feat(Data): added various data loading config options, walk forward method draft (#9)

* feat(Eval): added format_data_for_backtest()

* feat(Data): added many configurable parameters to load_files to reduce boilerplate and prepare for HPO

* feat(Core): added walk forward method of training/testing

* fix(Model): remove the unnecessary softmax activation from the keras models

* feat(Core): added walk_forward_train_test()

model_classification_sktime.py

@@ -1,5 +1,5 @@
 #%% Import all the stuff, load data, define constants
-from load_data import create_target_cum_forward_returns, create_target_pos_neg_classes, load_files, create_target_four_classes
+from load_data import load_files, create_target_classes
 import pandas as pd
 import numpy as np
 from utils.sktime import from_df_to_sktime_data
@@ -9,27 +9,46 @@ from sklearn.metrics import accuracy_score
 from sklearn.pipeline import Pipeline
 from sktime.classification.interval_based import (
     TimeSeriesForestClassifier,
+    SupervisedTimeSeriesForest,
+
 )
+from sktime.forecasting.model_selection import SlidingWindowSplitter
+from sktime.forecasting.model_selection import ForecastingRandomizedSearchCV
+
+from sktime.forecasting.compose import make_reduction
+
+from sklearn.tree import DecisionTreeClassifier
 from sklearn.metrics import confusion_matrix
 from sklearn.preprocessing import StandardScaler
 from sktime.forecasting.model_selection import (
     SlidingWindowSplitter,
     ForecastingGridSearchCV,
 )
-# from sktime.forecasting.model_evaluation import 
-# from sklearnex import patch_sklearn
-# patch_sklearn()
+from utils.evaluate import print_classification_metrics, format_data_for_backtest
+from sklearn.ensemble import RandomForestRegressor
+from sklearnex import patch_sklearn
+patch_sklearn()
 
 
+ticket_to_predict = 'BTC_USD'
+print('Predicting: ', ticket_to_predict)
+
+data = load_files(path='data/',
+    own_asset=ticket_to_predict,
+    load_other_assets=True,
+    log_returns=True,
+    add_date_features=True,
+    own_technical_features='level2',
+    other_technical_features='none',
+    exogenous_features='none',
+    index_column='int'
+)
 
-data = load_files('data/', add_features=True, log_returns=True, narrow_format=False)
-data.reset_index(drop=True, inplace=True)
-data = data[[column for column in data.columns if not column.endswith('volume')]]
-data = data[[column for column in data.columns if column.startswith('BTC_ETH_')]]
 target_col = 'target'
-data = create_target_pos_neg_classes(data, 'BTC_ETH_returns', 1)
+returns_col = ticket_to_predict + '_returns'
+data = create_target_classes(data, returns_col, 1, 'two')
 
-X = data
+X = data.drop(columns=[target_col])
 y = data[target_col]
 
 X_train, X_test, y_train, y_test = temporal_train_test_split(X, y, test_size=0.2)
@@ -45,11 +64,32 @@ X_test = from_df_to_sktime_data(X_test)
 # ])
 
 # pipe.fit(X_train, y_train)
+# regressor = RandomForestRegressor(n_estimators=20)
+# model = DecisionTreeClassifier(random_state=1)
+model = TimeSeriesForestClassifier(n_estimators=50, random_state=1)
+model.fit(y = y_train, X = X_train)
+# forecaster = make_reduction(model, scitype="tabular-regressor")
+# nested_params = {"window_length": list(range(2,30)), 
+#                  "estimator__max_depth": list(range(5,16))}
+#                 # "estimator__n_estimators": list(range(10,200))}
+#%%
 
-model = TimeSeriesForestClassifier(n_estimators=200, random_state=1)
-model.fit(X_train, y_train)
+# cv = SlidingWindowSplitter(initial_window=40, window_length=30)
+# nrcv = ForecastingRandomizedSearchCV(forecaster, strategy="refit", cv=cv, 
+#                                      param_distributions=nested_params, 
+#                                      n_iter=5, random_state=42)
+# nrcv.fit(y = y_train, X = X_train, fh=np.array([1]))
+# print(nrcv.best_params_)
+# print(nrcv.best_score_)
 
+# model = DecisionTreeClassifier(random_state=1)
+# model.fit(X_train, y_train)
+
+# preds = nrcv.best_forecaster_.predict( X=X_test)
+# # print(preds)
 preds = model.predict(X_test)
-print(model.score(X_test, y_test))
-print(confusion_matrix(y_test, preds))
+print(print_classification_metrics(y_test, preds))
 
+# backtest_data = format_data_for_backtest(data, returns_col, X_test, preds)
+# print(backtest_data)
+# %%