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@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+cleaned_training.csv filter=lfs diff=lfs merge=lfs -text

cfg.py

@@ -0,0 +1,170 @@
+DROP_LIST = ['ID',
+ '外資券商_分點進出',
+ '外資券商_前1天分點進出',
+ '外資券商_前2天分點進出',
+ '外資券商_前3天分點進出',
+ '外資券商_前4天分點進出',
+ '外資券商_前5天分點進出',
+ '外資券商_前6天分點進出',
+ '外資券商_前7天分點進出',
+ '外資券商_前8天分點進出',
+ '外資券商_前9天分點進出',
+ '外資券商_前10天分點進出',
+ '外資券商_前11天分點進出',
+ '外資券商_前12天分點進出',
+ '外資券商_前13天分點進出',
+ '外資券商_前14天分點進出',
+ '外資券商_前15天分點進出',
+ '外資券商_前16天分點進出',
+ '外資券商_前17天分點進出',
+ '外資券商_前18天分點進出',
+ '外資券商_前19天分點進出',
+ '外資券商_前20天分點進出',
+ '主力券商_分點進出',
+ '主力券商_前1天分點進出',
+ '主力券商_前2天分點進出',
+ '主力券商_前3天分點進出',
+ '主力券商_前4天分點進出',
+ '主力券商_前5天分點進出',
+ '主力券商_前6天分點進出',
+ '主力券商_前7天分點進出',
+ '主力券商_前8天分點進出',
+ '主力券商_前9天分點進出',
+ '主力券商_前10天分點進出',
+ '主力券商_前11天分點進出',
+ '主力券商_前12天分點進出',
+ '主力券商_前13天分點進出',
+ '主力券商_前14天分點進出',
+ '主力券商_前15天分點進出',
+ '主力券商_前16天分點進出',
+ '主力券商_前17天分點進出',
+ '主力券商_前18天分點進出',
+ '主力券商_前19天分點進出',
+ '主力券商_前20天分點進出',
+ '日外資_外資買張',
+ '日外資_外資賣張',
+ '日外資_外資買賣超',
+ '日外資_外資持股異動',
+ '日外資_外資持股張數',
+ '日外資_外資買金額(千)',
+ '日外資_外資賣金額(千)',
+ '日外資_外資買賣超金額(千)',
+ '日外資_外資持股比率(%)',
+ '日外資_外資持股市值(百萬)',
+ '日外資_外資尚可投資張數',
+ '日外資_外資尚可投資比率(%)',
+ '日外資_外資投資上限比率(%)',
+ '日自營_自營商買張',
+ '日自營_自營商賣張',
+ '日自營_自營商買賣超',
+ '日自營_自營商買張(自行買賣)',
+ '日自營_自營商賣張(自行買賣)',
+ '日自營_自營商買賣超(自行買賣)',
+ '日自營_自營商買張(避險)',
+ '日自營_自營商賣張(避險)',
+ '日自營_自營商買賣超(避險)',
+ '日自營_自營商庫存',
+ '日自營_自營商買金額(千)',
+ '日自營_自營商賣金額(千)',
+ '日自營_自營商買賣超金額(千)',
+ '日自營_自營商持股比率(%)',
+ '日自營_自營商持股市值(百萬)',
+ '日投信_投信買張',
+ '日投信_投信賣張',
