use lightgbm now

model_1h.py

@@ -16,6 +16,7 @@ from sklearn.metrics import roc_auc_score, precision_score, recall_score
 import datetime
 from pandas.tseries.offsets import BDay
 from datasets import load_dataset
+import lightgbm as lgb
 
 def walk_forward_validation(df, target_column, num_training_rows, num_periods):
     
@@ -73,7 +74,8 @@ def walk_forward_validation_seq(df, target_column_clf, target_column_regr, num_t
     df['RegrModelOut'] = df['RegrModelOut'].astype(bool)
 
     # Create an XGBRegressor model
-    model2 = xgb.XGBClassifier(n_estimators=10, random_state = 42)
+    # model2 = xgb.XGBClassifier(n_estimators=10, random_state = 42)
+    model2 = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbosity=-1)
     # model = linear_model.LogisticRegression(max_iter=1500)
     
     overall_results = []

model_30m.py

@@ -16,6 +16,7 @@ from sklearn.metrics import roc_auc_score, precision_score, recall_score
 import datetime
 from pandas.tseries.offsets import BDay
 from datasets import load_dataset
+import lightgbm as lgb
 
 # If the dataset is gated/private, make sure you have run huggingface-cli login
 def walk_forward_validation(df, target_column, num_training_rows, num_periods):
@@ -73,7 +74,8 @@ def walk_forward_validation_seq(df, target_column_clf, target_column_regr, num_t
     df['RegrModelOut'] = df['RegrModelOut'].astype(bool)
 
     # Create an XGBRegressor model
-    model2 = xgb.XGBClassifier(n_estimators=10, random_state = 42)
+    # model2 = xgb.XGBClassifier(n_estimators=10, random_state = 42)
+    model2 = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbosity=-1)
     # model = linear_model.LogisticRegression(max_iter=1500)
     
     overall_results = []
@@ -233,7 +235,7 @@ def get_data():
     # Get incremental data
     spx1 = yf.Ticker('^GSPC')
     yfp = spx1.history(start=last_date, interval='30m')
-    
+
     if len(yfp) > 0:
         # Concat current and incremental
         df_30m = pd.concat([fr, yfp])

model_90m.py

@@ -16,6 +16,7 @@ from sklearn.metrics import roc_auc_score, precision_score, recall_score
 import datetime
 from pandas.tseries.offsets import BDay
 from datasets import load_dataset
+import lightgbm as lgb
 
 def walk_forward_validation(df, target_column, num_training_rows, num_periods):
     
@@ -73,7 +74,8 @@ def walk_forward_validation_seq(df, target_column_clf, target_column_regr, num_t
     df['RegrModelOut'] = df['RegrModelOut'].astype(bool)
 
     # Create an XGBRegressor model
-    model2 = xgb.XGBClassifier(n_estimators=10, random_state = 42)
+    # model2 = xgb.XGBClassifier(n_estimators=10, random_state = 42)
+    model2 = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbosity=-1)
     # model = linear_model.LogisticRegression(max_iter=1500)
     
     overall_results = []

model_day.py

@@ -15,6 +15,8 @@ import os
 from sklearn.metrics import roc_auc_score, precision_score, recall_score
 import datetime
 from pandas.tseries.offsets import BDay
+import lightgbm as lgb
+
 
 def walk_forward_validation(df, target_column, num_training_rows, num_periods):
     
@@ -67,7 +69,8 @@ def walk_forward_validation_seq(df, target_column_clf, target_column_regr, num_t
     df['RegrModelOut'] = df['RegrModelOut'].astype(bool)
 
     # Create an XGBRegressor model
-    model2 = xgb.XGBClassifier(n_estimators=10, random_state = 42)
+    # model2 = xgb.XGBClassifier(n_estimators=10, random_state = 42)
+    model2 = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbosity=-1)
     # model = linear_model.LogisticRegression(max_iter=1500)
     
     overall_results = []

requirements.txt

@@ -7,6 +7,7 @@ requests
 beautifulsoup4
 typing_extensions
 xgboost
+lightgbm
 tqdm
 fastjsonschema
 json5