Create MarketPredictionModel.py

coin/QuantumCoin/AI/MarketPredictionModel.py

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+# MarketPredictionModel.py
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.preprocessing import StandardScaler
+from xgboost import XGBRegressor
+from sklearn.metrics import mean_squared_error, r2_score
+import joblib
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+
+class MarketPredictionModel:
+    def __init__(self, data_file):
+        self.data_file = data_file
+        self.model = XGBRegressor()
+        self.scaler = StandardScaler()
+
+    def load_data(self):
+        # Load historical market data
+        self.data = pd.read_csv(self.data_file)
+        logging.info("Data loaded successfully.")
+
+    def preprocess_data(self):
+        # Preprocessing: Normalize features and create target variable
+        self.data['Date'] = pd.to_datetime(self.data['Date'])
+        self.data.set_index('Date', inplace=True)
+        self.data['Target'] = self.data['Price'].shift(-1)  # Predict next day's price
+        self.data.dropna(inplace=True)
+
+        # Normalize features
+        features = ['Open', 'High', 'Low', 'Volume']
+        self.X = self.scaler.fit_transform(self.data[features])
+        self.y = self.data['Target'].values
+
+    def train_model(self):
+        # Split data into training and testing sets
+        X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.2, random_state=42)
+
+        # Hyperparameter tuning using GridSearchCV
+        param_grid = {
+            'n_estimators': [100, 200],
+            'max_depth': [3, 5, 7],
+            'learning_rate': [0.01, 0.1, 0.2]
+        }
+        grid_search = GridSearchCV(self.model, param_grid, scoring='neg_mean_squared_error', cv=3)
+        grid_search.fit(X_train, y_train)
+
+        self.model = grid_search.best_estimator_
+        logging.info(f"Best parameters: {grid_search.best_params_}")
+
+        # Evaluate the model
+        predictions = self.model.predict(X_test)
+        mse = mean_squared_error(y_test, predictions)
+        r2 = r2_score(y_test, predictions)
+        logging.info(f"Model trained. MSE: {mse}, R^2: {r2}")
+
+    def save_model(self, model_file):
+        joblib.dump(self.model, model_file)
+        joblib.dump(self.scaler, 'scaler.pkl')  # Save the scaler as well
+        logging.info(f"Model and scaler saved to {model_file} and scaler.pkl")
+
+    def load_model(self, model_file):
+        self.model = joblib.load(model_file)
+        self.scaler = joblib.load('scaler.pkl')  # Load the scaler
+        logging.info(f"Model and scaler loaded from {model_file} and scaler.pkl")
+
+    def predict(self, input_data):
+        # Predict market price based on input data
+        input_data_scaled = self.scaler.transform(np.array(input_data).reshape(1, -1))
+        prediction = self.model.predict(input_data_scaled)
+        return prediction[0]
+
+# Example usage
+if __name__ == "__main__":
+    model = MarketPredictionModel("historical_market_data.csv")
+    model.load_data()
+    model.preprocess_data()
+    model.train_model()
+    model.save_model("market_prediction_model.pkl")
+
+    # Example prediction
+    input_data = [100, 105, 95, 1000]  # Open, High, Low, Volume
+    predicted_price = model.predict(input_data)
+    logging.info(f"Predicted next day's price: {predicted_price}")