+ '日投信_投信買賣超',
+ '日投信_投信庫存',
+ '日投信_投信買金額(千)',
+ '日投信_投信賣金額(千)',
+ '日投信_投信買賣超金額(千)',
+ '日投信_投信持股比率(%)',
+ '日投信_投信持股市值(百萬)',
+ '技術指標_週MACD',
+ '技術指標_週DIF-週MACD',
+ '技術指標_週DIF',
+ '技術指標_週-DI(14)',
+ '技術指標_週ADX(14)',
+ '技術指標_週+DI(14)',
+ '技術指標_相對強弱比(週)',
+ '技術指標_相對強弱比(日)',
+ '技術指標_近一月歷史波動率(%)',
+ '技術指標_乖離率(20日)',
+ '技術指標_RSI(5)',
+ '技術指標_RSI(10)',
+ '技術指標_MACD',
+ '技術指標_K(9)',
+ '技術指標_EWMA波動率(%)',
+ '技術指標_DIF-MACD',
+ '技術指標_DIF',
+ '技術指標_+DI(14)',
+ '技術指標_-DI(14)',
+ '技術指標_D(9)',
+ '技術指標_Beta係數(21D)',
+ '技術指標_ADX(14)',
+ '技術指標_保力加通道–頂部(20)',
+ '技術指標_保力加通道–均線(20)',
+ '技術指標_保力加通道–底部(20)',
+ '技術指標_SAR',
+ '技術指標_TR(1)',
+ '技術指標_ADXR(14)',
+ '技術指標_+DM(14)',
+ '技術指標_-DM(14)',
+ '技術指標_週TR(14)',
+ '技術指標_週+DM(14)',
+ '技術指標_週-DM(14)',
+ "飆股",
+ '個股收盤價',
+ '個股前1天收盤價',
+ '個股前2天收盤價',
+ '個股前3天收盤價',
+ '個股前4天收盤價',
+ '個股前5天收盤價',
+ '個股前6天收盤價',
+ '個股前7天收盤價',
+ '個股前8天收盤價',
+ '個股前9天收盤價',
+ '個股前10天收盤價',
+ '個股前11天收盤價',
+ '個股前12天收盤價',
+ '個股前13天收盤價',
+ '個股前14天收盤價',
+ '個股前15天收盤價',
+ '個股前16天收盤價',
+ '個股前17天收盤價',
+ '個股前18天收盤價',
+ '個股前19天收盤價',
+ '個股前20天收盤價',
+ '個股1天報酬率',
+ '個股5天報酬率',
+ '個股10天報酬率',
+ '個股20天報酬率',
+ '個股5天波動度',
+ '個股10天波動度',
+ '個股20天波動度',
+ '個股5天乖離率',
+ '個股10天乖離率',
+ '個股19天乖離率',
+ '個股成交量',
+ '個股前1天成交量',
+ '個股前2天成交量',
+ '個股前3天成���量',
+ '個股前4天成交量',
+ '個股前5天成交量',
+ '個股前6天成交量',
+ '個股前7天成交量',
+ '個股前8天成交量',
+ '個股前9天成交量',
+ '個股前10天成交量',
+ '個股前11天成交量',
+ '個股前12天成交量',
+ '個股前13天成交量',
+ '個股前14天成交量',
+ '個股前15天成交量',
+ '個股前16天成交量',
+ '個股前17天成交量',
+ '個股前18天成交量',
+ '個股前19天成交量',
+ '個股前20天成交量',
+ '個股5天成交量波動度',
+ '個股10天成交量波動度',
+ '個股20天成交量波動度',
+]

