refactor: Improve documentation and code quality

separate_codes/Model_Development.py

@@ -1,76 +1,85 @@
 import pandas as pd
-
-# Load the data
-raw_price_df = pd.read_csv("raw_price_data.csv")
-
-# Feature Engineering
-raw_price_df["feature_price_change"] = raw_price_df["Close"].diff()
-
-# Target Engineering
-raw_price_df["y_target_direction"] = (raw_price_df["Close"].shift(-1) > raw_price_df["Close"]).astype(int)
-
-# Drop rows with NaN values
-raw_price_df.dropna(inplace=True)
-
 from sklearn.model_selection import TimeSeriesSplit, RandomizedSearchCV
 from sklearn.neural_network import MLPClassifier
-
-# --- Hyperparameter Tuning ---
-# Define the parameter search space
-param_distributions = {
-    'hidden_layer_sizes': [(50,), (100,), (50, 50)],
-    'activation': ['tanh', 'relu'],
-    'solver': ['adam', 'sgd'],
-    'learning_rate': ['constant', 'adaptive'],
-}
-
-# Create the neural network model
-neural_network_model = MLPClassifier(max_iter=1000)
-
-# Create the time series split object
-ts_split = TimeSeriesSplit(n_splits=2, gap=1)
-
-# Create the randomized search object
-random_search = RandomizedSearchCV(
-    estimator=neural_network_model,
-    param_distributions=param_distributions,
-    n_iter=10,  # Reduced for faster execution
-    cv=ts_split,
-    scoring='accuracy',
-    random_state=42,
-    n_jobs=-1
-)
-
-# Separate features and target
-X = raw_price_df[['feature_price_change']]
-y = raw_price_df['y_target_direction']
-
-# Fit the randomized search to the data
-random_search.fit(X, y)
-
-# Print the best parameters
 import skl2onnx
-import onnxruntime as rt
 from skl2onnx.common.data_types import FloatTensorType
 
-# --- Final Model Training ---
-# Initialize a new neural network model with the best parameters
-final_model = MLPClassifier(**random_search.best_params_, max_iter=1000)
+def main():
+    """
+    This script performs the following steps:
+    1. Loads the historical price data from a CSV file.
+    2. Performs feature engineering to create a 'feature_price_change' feature.
+    3. Performs target engineering to create a 'y_target_direction' target variable.
+    4. Performs hyperparameter tuning using RandomizedSearchCV and TimeSeriesSplit to find the best parameters for an MLPClassifier.
+    5. Trains a final MLPClassifier model with the best parameters.
+    6. Exports the trained model to an ONNX file named 'trading_neural_network_model.onnx'.
+    """
+    # Load the data
+    raw_price_df = pd.read_csv("raw_price_data.csv")
 
-# Train the model on the entire dataset
-final_model.fit(X, y)
+    # Feature Engineering
+    raw_price_df["feature_price_change"] = raw_price_df["Close"].diff()
 
-# --- Export to ONNX ---
-# Define the initial types for the ONNX conversion
-initial_type = [('float_input', FloatTensorType([None, 1]))]
+    # Target Engineering
+    raw_price_df["y_target_direction"] = (raw_price_df["Close"].shift(-1) > raw_price_df["Close"]).astype(int)
 
-# Convert the model to ONNX format
-onnx_model = skl2onnx.convert_sklearn(final_model, initial_types=initial_type)
+    # Drop rows with NaN values
+    raw_price_df.dropna(inplace=True)
 
-# Save the ONNX model
-with open("trading_neural_network_model.onnx", "wb") as f:
-    f.write(onnx_model.SerializeToString())
+    # --- Hyperparameter Tuning ---
+    # Define the parameter search space
+    param_distributions = {
+        'hidden_layer_sizes': [(50,), (100,), (50, 50)],
+        'activation': ['tanh', 'relu'],
+        'solver': ['adam', 'sgd'],
+        'learning_rate': ['constant', 'adaptive'],
+    }
 
-print("Model successfully exported to trading_neural_network_model.onnx")
+    # Create the neural network model
+    neural_network_model = MLPClassifier(max_iter=1000)
 
+    # Create the time series split object
+    ts_split = TimeSeriesSplit(n_splits=2, gap=1)
 
+    # Create the randomized search object
+    random_search = RandomizedSearchCV(
+        estimator=neural_network_model,
+        param_distributions=param_distributions,
+        n_iter=10,  # Reduced for faster execution
+        cv=ts_split,
+        scoring='accuracy',
+        random_state=42,
+        n_jobs=-1
+    )
+
+    # Separate features and target
+    X = raw_price_df[['feature_price_change']]
+    y = raw_price_df['y_target_direction']
+
+    # Fit the randomized search to the data
+    random_search.fit(X, y)
+
+    print(f"Best parameters found: {random_search.best_params_}")
+
+    # --- Final Model Training ---
+    # Initialize a new neural network model with the best parameters
+    final_model = MLPClassifier(**random_search.best_params_, max_iter=1000)
+
+    # Train the model on the entire dataset
+    final_model.fit(X, y)
+
+    # --- Export to ONNX ---
+    # Define the initial types for the ONNX conversion
+    initial_type = [('float_input', FloatTensorType([None, 1]))]
+
+    # Convert the model to ONNX format
+    onnx_model = skl2onnx.convert_sklearn(final_model, initial_types=initial_type)
+
+    # Save the ONNX model
+    with open("trading_neural_network_model.onnx", "wb") as f:
+        f.write(onnx_model.SerializeToString())
+
+    print("Model successfully exported to trading_neural_network_model.onnx")
+
+if __name__ == "__main__":
+    main()