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Runtime error
Runtime error
| import pandas as pd | |
| import numpy as np | |
| import torch | |
| from sklearn.preprocessing import StandardScaler | |
| try: | |
| import yfinance as yf | |
| except ImportError: | |
| raise ImportError("yfinance must be installed to fetch financial data.") | |
| def load_data(data_src="yahoo", ticker="AAPL", file_upload=None, start="2020-01-01", end="2023-01-01", horizon=1): | |
| main_df = None | |
| future_df = None | |
| if data_src == "yahoo": | |
| try: | |
| info = yf.Ticker(ticker).info | |
| if not info: | |
| raise ValueError(f"Ticker '{ticker}' not found.") | |
| # Fetch data up to end_date + horizon for future actuals | |
| extended_end = (pd.to_datetime(end) + pd.Timedelta(days=horizon)).strftime('%Y-%m-%d') | |
| df = yf.download(ticker, start=start, end=extended_end, progress=False) | |
| if df.empty: | |
| raise ValueError(f"No data found for ticker '{ticker}' in the specified date range. Please check the symbol and dates.") | |
| df = df[['Close']].dropna().rename(columns={'Close': 'value'}) | |
| df.reset_index(inplace=True) | |
| # Split into main_df (up to end_date) and future_df (beyond end_date) | |
| main_df = df[df['Date'] <= pd.to_datetime(end)].copy() | |
| future_df = df[(df['Date'] > pd.to_datetime(end)) & (df['Date'] <= pd.to_datetime(extended_end))].copy() | |
| except Exception as e: | |
| raise ValueError(f"Error fetching data for ticker '{ticker}': {e}") | |
| elif data_src == "csv": | |
| if file_upload is None: | |
| raise ValueError("CSV file upload required but not provided.") | |
| try: | |
| df = pd.read_csv(file_upload) | |
| except Exception as e: | |
| raise ValueError(f"Failed to read uploaded CSV file: {e}") | |
| if 'value' not in df.columns: | |
| if 'Close' in df.columns: | |
| df = df[['Close']].rename(columns={'Close': 'value'}) | |
| else: | |
| raise ValueError("CSV must contain a 'value' or 'Close' column.") | |
| df['Date'] = pd.to_datetime(df.get('Date', df.index)) | |
| df = df[['Date', 'value']].dropna().reset_index(drop=True) | |
| # Split into main_df (up to end_date) and future_df (beyond end_date) | |
| main_df = df[df['Date'] <= pd.to_datetime(end)].copy() | |
| future_df = df[(df['Date'] > pd.to_datetime(end)) & (df['Date'] <= pd.to_datetime(end) + pd.Timedelta(days=horizon))].copy() | |
| else: | |
| raise ValueError("Invalid data source. 'csv' or 'yahoo' expected.") | |
| return main_df, future_df | |
| def preprocess_data(df, column, window_size=30): | |
| scaler = StandardScaler() | |
| data = df[[column]].values.astype(float) | |
| scaled = scaler.fit_transform(data) | |
| X, y = [], [] | |
| for i in range(len(scaled) - window_size): | |
| X.append(scaled[i:i + window_size]) | |
| y.append(scaled[i + window_size]) | |
| X = np.array(X) | |
| y = np.array(y) | |
| return X, y, scaler |