qubitrenegade · October 27, 2024 18:36
diff --git a/stock-weekend-gaps.py b/stock-weekend-gaps.py
 #!/usr/bin/env python3

 """
 stock-weekend-gaps.py - Analyze stock price gaps between last trading day of week and first trading day of next week

 This script fetches historical stock data and analyzes the price gaps that occur over weekends
 and holidays. It captures the opening and closing prices of the last trading day of each week
 and the first trading day of the following week, accounting for holidays and irregular trading days.

 Installation:
  pip install yfinance pandas

 Usage:
  python3 stock-weekend-gaps.py AAPL              # Analyze Apple stock for last 5 years
  python3 stock-weekend-gaps.py AAPL --years=10   # Analyze Apple stock for last 10 years
  python3 stock-weekend-gaps.py AAPL --since=2019-10-10  # Analyze from specific date
  python3 stock-weekend-gaps.py AAPL -o custom_filename.csv  # Specify output file

 The script will create a CSV with columns:
  Week_End_Date: Date of last trading day of week (typically Friday)
  Week_End_Day: Day of week for last trading day
  Week_End_Open: Opening price of last trading day
  Week_End_Close: Closing price of last trading day
  Next_Open_Date: Date of first trading day of next week (typically Monday)
  Next_Open_Day: Day of week for first trading day
  Next_Open: Opening price of first trading day
  Next_Close: Closing price of first trading day
  Days_Between: Number of calendar days between trading days
  Price_Gap: Difference between Week_End_Close and Next_Open
  Price_Gap_Percent: Percentage change in price over the gap

 Data is fetched using Yahoo Finance API via yfinance library.
 """

 import yfinance as yf
 import pandas as pd
 from datetime import datetime, timedelta
 import argparse
 import sys
 import os

 def parse_date(date_str):
  """Parse date string in YYYY-MM-DD format"""
  try:
    return datetime.strptime(date_str, '%Y-%m-%d')
  except ValueError:
    raise argparse.ArgumentTypeError(f'Invalid date format: {date_str}. Use YYYY-MM-DD')

 def get_weekend_prices(ticker_symbol, start_date=None, years=None, output_file=None):
  """
  Fetch weekend (Friday close, Monday open/close) stock prices.
  
  Args:
    ticker_symbol (str): Stock ticker symbol
    start_date (datetime, optional): Start date for data collection
    years (int, optional): Number of years of historical data to fetch
    output_file (str, optional): Custom output file path
  """
  if not ticker_symbol:
    raise ValueError("Ticker symbol is required")
  
  ticker = yf.Ticker(ticker_symbol)
  
  try:
    ticker.info
  except:
    raise ValueError(f"Could not find ticker: {ticker_symbol}")
  
  end_date = datetime.now()
  if start_date:
    if start_date > end_date:
      raise ValueError("Start date cannot be in the future")
  elif years:
    start_date = end_date - timedelta(days=years*365)
  else:
    start_date = end_date - timedelta(days=5*365)
  
  print(f"Fetching data for {ticker_symbol} from {start_date.date()} to {end_date.date()}...")
  
  # Get the historical data with some buffer days to handle holidays
  df = ticker.history(start=start_date, end=end_date)
  
  if df.empty:
    raise ValueError(f"No data found for {ticker_symbol} in the specified date range")
  
  df = df.reset_index()
  df['DayOfWeek'] = df['Date'].dt.dayofweek
  
  # Create a new dataframe with weekend transitions
  results = []
  
  # Iterate through the dataframe
  i = 0
  while i < len(df) - 1:
    current_row = df.iloc[i]
    
    # Look for trading days near weekend (Thursday/Friday for end of week)
    if current_row['DayOfWeek'] <= 4:  # Monday to Friday
      # Find the last trading day of the week
      week_end_idx = i
      while (week_end_idx < len(df) - 1 and 
             df.iloc[week_end_idx]['Date'].week == current_row['Date'].week):
        week_end_idx += 1
      week_end_idx -= 1
      
