=1.1] score=3.3293254577040705
• [5][model=BayesianRidge s=10 frac=0.7fit_intercept=False n_iter=300] score=3.3468441167291743
• [6][model=LassoLars s=10 frac=0.7fit_intercept=False alpha=1.1] score=3.361863077205247
• [7][model=LassoLars s=10 frac=0.7fit_intercept=False alpha=1.0] score=3.3628894021644435
• [8][model=LassoLars s=15 frac=0.7fit_intercept=False alpha=1.1] score=3.399937460735615
• [9][model=LassoLars s=15 frac=0.7fit_intercept=False alpha=1.0] score=3.4029249158774477
• [10][model=LinearRegression s=15 frac=0.7fit_intercept=False normalize=True] score=3.4402616157909653
• [11][model=LinearRegression s=15 frac=0.7fit_intercept=False normalize=False] score=3.4402616157909653
• [12][model=Ridge s=15 frac=0.7fit_intercept=False alpha=1.0] score=3.4404017547558765
• [13][model=Ridge s=15 frac=0.7fit_intercept=False alpha=1.1] score=3.4404157852087014
• [14][model=LassoLars s=55 frac=0.7fit_intercept=False alpha=1.1] score=3.4566733970927497
• [15][model=LassoLars s=55 frac=0.7fit_intercept=False alpha=1.0] score=3.4738485938536816
• [16][model=BayesianRidge s=10 frac=0.8fit_intercept=False n_iter=300] score=3.4865633099831275
• [17][model=BayesianRidge s=15 frac=0.7fit_intercept=False n_iter=300] score=3.4873511446158356
• [18][model=Ridge s=10 frac=0.8fit_intercept=False alpha=1.1] score=3.489907851164658
• [19][model=Ridge s=10 frac=0.8fit_intercept=False alpha=1.0] score=3.489912997254991
• [20][model=LinearRegression s=10 frac=0.8fit_intercept=False normalize=True] score=3.489964738843719
• [21][model=LinearRegression s=10 frac=0.8fit_intercept=False normalize=False] score=3.489964738843719
• [22][model=LassoLars s=30 frac=0.7fit_intercept=False alpha=1.0] score=3.5291854352735186
• [23][model=LassoLars s=30 frac=0.7fit_intercept=False alpha=1.1] score=3.5321217727932126
• [24][model=BayesianRidge s=15 frac=0.8fit_intercept=False n_iter=300] score=3.539748258433491
• [25][model=Ridge s=15 frac=0.8fit_intercept=False alpha=1.1] score=3.5410391205878593

Samples:

• [1][model=LinearRegression s=10 frac=0.7fit_intercept=False normalize=True] score=3.329237228064044 ** BEST
• [2][model=LinearRegression s=10 frac=0.7fit_intercept=False normalize=False] score=3.329237228064044
• [3][model=Ridge s=10 frac=0.7fit_intercept=False normalize=True alpha=0.1] score=3.3292452313618024
• [4][model=Ridge s=10 frac=0.7fit_intercept=False normalize=False alpha=0.1] score=3.3292452313618024
• [5][model=Ridge s=10 frac=0.7fit_intercept=False normalize=True alpha=0.30000000000000004] score=3.3292612485236264
• [6][model=Ridge s=10 frac=0.7fit_intercept=False normalize=False alpha=0.30000000000000004] score=3.3292612485236264
• [7][model=Ridge s=10 frac=0.7fit_intercept=False normalize=True alpha=0.5000000000000001] score=3.3292772797589114
• [8][model=Ridge s=10 frac=0.7fit_intercept=False normalize=False alpha=0.5000000000000001] score=3.3292772797589114
• [9][model=Ridge s=10 frac=0.7fit_intercept=False normalize=True alpha=0.7000000000000001] score=3.3292933250474643
• [10][model=Ridge s=10 frac=0.7fit_intercept=False normalize=False alpha=0.7000000000000001] score=3.3292933250474643