79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
import pandas as pd
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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from sklearn.metrics import mean_squared_error, mean_absolute_error
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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from tensorflow.keras.callbacks import EarlyStopping
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# Load the training and testing data
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training_data = pd.read_csv("3_years_training_data.csv")
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testing_data = pd.read_csv("3_month_testing_data.csv")
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# Drop unnecessary columns
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training_data = training_data.drop(columns=["Unnamed: 0", "Date"])
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testing_data = testing_data.drop(columns=["Unnamed: 0", "Date"])
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# Create lagged features for the model
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def create_lagged_features(data, n_lags=3):
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df = data.copy()
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for lag in range(1, n_lags + 1):
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df[f'Close_lag_{lag}'] = df['Close'].shift(lag)
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df.dropna(inplace=True) # Remove rows with NaN values due to shifting
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return df
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# Apply lagged features to the training and testing datasets
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training_data = create_lagged_features(training_data)
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testing_data = create_lagged_features(testing_data)
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# Separate features and target
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X_train = training_data.drop(columns=["Close"]).values
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y_train = training_data["Close"].values
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X_test = testing_data.drop(columns=["Close"]).values
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y_test = testing_data["Close"].values
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# Standardize the features
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train)
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X_test = scaler.transform(X_test)
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# Build the neural network model
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model = Sequential([
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Dense(64, activation='sigmoid', input_shape=(X_train.shape[1],)),
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Dense(32, activation='sigmoid'),
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Dense(16, activation='sigmoid'),
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Dense(1) # Output layer for regression
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])
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# Compile the model
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model.compile(optimizer='adam', loss='mse', metrics=['mae'])
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# Use early stopping to prevent overfitting
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early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
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# Train the model
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history = model.fit(
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X_train, y_train,
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epochs=100,
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batch_size=32,
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validation_split=0.2,
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callbacks=[early_stopping],
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verbose=1
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)
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# Evaluate the model on the test set
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y_pred = model.predict(X_test).flatten()
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mse = mean_squared_error(y_test, y_pred)
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mae = mean_absolute_error(y_test, y_pred)
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print(f"Neural Network MSE: {mse:.2f}")
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print(f"Neural Network MAE: {mae:.2f}")
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# Prepare the latest data to predict tomorrow's price
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latest_data = testing_data.tail(1).drop(columns=["Close"])
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latest_data_scaled = scaler.transform(latest_data)
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# Predict tomorrow's close price
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tomorrow_pred = model.predict(latest_data_scaled)
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print(f"Predicted Close Price for Tomorrow: {tomorrow_pred[0][0]:.2f}")
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