      # Find the first trading day of next week
      next_week_idx = week_end_idx + 1
      while (next_week_idx < len(df) and 
             df.iloc[next_week_idx]['Date'].week == df.iloc[week_end_idx]['Date'].week):
        next_week_idx += 1
      
      if next_week_idx < len(df):
        week_end_row = df.iloc[week_end_idx]
        next_week_row = df.iloc[next_week_idx]
        
        # Calculate the number of calendar days between close and next open
        days_between = (next_week_row['Date'] - week_end_row['Date']).days
        
        results.append({
          'Week_End_Date': week_end_row['Date'].strftime('%Y-%m-%d'),
          'Week_End_Day': week_end_row['Date'].strftime('%A'),
          'Week_End_Open': week_end_row['Open'],
          'Week_End_Close': week_end_row['Close'],
          'Next_Open_Date': next_week_row['Date'].strftime('%Y-%m-%d'),
          'Next_Open_Day': next_week_row['Date'].strftime('%A'),
          'Next_Open': next_week_row['Open'],
          'Next_Close': next_week_row['Close'],
          'Days_Between': days_between,
          'Price_Gap': next_week_row['Open'] - week_end_row['Close'],
          'Price_Gap_Percent': ((next_week_row['Open'] - week_end_row['Close']) / week_end_row['Close'] * 100)
        })
      
      i = next_week_idx
    else:
      i += 1
  
  results_df = pd.DataFrame(results)
  
  if results_df.empty:
    print("No weekend transitions found in the specified date range")
    return None
  
  if not output_file:
    output_file = f"{ticker_symbol}_weekend_prices_{start_date.strftime('%Y%m%d')}-{end_date.strftime('%Y%m%d')}.csv"
  
  output_dir = os.path.dirname(output_file)
  if output_dir:
    os.makedirs(output_dir, exist_ok=True)
  
  # Save to CSV with original precision
  results_df.to_csv(output_file, index=False, float_format='%g')
  print(f"\nData saved to {output_file}")
  
  # Print summary statistics
  print("\nSummary Statistics:")
  print(f"Total weekend transitions: {len(results_df)}")
  print(f"Regular Friday-Monday transitions: {len(results_df[results_df['Days_Between'] == 3])}")
  print(f"Extended weekend transitions: {len(results_df[results_df['Days_Between'] > 3])}")
  print(f"Average price gap: {results_df['Price_Gap'].mean()}")
  print(f"Average price gap %: {results_df['Price_Gap_Percent'].mean()}%")
  print(f"Largest positive gap: {results_df['Price_Gap'].max()}")
  print(f"Largest negative gap: {results_df['Price_Gap'].min()}")
  
  # Print holiday weekend summary
  holiday_weekends = results_df[results_df['Days_Between'] > 3]
  if not holiday_weekends.empty:
    print("\nHoliday/Extended Weekends Found:")
    for _, row in holiday_weekends.iterrows():
      print(f"{row['Week_End_Day']} {row['Week_End_Date']} to {row['Next_Open_Day']} {row['Next_Open_Date']} "
            f"({row['Days_Between']} days)")
  
  return results_df

 def main():
  parser = argparse.ArgumentParser(
    description='Fetch weekend (Friday close, Monday open/close) stock prices.',
    formatter_class=argparse.RawTextHelpFormatter
  )
  parser.add_argument('ticker', nargs='?', help='Stock ticker symbol (e.g., AAPL)')
  parser.add_argument('--since', type=parse_date, help='Start date (YYYY-MM-DD)')
  parser.add_argument('--years', type=int, help='Number of years of historical data')
  parser.add_argument('--output', '-o', help='Output file path (default: TICKER_weekend_prices_STARTDATE-ENDDATE.csv)')
  
  args = parser.parse_args()
  
  if not args.ticker:
    args.ticker = input("Enter ticker symbol (e.g., AAPL): ").strip().upper()
  
  try:
    get_weekend_prices(args.ticker, args.since, args.years, args.output)
  except Exception as e:
    print(f"Error: {str(e)}", file=sys.stderr)
    sys.exit(1)

 if __name__ == "__main__":
  main()
	#!/usr/bin/env python3

	"""
	stock-weekend-gaps.py - Analyze stock price gaps between last trading day of week and first trading day of next week

	This script fetches historical stock data and analyzes the price gaps that occur over weekends
	and holidays. It captures the opening and closing prices of the last trading day of each week
	and the first trading day of the following week, accounting for holidays and irregular trading days.

