download_options.py

import token
import tokenize
from six.moves import cStringIO as StringIO
import json
from pandas.io.json import json_normalize
import requests
import pandas as pd


def __fix_lazy_json(in_text):
    """
    Handle lazy JSON - to fix expecting property name
    this function fixes the json output from google
    http://stackoverflow.com/questions/4033633/handling-lazy-json-in-python-expecting-property-name
    """
    tokengen = tokenize.generate_tokens(StringIO(in_text).readline)

    result = []
    for tokid, tokval, _, _, _ in tokengen:
        # fix unquoted strings
        if (tokid == token.NAME):
            if tokval not in ['true', 'false', 'null', '-Infinity', 'Infinity', 'NaN']:
                tokid = token.STRING
                tokval = u'"%s"' % tokval

        # fix single-quoted strings
        elif (tokid == token.STRING):
            if tokval.startswith("'"):
                tokval = u'"%s"' % tokval[1:-1].replace('"', '\\"')

        # remove invalid commas
        elif (tokid == token.OP) and ((tokval == '}') or (tokval == ']')):
            if (len(result) > 0) and (result[-1][1] == ','):
                result.pop()

        # fix single-quoted strings
        elif (tokid == token.STRING):
            if tokval.startswith("'"):
                tokval = u'"%s"' % tokval[1:-1].replace('"', '\\"')

        result.append((tokid, tokval))

    return tokenize.untokenize(result)


def Options(symbol):
    url = "https://www.google.com/finance/option_chain"
    r = requests.get(url, params={"q": symbol, "output": "json"})
    content_json = r.text
    dat = json.loads(__fix_lazy_json(content_json))
    puts = json_normalize(dat['puts'])
    calls = json_normalize(dat['calls'])
    np = len(puts)
    nc = len(calls)

    for i in dat['expirations'][1:]:
        r = requests.get(url, params={"q": symbol, "expd": i['d'], "expm": i[
                         'm'], "expy": i['y'], "output": "json"})
        content_json = r.text
        idat = json.loads(__fix_lazy_json(content_json))
        puts1 = json_normalize(idat['puts'])
        calls1 = json_normalize(idat['calls'])
        puts1.index = [np + i for i in puts1.index]
        calls1.index = [nc + i for i in calls1.index]
        np += len(puts1)
        nc += len(calls1)
        puts = puts.append(puts1)
        calls = calls.append(calls1)
    calls.columns = ['Ask', 'Bid', 'Chg', 'cid', 'PctChg', 'cs', 'IsNonstandard',
                     'Expiry', 'Underlying', 'Open_Int', 'Last', 'Symbol', 'Strike', 'Vol']
    puts.columns = ['Ask', 'Bid', 'Chg', 'cid', 'PctChg', 'cs', 'IsNonstandard',
                    'Expiry', 'Underlying', 'Open_Int', 'Last', 'Symbol', 'Strike', 'Vol']
    calls['Type'] = ['call' for i in range(len(calls))]
    puts['Type'] = ['put' for i in range(len(puts))]
    puts.index = [i + len(calls) for i in puts.index]
    opt = pd.concat([calls, puts])
    opt['Underlying'] = [symbol for i in range(len(opt))]
    opt['Underlying_Price'] = [dat['underlying_price']
                               for i in range(len(opt))]
    opt['Root'] = opt['Underlying']
    for j in ['Vol', 'Strike', 'Last', 'Bid', 'Ask', 'Chg']:
        opt[j] = pd.to_numeric(opt[j], errors='coerce')
    opt['IsNonstandard'] = opt['IsNonstandard'].apply(lambda x: x != 'OPRA')
    opt = opt.sort_values(by=['Strike', 'Type'])
    opt.index = range(len(opt))
    col = ['Strike', 'Expiry', 'Type', 'Symbol', 'Last', 'Bid', 'Ask', 'Chg', 'PctChg', 'Vol',
           'Open_Int', 'Root', 'IsNonstandard', 'Underlying', 'Underlying_Price', 'cid', 'cs']
    opt = opt[col]

    return opt