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Create yfinance_data/fetcher.py
Browse files- yfinance_data/fetcher.py +59 -0
yfinance_data/fetcher.py
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# yfinance_data/fetcher.py
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import yfinance as yf
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import pandas as pd
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def fetch_stock_data(symbol, start_date, end_date, interval='1h'):
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"""
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Fetches historical stock data for a given symbol from Yahoo Finance.
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Parameters:
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- symbol: The ticker symbol for the stock (str).
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- start_date: The start date for the data fetching in 'YYYY-MM-DD' format (str).
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- end_date: The end date for the data fetching in 'YYYY-MM-DD' format (str).
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- interval: The data interval. Valid intervals: '1m', '2m', '5m', '15m', '30m', '1h', '1d', '5d', '1wk', '1mo', '3mo' (str).
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Returns:
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- DataFrame: Historical stock data including date, open, high, low, close, volume.
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"""
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# Fetch the data
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data = yf.download(symbol, start=start_date, end=end_date, interval=interval)
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# Drop any NaNs (usually in case of missing data)
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data.dropna(inplace=True)
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return data
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def fetch_data_for_indicators(symbol, days=30):
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"""
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Fetches historical stock data for the past 30 days for both 4-hour and 1-hour intervals.
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Parameters:
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- symbol: The ticker symbol for the stock (str).
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- days: Number of days in the past to fetch data for (int).
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Returns:
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- Tuple of DataFrames: (data_4h, data_1h)
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"""
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# Calculate start and end dates
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end_date = pd.Timestamp.now()
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start_date = end_date - pd.Timedelta(days=days)
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# Convert dates to string format for the API call
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start_date_str = start_date.strftime('%Y-%m-%d')
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end_date_str = end_date.strftime('%Y-%m-%d')
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# Fetch data for both intervals
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data_4h = fetch_stock_data(symbol, start_date_str, end_date_str, interval='4h')
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data_1h = fetch_stock_data(symbol, start_date_str, end_date_str, interval='1h')
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return data_4h, data_1h
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# Example usage
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if __name__ == "__main__":
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symbol = "AAPL" # Example stock symbol
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data_4h, data_1h = fetch_data_for_indicators(symbol)
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print("4-Hour Data:")
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print(data_4h.head()) # Display the first few rows
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print("\n1-Hour Data:")
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print(data_1h.head()) # Display the first few rows
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