	Installation:
	pip install yfinance pandas

	Usage:
	python3 stock-weekend-gaps.py AAPL # Analyze Apple stock for last 5 years
	python3 stock-weekend-gaps.py AAPL --years=10 # Analyze Apple stock for last 10 years
	python3 stock-weekend-gaps.py AAPL --since=2019-10-10 # Analyze from specific date
	python3 stock-weekend-gaps.py AAPL -o custom_filename.csv # Specify output file

	The script will create a CSV with columns:
	Week_End_Date: Date of last trading day of week (typically Friday)
	Week_End_Day: Day of week for last trading day
	Week_End_Open: Opening price of last trading day
	Week_End_Close: Closing price of last trading day
	Next_Open_Date: Date of first trading day of next week (typically Monday)
	Next_Open_Day: Day of week for first trading day
	Next_Open: Opening price of first trading day
	Next_Close: Closing price of first trading day
	Days_Between: Number of calendar days between trading days
	Price_Gap: Difference between Week_End_Close and Next_Open
	Price_Gap_Percent: Percentage change in price over the gap

	Data is fetched using Yahoo Finance API via yfinance library.
	"""

	import yfinance as yf
	import pandas as pd
	from datetime import datetime, timedelta
	import argparse
	import sys
	import os

	def parse_date(date_str):
	"""Parse date string in YYYY-MM-DD format"""
	try:
	return datetime.strptime(date_str, '%Y-%m-%d')
	except ValueError:
	raise argparse.ArgumentTypeError(f'Invalid date format: {date_str}. Use YYYY-MM-DD')

	def get_weekend_prices(ticker_symbol, start_date=None, years=None, output_file=None):
	"""
	Fetch weekend (Friday close, Monday open/close) stock prices.

	Args:
	ticker_symbol (str): Stock ticker symbol
	start_date (datetime, optional): Start date for data collection
	years (int, optional): Number of years of historical data to fetch
	output_file (str, optional): Custom output file path
	"""
	if not ticker_symbol:
	raise ValueError("Ticker symbol is required")

	ticker = yf.Ticker(ticker_symbol)

	try:
	ticker.info
	except:
	raise ValueError(f"Could not find ticker: {ticker_symbol}")

	end_date = datetime.now()
	if start_date:
	if start_date > end_date:
	raise ValueError("Start date cannot be in the future")
	elif years:
	start_date = end_date - timedelta(days=years*365)
	else:
	start_date = end_date - timedelta(days=5*365)

	print(f"Fetching data for {ticker_symbol} from {start_date.date()} to {end_date.date()}...")

	# Get the historical data with some buffer days to handle holidays
	df = ticker.history(start=start_date, end=end_date)

	if df.empty:
	raise ValueError(f"No data found for {ticker_symbol} in the specified date range")

	df = df.reset_index()
	df['DayOfWeek'] = df['Date'].dt.dayofweek

	# Create a new dataframe with weekend transitions
	results = []

	# Iterate through the dataframe
	i = 0
	while i < len(df) - 1:
	current_row = df.iloc[i]

	# Look for trading days near weekend (Thursday/Friday for end of week)
	if current_row['DayOfWeek'] <= 4: # Monday to Friday
	# Find the last trading day of the week
	week_end_idx = i
	while (week_end_idx < len(df) - 1 and
	df.iloc[week_end_idx]['Date'].week == current_row['Date'].week):
	week_end_idx += 1
	week_end_idx -= 1

