feat(Pipeline): save results, train on all assets

2026-08-23 15:58:08 +00:00 · 2021-12-14 22:59:44 +01:00
parent 7a6ddcec08
commit 64721330a3
2 changed files with 27 additions and 23 deletions
@@ -0,0 +1,106 @@
+from typing import Literal
+from sklearnex import patch_sklearn
+patch_sklearn()
+
+from load_data import get_all_assets, load_data
+from utils.evaluate import evaluate_predictions
+
+import pandas as pd
+from sklearn.linear_model import LinearRegression, Lasso, BayesianRidge, LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsRegressor, KNeighborsClassifier
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.svm import SVR
+from sklearn.naive_bayes import GaussianNB
+from sklearn.neural_network import MLPRegressor, MLPClassifier
+from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor, ExtraTreesRegressor, AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier, ExtraTreesClassifier
+from sklearn.preprocessing import MinMaxScaler
+
+from utils.walk_forward import walk_forward_train_test
+
+regression_models = [
+    ('LR', LinearRegression(n_jobs=-1)),
+    ('BayesianRidge', BayesianRidge()),
+    ('KNN', KNeighborsRegressor(n_neighbors=15)),
+    # ('MLP', MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000)),
+    ('AB', AdaBoostRegressor()),
+    # ('RF', RandomForestRegressor(n_jobs=-1)),
+    # ('SVR', SVR(kernel='rbf', C=1e3, gamma=0.1))
+]
+classification_models = [
+    ('LR', LogisticRegression(n_jobs=-1)),
+    ('LDA', LinearDiscriminantAnalysis()),
+    ('KNN', KNeighborsClassifier()),
+    ('CART', DecisionTreeClassifier()),
+    ('NB', GaussianNB()),
+    ('AB', AdaBoostClassifier()),
+    ('RF', RandomForestClassifier(n_jobs=-1))
+]
+
+
+
+def run_whole_pipeline(
+                    ticker_to_predict: str,
+                    models,
+                    method: Literal['regression', 'classification'],
+                    sliding_window_size: int,
+                    retrain_every: int,
+                    scaling: bool,
+                    ):
+    print('--------\nPredicting: ', ticker_to_predict)
+
+    X, y = load_data(path='data/',
+        target_asset=ticker_to_predict,
+        target_asset_lags=[1,2,3,4,5,6,8,10,15],
+        load_other_assets=False,
+        other_asset_lags=[],
+        log_returns=True,
+        add_date_features=True,
+        own_technical_features='level2',
+        other_technical_features='none',
+        exogenous_features='none',
+        index_column='int',
+        method=method,
+    )
+
+
+    if scaling:
+        # TODO: should move scaling to an expanding window compomenent, probably worth not turning it on for now
+        feature_scaler = MinMaxScaler(feature_range= (-1, 1))
+        X = pd.DataFrame(feature_scaler.fit_transform(X), columns=X.columns, index=X.index)
+        # TODO: should scale y as well probably
+
+    results = pd.DataFrame()
+
+    for model_name, model in models:
+
+        model_over_time, preds = walk_forward_train_test(
+            model_name=model_name,
+            model = model,
+            X = X,
+            y = y,
+            window_size = sliding_window_size,
+            retrain_every = retrain_every
+        )
+        result = evaluate_predictions(model_name, y, preds, sliding_window_size, method)
+        column_name = ticker_to_predict + "_" + model_name
+        results[column_name] = result
+
+    return results
+
+results = pd.DataFrame()
+all_assets = get_all_assets('data/')
+
+for asset in all_assets:
+    for method in ['regression', 'classification']:
+        current_result = run_whole_pipeline(
+            ticker_to_predict = asset,
+            models = regression_models if method == 'regression' else classification_models,
+            method = method,
+            sliding_window_size = 120,
+            retrain_every = 50,
+            scaling = False
+        )
+        results = pd.concat([results, current_result], axis=1)
+
+results.to_csv('results.csv')