Below is a piece of Python code allowing to download option chains from NASDAQ website. It is basically a big function relying heavily on BeautifulSoup and wrapped into a class (‘c’est chic’). User provides the ticker, expiry and a some other arguments. He is then returned a pandas.DataFrame object containing options data.
Notice that I wrote this piece of code for some home research project. If you were to use it and actually download data from NASDAQ website, make sure it is ‘fair use’ as stipulated in its terms of use section. I will not be responsible or liable, directly or indirectly, in any way bla bla bla.
''' Copyright (C) 2015, Edouard 'tagoma' Tallent Class fetching options data from www.nasdaq.com Nasdaq_option_quotes.py v0.2 (Nov15) QuantCorner @ https://quantcorner.wordpress.com ''' from bs4 import BeautifulSoup import requests import re import numpy as np import pandas as pd class NasdaqOptions(object): ''' Class NasdaqOptions fetches options data from Nasdaq website User inputs: Ticker: ticker - Ticker for the underlying Expiry: nearby - 1st Nearby: 1 - 2nd Nearby: 2 - etc ... Moneyness: money - All moneyness: all - In-the-money: in - Out-of-the-money: out - Near the money: near Market: market - Composite quote: Composite - Chicago Board Options Exchange: CBO - American Options Exchange: AOE - New York Options Exchange: NYO - Philadelphia Options Exchange: PHO - Montreal Options Exchange: MOE - Boston Options Exchange: BOX - International Securities Exchange: ISE - Bats Exchange Options Market: BTO - NASDAQ Options: NSO - C2(Chicago) Options Exchange: C2O - NASDAQ OMX BX Options Exchange: BXO - MIAX: MIAX Option category: expi - Weekly options: week - Monthly options: stand - Quarterly options: quart - CEBO options (Credit Event Binary Options): cebo ''' def __init__(self, ticker, nearby, money='near', market='cbo', expi='stan'): self.ticker = ticker self.nearby = nearby-1 # ' refers 1st nearby on NASDAQ website #self.type = type # Deprecated self.market = market self.expi = expi if money == 'near': self.money = '' else: self.money = '&money=' + money def get_options_table(self): ''' - Loop over as many webpages as required to get the complete option table for the option desired - Return a pandas.DataFrame() object ''' # Create an empty pandas.Dataframe object. New data will be appended to old_df = pd.DataFrame() # Variables loop = 0 # Loop over webpages starts at 0 page_nb = 1 # Get the top of the options table flag = 1 # Set a flag that will be used to call get_pager() old_rows_nb = 0 # Number of rows so far in the table # Loop over webpages while loop < int(page_nb): # Construct the URL '''url = 'http://www.nasdaq.com/symbol/' + self.ticker + '/option-chain?dateindex='\ + str(self.nearby) + '&callput=' + self.type + '&money=all&expi='\ + self.expi + '&excode=' + self.market + '&page=' + str(loop+1)''' url = 'http://www.nasdaq.com/symbol/' + self.ticker + '/option-chain?excode=' + self.market + self.money + '&expir=' + self.expi + '&dateindex=' + str(self.nearby) + '&page=' + str(loop+1) # Query NASDAQ website try: response = requests.get(url)#, timeout=0.1) # DNS lookup failure except requests.exceptions.ConnectionError as e: print('''Webpage doesn't seem to exist!\n%s''' % e) pass # Timeout failure except requests.exceptions.ConnectTimeout as e: print('''Slow connection!\n%s''' % e) pass # HTTP error except requests.exceptions.HTTPError as e: print('''HTTP error!\n%s''' % e) pass # Get webpage content soup = BeautifulSoup(response.content, 'html.parser') # Determine actual number of pages to loop over if flag == 1: # It is run only once # Get the number of page the option table lies on last_page_raw = soup.find('a', {'id': 'quotes_content_left_lb_LastPage'}) last_page = re.findall(pattern='(?:page=)(\d+)', string=str(last_page_raw)) page_nb = ''.join(last_page) flag = 0 # Extract table containing the option data from the webpage table = soup.find_all('table')[4] # table #4 in the webpage is the one of interest # Extract option data from table as a list elems = table.find_all('td') # Python object lst = [elem.text for elem in elems] # Option data as a readable list # Rearrange data and create a pandas.DataFrame arr = np.array(lst) reshaped = arr.reshape((len(lst)/16, 16)) new_df = pd.DataFrame(reshaped) frames = [old_df, new_df] old_df = pd.concat(frames) rows_nb = old_df.shape[0] # Increment loop counter if rows_nb > old_rows_nb: loop+=1 old_rows_nb = rows_nb elif rows_nb == old_rows_nb: print('Problem while catching data.