cleaned_training.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f00e1b54b7230e0949d2ac31034cfa99017698c6df29c9349b6020f58b6d8b65
+size 311126925

train.py

@@ -0,0 +1,168 @@
+import polars as pl
+import tsfel
+import numpy as np
+from tqdm import tqdm
+import warnings
+import os
+import optuna  # Added import for Optuna
+from sklearn.model_selection import train_test_split, cross_val_score
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import FunctionTransformer
+from sklearn.base import BaseEstimator, TransformerMixin
+from xgboost import XGBClassifier
+from lightgbm import LGBMClassifier
+
+from cfg import DROP_LIST
+
+# Rest of the imports and class definitions remain unchanged...
+
+def load_and_balance_data(filename, ratio=1/30):
+    """
+    Loads data from a CSV file and balances classes to address potential imbalance.
+    
+    Args:
+        filename (str): Path to the CSV file
+        ratio (float): Ratio of positive to negative examples to balance
+        
+    Returns:
+        pl.DataFrame: Balanced dataframe
+    """
+    print(f"Ratio: {ratio}")
+
+    # Load the CSV file
+    data = pl.read_csv(filename)
+    
+    positive = data.filter(pl.col("飆股") == 1)
+    negative = data.filter(pl.col("飆股") == 0)
+
+    # Balance the classes
+    negative = negative.sample(fraction=ratio)
+
+    # Combine the balanced classes
+    combined = positive.vstack(negative)
+
+    return combined
+
+def create_stock_prediction_pipeline(params=None):
+    start_end_list = [
+        # ("個股", "收盤價"),
+        # ("上市加權指數", "收盤價"),
+        # ("外資券商", "分點進出"),
+        # ("主力券商", "分點進出"),
+        # ("個股", "成交量")
+    ]
+    
+    # Use default parameters if none provided
+    if params is None:
+        classifier = XGBClassifier(booster="dart", n_jobs=-1)
+    else:
+        classifier = XGBClassifier(
+            booster="dart",
+            n_jobs=-1,
+            **params
+        )
+    
+    pipeline = Pipeline([
+        # ('time_series_features', TimeSeriesFeatureExtractor(start_end_list=start_end_list)),
+        ('classifier', classifier)
+    ])
+    return pipeline
+
+def objective(trial, X, y):
+    """Optuna objective function for hyperparameter tuning"""
+    
+    # Define the hyperparameters to tune
+    params = {
+        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3, log=True),
+        'max_depth': trial.suggest_int('max_depth', 3, 10),
+        'n_estimators': trial.suggest_int('n_estimators', 50, 300),
+        'subsample': trial.suggest_float('subsample', 0.5, 1.0),
+        'colsample_bytree': trial.suggest_float('colsample_bytree', 0.5, 1.0),
+        'gamma': trial.suggest_float('gamma', 0, 5),
+        'min_child_weight': trial.suggest_int('min_child_weight', 1, 10),
+        'reg_alpha': trial.suggest_float('reg_alpha', 0, 10),
+        'reg_lambda': trial.suggest_float('reg_lambda', 1, 10)
+    }
+    
+    # Create and evaluate the pipeline
+    pipeline = create_stock_prediction_pipeline(params)
+    
+    # Use 3-fold cross validation for stability
+    scores = cross_val_score(pipeline, X, y, cv=3, scoring='f1')
+    
+    # Return the mean F1 score
+    return scores.mean()
+
+def main():
+    # Load and preprocess training data
+    print("Loading and preprocessing training data...")
+    combined = load_and_balance_data('./cleaned_training.csv', ratio=1/30)
+    
+    # Define features and target
+    X = combined.drop(DROP_LIST)
+    y = combined["飆股"]
+    
+    # Split data into training and testing sets
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    # Hyperparameter tuning with Optuna
+    print("Starting hyperparameter optimization with Optuna...")
+    study = optuna.create_study(direction='maximize')  # We want to maximize the F1 score
+    study.optimize(lambda trial: objective(trial, X_train, y_train), n_trials=20)
+    
+    # Print the best parameters
+    best_params = study.best_params
+    best_value = study.best_value
+    print(f"Best F1 score: {best_value:.4f}")
+    print("Best hyperparameters:", best_params)
+    
+    # Create and train the pipeline with the best parameters
+    print("Training final model with the best parameters...")
+    pipeline = create_stock_prediction_pipeline(best_params)
+    pipeline.fit(X_train, y_train)
+    
+    # Evaluate the model
+    print("Evaluating the model...")
+    y_pred = pipeline.predict(X_test)
+    
+    # Calculate metrics
+    from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
+    
+    accuracy = accuracy_score(y_test, y_pred)
+    f1 = f1_score(y_test, y_pred)
+    precision = precision_score(y_test, y_pred)
+    recall = recall_score(y_test, y_pred)
+    
+    print(f"Accuracy: {accuracy:.4f}")
+    print(f"F1 Score: {f1:.4f}")
+    print(f"Precision: {precision:.4f}")
+    print(f"Recall: {recall:.4f}")
+    
+    # Confusion matrix
+    cm = confusion_matrix(y_test, y_pred)
+    print("Confusion Matrix:")
+    print(cm)
+    
+    # Load and predict on test data
+    print("Loading test data and making predictions...")
+    test = pl.read_csv('./38_Public_Test_Set_and_Submmision_Template/38_Public_Test_Set_and_Submmision_Template/public_x.csv')
+    template = pl.read_csv('./38_Public_Test_Set_and_Submmision_Template/38_Public_Test_Set_and_Submmision_Template/submission_template_public.csv')
+    
+    # Ensure test data has the same columns as training data
+    selected = test.select(X.columns)
+    
+    # Make predictions
+    y_pred_test = pipeline.predict(selected)
+    
+    # Count predictions
+    unique, counts = np.unique(y_pred_test, return_counts=True)
+    print("Prediction counts:", dict(zip(unique, counts)))
+    
+    # Save predictions to submission file
+    template = template.with_columns(pl.Series("飆股", y_pred_test))
+    template.write_csv("submission.csv")
+    print("Predictions saved to submission.csv")
+
+if __name__ == "__main__":
+    main()