	# Find the first trading day of next week
	next_week_idx = week_end_idx + 1
	while (next_week_idx < len(df) and
	df.iloc[next_week_idx]['Date'].week == df.iloc[week_end_idx]['Date'].week):
	next_week_idx += 1

	if next_week_idx < len(df):
	week_end_row = df.iloc[week_end_idx]
	next_week_row = df.iloc[next_week_idx]

	# Calculate the number of calendar days between close and next open
	days_between = (next_week_row['Date'] - week_end_row['Date']).days

	results.append({
	'Week_End_Date': week_end_row['Date'].strftime('%Y-%m-%d'),
	'Week_End_Day': week_end_row['Date'].strftime('%A'),
	'Week_End_Open': week_end_row['Open'],
	'Week_End_Close': week_end_row['Close'],
	'Next_Open_Date': next_week_row['Date'].strftime('%Y-%m-%d'),
	'Next_Open_Day': next_week_row['Date'].strftime('%A'),
	'Next_Open': next_week_row['Open'],
	'Next_Close': next_week_row['Close'],
	'Days_Between': days_between,
	'Price_Gap': next_week_row['Open'] - week_end_row['Close'],
	'Price_Gap_Percent': ((next_week_row['Open'] - week_end_row['Close']) / week_end_row['Close'] * 100)
	})

	i = next_week_idx
	else:
	i += 1

	results_df = pd.DataFrame(results)

	if results_df.empty:
	print("No weekend transitions found in the specified date range")
	return None

	if not output_file:
	output_file = f"{ticker_symbol}_weekend_prices_{start_date.strftime('%Y%m%d')}-{end_date.strftime('%Y%m%d')}.csv"

	output_dir = os.path.dirname(output_file)
	if output_dir:
	os.makedirs(output_dir, exist_ok=True)

	# Save to CSV with original precision
	results_df.to_csv(output_file, index=False, float_format='%g')
	print(f"\nData saved to {output_file}")

	# Print summary statistics
	print("\nSummary Statistics:")
	print(f"Total weekend transitions: {len(results_df)}")
	print(f"Regular Friday-Monday transitions: {len(results_df[results_df['Days_Between'] == 3])}")
	print(f"Extended weekend transitions: {len(results_df[results_df['Days_Between'] > 3])}")
	print(f"Average price gap: {results_df['Price_Gap'].mean()}")
	print(f"Average price gap %: {results_df['Price_Gap_Percent'].mean()}%")
	print(f"Largest positive gap: {results_df['Price_Gap'].max()}")
	print(f"Largest negative gap: {results_df['Price_Gap'].min()}")

	# Print holiday weekend summary
	holiday_weekends = results_df[results_df['Days_Between'] > 3]
	if not holiday_weekends.empty:
	print("\nHoliday/Extended Weekends Found:")
	for _, row in holiday_weekends.iterrows():
	print(f"{row['Week_End_Day']} {row['Week_End_Date']} to {row['Next_Open_Day']} {row['Next_Open_Date']} "
	f"({row['Days_Between']} days)")

	return results_df

	def main():
	parser = argparse.ArgumentParser(
	description='Fetch weekend (Friday close, Monday open/close) stock prices.',
	formatter_class=argparse.RawTextHelpFormatter
	)
	parser.add_argument('ticker', nargs='?', help='Stock ticker symbol (e.g., AAPL)')
	parser.add_argument('--since', type=parse_date, help='Start date (YYYY-MM-DD)')
	parser.add_argument('--years', type=int, help='Number of years of historical data')
	parser.add_argument('--output', '-o', help='Output file path (default: TICKER_weekend_prices_STARTDATE-ENDDATE.csv)')

	args = parser.parse_args()

	if not args.ticker:
	args.ticker = input("Enter ticker symbol (e.g., AAPL): ").strip().upper()

	try:
	get_weekend_prices(args.ticker, args.since, args.years, args.output)
	except Exception as e:
	print(f"Error: {str(e)}", file=sys.stderr)
	sys.exit(1)

	if __name__ == "__main__":
	main()
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