\n## You must try again. ##') pass else: # Case where rows have been deleted # which shall never occur print('Failure!\n## You must try again. ##') pass # Name the column 'Strike' old_df.rename(columns={old_df.columns[8]:'Strike'}, inplace=True) ## Split into 2 dataframes (1 for calls and 1 for puts) calls = old_df.ix[:,1:7] puts = old_df.ix[:,10:16] # Slicing is not incluse of the last column # Set 'Strike' column as dataframe index calls = calls.set_index(old_df['Strike']) puts = puts.set_index(old_df['Strike']) ## Headers names headers = ['Last', 'Chg', 'Bid', 'Ask', 'Vol', 'OI'] calls.columns = headers puts.columns = headers return calls, puts if __name__ == '__main__': # Get data for Dec-15 SPX options, Dec-15 being the 2nd nearby options = NasdaqOptions('SPX',2) calls, puts = options.get_options_table() # Write on the screen print('\n######\nCalls:\n######\n', calls,\ '\n\n######\nPuts:\n######\n', puts) ''' ###### Calls: ###### Last Chg Bid Ask Vol OI Strike 1900 179.40 0 38292 1905 103.75 0 9693 1910 191.30 0 8378 1915 186.45 -1.97 0 3671 1920 173.95 0 7218 1925 181.51 6.38 1 23678 1930 170.90 5.30 10 15743 1935 160.37 0 17814 1940 152.30 0 10564 1945 88.82 0 10687 1950 149.73 0 66844 1955 124.81 0 11206 1960 120.60 0 10682 1965 123.60 0 10737 1970 143.00 5.50 789 15184 1975 136.48 3.70 1 25654 1980 129.15 0.01 41 12569 1985 127.90 12.45 3 15602 1990 98.86 0 7900 1995 98.25 0 10133 2000 114.40 1.30 31 116981 2005 103.20 3.90 3 15097 2010 98.45 -4.07 2 8119 2015 94.33 12.83 2 7058 2020 86.40 0 17249 2025 90.50 -1.35 20 59959 2030 89.65 3.35 99 3303 2035 80.45 1.55 1 2979 2040 77.10 -1.55 13 5517 2045 74.25 0.15 15 3768 ... ... ... .. .. ... ... 2115 28.75 1.45 136 6475 2120 26.12 1.42 64 5201 2125 23.90 1.40 154 28130 2130 21.27 1.67 10077 1647 2135 19.65 2.25 82 13484 2140 17.50 1.54 218 5150 2145 14.80 1.10 85 3644 2150 13.01 1.16 2073 55095 2155 11.23 0.98 71 1543 2160 9.90 1.05 70 10684 2165 8.60 1.05 14 1124 2170 7.23 0.52 28 2911 2175 6.30 0.77 166 22039 2180 5.30 0.70 74 5608 2185 4.55 0.65 8 524 2190 3.80 0.50 47 2154 2195 3.10 0.30 25 3563 2200 3.00 0.70 1815 63117 2205 2.15 0.13 32 429 2210 2.05 0.40 33 12771 2215 1.35 0 129 2220 1.50 0.40 5 2807 2225 1.10 0.09 61 18367 2230 0.90 0.05 25 187 2235 1.10 0.41 7 81 2240 0.70 0 444 2245 0.60 0 1060 2250 0.70 0.10 4058 42602 2275 0.35 0 37307 2300 0.30 0.05 9004 91173 [73 rows x 6 columns] ###### Puts: ###### Last Chg Bid Ask Vol OI Strike 1900 5.80 -0.16 3135 115697 1905 5.95 -0.90 1 9772 1910 5.70 -0.75 1 8667 1915 6.90 0.41 13 4304 1920 6.70 -0.20 1146 9707 1925 7.02 -1.18 271 50314 1930 7.40 0.10 61 21183 1935 7.85 0 16832 1940 8.25 -0.05 19 12021 1945 8.35 -0.05 4 20285 1950 9.05 0.17 5308 115872 1955 9.28 0.06 8 11626 1960 9.55 -0.08 5051 16218 1965 10.13 0.11 9 11052 1970 10.50 -0.10 115 16865 1975 10.80 -0.33 218 35755 1980 11.35 -0.35 13 15200 1985 12.05 -1.81 3 16854 1990 12.80 0.01 67 8195 1995 13.59 0.19 14 10430 2000 14.00 -1.00 5473 142800 2005 14.13 -0.63 20 16162 2010 14.96 -0.76 4 11485 2015 16.46 -0.32 1 7123 2020 16.55 -0.75 144 21422 2025 17.84 -0.06 88 47092 2030 18.17 -0.88 166 7872 2035 21.55 16 2749 2040 20.02 -0.62 106 5465 2045 21.33 -0.32 20 4603 ... ... ... .. .. ... ... 2115 51.10 0 10 2120 47.10 -0.40 6 147 2125 48.40 -1.15 19 5128 2130 50.00 -14.01 132 37 2135 54.50 0 1 2140 57.84 0 44 2145 64.35 -0.60 10 15 2150 63.16 0.51 5 9430 2155 0 2160 68.02 -142.03 40 1 2165 0 2170 79.50 -14.30 4 2 2175 98.90 0 153 2180 0 2185 0 2190 0 2195 112.28 0 8 2200 101.90 -5.30 1 5498 2205 0 2210 0 2215 208.50 0 5 2220 245.20 0 1 2225 247.66 0 84 2230 209.50 0 42 2235 0 2240 253.95 0 4 2245 0 2250 182.85 0 354 2275 205.80 0 796 2300 230.70 0 2562 [73 rows x 6 columns] '''
Versions history
v0.1 (Nov 02, 15): first release
v0.2 (Nov 03, 15): now returns a unique dataframe for calls and puts options; user can now select between 4 option moneyness (all, ITM, OTM, near the money)
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