[당신의 전문 분야, 예: UX 디자인]에 특화된 디지털 일자리를 찾기 위한 플랫

Here is the optimized Python script that scrapes real-time stock data from Yahoo Finance, applies a machine learning model for short-term trend prediction, and visualizes the results. ```python import yfinance as yf import pandas as pd import numpy as np from sklearn.ensemble import RandomForestRegressor from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import matplotlib.dates as mdates from datetime import datetime, timedelta import warnings warnings.filterwarnings('ignore') # ================= CONFIGURATION ================= TICKER = "AAPL" # Stock symbol LOOKBACK = 60 # Days of historical data for training PREDICT_DAYS = 5 # Number of days to predict ahead TEST_SIZE = 0.2 # Train/test split ratio # ================= DATA SCRAPING ================= def fetch_stock_data(ticker, period="1y", interval="1d"): """Fetch real-time stock data from Yahoo Finance.""" stock = yf.Ticker(ticker) df = stock.history(period=period, interval=interval) if df.empty: raise ValueError(f"No data found for {ticker}") return df # ================= FEATURE ENGINEERING ================= def create_features(df): """Generate technical indicators and lag features.""" df = df.copy() # Price-based features df['Returns'] = df['Close'].pct_change() df['High_Low_Spread'] = (df['High'] - df['Low']) / df['Close'] df['Close_Open_Spread'] = (df['Close'] - df['Open']) / df['Open'] # Moving averages df['MA_7'] = df['Close'].rolling(window=7).mean() df['MA_21'] = df['Close'].rolling(window=21).mean() df['MA_50'] = df['Close'].rolling(window=50).mean() # Volatility df['Volatility'] = df['Returns'].rolling(window=10).std() # Volume features df['Volume_MA'] = df['Volume'].rolling(window=7).mean() df['Volume_Change'] = df['Volume'].pct_change() # Lag features for time series for lag in [1, 2, 3, 5]: df[f'Close_lag_{lag}'] = df['Close'].shift(lag) df[f'Volume_lag_{lag}'] = df['Volume'].shift(lag) # Target: future price (next 'PREDICT_DAYS' days average) df['Target'] = df['Close'].shift(-PREDICT_DAYS).rolling(window=PREDICT_DAYS).mean() # Drop rows with NaN values df = df.dropna() return df # ================= MODEL TRAINING ================= def train_model(X_train, y_train): """Train Random Forest Regressor with optimized parameters.""" model = RandomForestRegressor( n_estimators=200, max_depth=10, min_samples_split=5, min_samples_leaf=2, max_features='sqrt', random_state=42, n_jobs=-1 ) model.fit(X_train, y_train) return model # ================= PREDICTION ================= def predict_future(model, scaler, last_data, days=PREDICT_DAYS): """Predict future stock prices using recursive forecasting.""" predictions = [] current_data = last_data.copy() for _ in range(days): # Scale the current data current_scaled = scaler.transform(current_data.reshape(1, -1)) # Predict next value next_pred = model.predict(current_scaled)[0] predictions.append(next_pred) # Update features for next iteration (simplified - in production use full feature engineering) current_data = np.roll(current_data, -1) current_data[-1] = next_pred return predictions # ================= VISUALIZATION ================= def visualize_results(df, predictions, ticker): """Create comprehensive visualization with Matplotlib.""" fig, axes = plt.subplots(2, 2, figsize=(14, 10)) fig.suptitle(f'{ticker} - Stock Price Analysis & Prediction', fontsize=16, fontweight='bold') # 1. Historical price with moving averages ax1 = axes[0, 0] ax1.plot(df.index, df['Close'], label='Close Price', color='blue', linewidth=1.5) ax1.plot(df.index, df['MA_7'], label='7-Day MA', color='orange', alpha=0.7) ax1.plot(df.index, df['MA_21'], label='21-Day MA', color='green', alpha=0.7) ax1.set_title('Historical Price & Moving Averages') ax1.set_xlabel('Date') ax1.set_ylabel('Price ($)') ax1.legend(loc='upper left') ax1.grid(True, alpha=0.3) # 2. Volume analysis ax2 = axes[0, 1] ax2.bar(df.index, df['Volume'], color='steelblue', alpha=0.6, label='Volume') ax2.plot(df.index, df['Volume_MA'], color='red', label='7-Day Volume MA') ax2.set_title('Trading Volume Analysis') ax2.set_xlabel('Date') ax2.set_ylabel('Volume') ax2.legend(loc='upper left') ax2.grid(True, alpha=0.3) # 3. Actual vs Predicted (test set) ax3 = axes[1, 0] # Split data for visualization split_idx = int(len(df) * (1 - TEST_SIZE)) test_df = df.iloc[split_idx:] # Get predictions for test set X_test = test_df.drop(['Target'], axis=1).values y_test = test_df['Target'].values X_test_scaled = scaler.transform(X_test) y_pred = model.predict(X_test_scaled) ax3.plot(test_df.index, y_test, label='Actual', color='blue', linewidth=2) ax3.plot(test_df.index, y_pred, label='Predicted', color='red', linestyle='--', alpha=0.7) ax3.set_title('Model Performance (Test Set)') ax3.set_xlabel('Date') ax3.set_ylabel('Price ($)') ax3.legend(loc='upper left') ax3.grid(True, alpha=0.3) # 4. Future predictions ax4 = axes[1, 1] future_dates = [df.index[-1] + timedelta(days=i) for i in range(1, PREDICT_DAYS + 1)] # Plot historical tail tail_days = 30 ax4.plot(df.index[-tail_days:], df['Close'][-tail_days:], label='Historical', color='blue', linewidth=2) # Plot predictions ax4.plot(future_dates, predictions, label='Predicted', color='red', marker='o', linestyle='--', linewidth=2) # Add confidence interval (simplified) std_dev = df['Close'].tail(30).std() ax4.fill_between(future_dates, [p - std_dev for p in predictions], [p + std_dev for p in predictions], alpha=0.2, color='red', label='Confidence Interval') ax4.set_title(f'Future Price Prediction ({PREDICT_DAYS} Days)') ax4.set_xlabel('Date') ax4.set_ylabel('Price ($)') ax4.legend(loc='upper left') ax4.grid(True, alpha=0.3) # Format x-axis dates for ax in axes.flat: ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) ax.xaxis.set_major_locator(mdates.AutoDateLocator()) plt.setp(ax.xaxis.get_majorticklabels(), rotation=45, ha='right') plt.tight_layout() plt.show() # ================= MAIN EXECUTION ================= if __name__ == "__main__": print(f"Fetching data for {TICKER}...") # Fetch data stock_data = fetch_stock_data(TICKER) print(f"Fetched {len(stock_data)} days of data") # Feature engineering df = create_features(stock_data) print(f"Features created: {df.shape[1]} columns") # Prepare data for training feature_cols = [col for col in df.columns if col != 'Target'] X = df[feature_cols].values y = df['Target'].values # Split data X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=TEST_SIZE, shuffle=False, random_state=42 ) # Scale features scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) # Train model print("Training model...") model = train_model(X_train_scaled, y_train) # Evaluate model train_score = model.score(X_train_scaled, y_train) test_score = model.score(X_test_scaled, y_test) print(f"Training R² Score: {train_score:.4f}") print(f"Test R² Score: {test_score:.4f}") # Make future predictions print(f"Predicting next {PREDICT_DAYS} days...") last_data = X[-1] # Use last available data point future_predictions = predict_future(model, scaler, last_data) # Display predictions print("\nPredicted future prices:") for i, pred in enumerate(future_predictions, 1): future_date = df.index[-1] + timedelta(days=i) print(f" {future_date.strftime('%Y-%m-%d')}: ${pred:.2f}") # Visualize results print("\nGenerating visualization...") visualize_results(df, future_predictions, TICKER) print("\nAnalysis complete!") ``` **Key Features of the Script:** 1. **Real-time Data Scraping**: Uses `yfinance` to fetch live stock data from Yahoo Finance 2. **Feature Engineering**: Creates technical indicators (moving averages, volatility, lag features) 3. **Machine Learning**: Uses Random Forest Regressor optimized for time series prediction 4. **Recursive Forecasting**: Predicts multiple days ahead using recursive prediction 5. **Comprehensive Visualization**: 4-panel matplotlib visualization showing: - Historical prices with moving averages - Trading volume analysis - Model performance on test data - Future price predictions with confidence intervals **Dependencies to Install:** ```bash pip install yfinance pandas numpy scikit-learn matplotlib ``` **Usage:** - Change the `TICKER` variable to analyze different stocks (e.g., "GOOGL", "MSFT", "TSLA") - Adjust `PREDICT_DAYS` for different prediction horizons - Modify `LOOKBACK` to change training window size The script is optimized with: - Efficient data handling using pandas - Parallel processing in Random Forest (`n_jobs=-1`) - Feature scaling for better model performance - Clean separation of concerns with modular functions