t1 = time.time()
options = pd.read_csv("options.csv")
tickers = options['Symbol']
#tickers=['GNW','MBI','VLO','BP']
yahoo_cap_url = 'http://finance.yahoo.com/d/quotes.csv?s='
bank_roll = 14000
offers = []
for e in tickers:
expiry_2 = (2018, 1)
expiry_1 = (2017, 1)
tick = Options(e, 'yahoo')
try:
frame = tick.get_call_data(month=expiry_2[1], year=expiry_2[0])
a = list(frame.index)
except:
try:
frame = tick.get_call_data(month=expiry_1[1], year=expiry_1[0])
a = list(frame.index)
except:
print e
pass
strike = []
for e in a:
strike.append(e[0])
frame['Strike'] = strike
frame['Mark'] = frame["Bid"] + ((frame["Ask"] - frame["Bid"]) / 2)
frame['Ratio'] = frame['Strike'] / frame['Mark']
today = dt.date.today() one_week = dt.timedelta(days=7) start = today - one_week stock = web.DataReader(ticker, 'yahoo', start) print(stock.tail()) # just to see what we have # take the last close (that's what the -1 does) atm = stock.ix[-1,'Close'] # the -1 takes the last observation #%% # get option prices for same ticker # http://pandas.pydata.org/pandas-docs/stable/remote_data.html#yahoo-finance-options option = Options(ticker, 'yahoo') expiry = dt.date(2014, 12, 20) data_calls = option.get_call_data(expiry=expiry) data_puts = option.get_put_data(expiry=expiry) print(data_calls.tail()) print(data_puts.tail()) #%% # plot puts v strike, call v strike calls_bid = data_calls['Bid'] calls_ask = data_calls['Ask'] calls_strikes = data_calls['Strike'] calls_mid = (data_calls['Bid'] + data_calls['Ask'])/2 puts_strikes = data_puts['Strike'] puts_mid = (data_puts['Bid'] + data_puts['Ask'])/2
def call_data(tickrr, exp_date):
x = Options(ticker, 'yahoo')
data = x.get_call_data(expiry=exp_date)
return data
###############################################################################
securities = ['spy', 'iwm', 'qqq', 'aapl', 'nflx', 'kmx']
for s in securities:
try:
opt = Options(s, 'yahoo')
today = datetime.datetime.today()
today = datetime.date(today.year, today.month, today.day)
for e in opt.expiry_dates:
if (e - today).days > 4 and (e - today).days < 15:
expiry = e
call = opt.get_call_data(expiry=expiry)
price = call.Underlying_Price[0]
prices, p = cdf(s, expiry, price)
bull = bull_call_spread()
if bull.empty:
print('%s is Empty' %s)
else:
print(filter_options('bull', prices, p, bull[bull.rror > 0.1], 0.3))
bear = bear_call_spread()
# Calculate Best Options for multiple indexes and secruities
###############################################################################
securities = ['spy', 'iwm', 'qqq', 'aapl', 'nflx', 'kmx']
for s in securities:
try:
opt = Options(s, 'yahoo')
today = datetime.datetime.today()
today = datetime.date(today.year, today.month, today.day)
for e in opt.expiry_dates:
if (e - today).days > 4 and (e - today).days < 15:
expiry = e
call = opt.get_call_data(expiry=expiry)
price = call.Underlying_Price[0]
prices, p = cdf(s, expiry, price)
bull = bull_call_spread()
if bull.empty:
print('%s is Empty' % s)
else:
print(filter_options('bull', prices, p, bull[bull.rror > 0.1],
0.3))
bear = bear_call_spread()
if bear.empty:
print('%s is Empty' % s)
today = dt.date.today() one_week = dt.timedelta(days=7) start = today - one_week stock = web.DataReader(ticker, 'yahoo', start) print(stock.tail()) # just to see what we have # take the last close (that's what the -1 does) atm = stock.ix[-1, 'Close'] # the -1 takes the last observation #%% # get option prices for same ticker # http://pandas.pydata.org/pandas-docs/stable/remote_data.html#yahoo-finance-options option = Options(ticker, 'yahoo') expiry = dt.date(2014, 12, 20) data_calls = option.get_call_data(expiry=expiry) data_puts = option.get_put_data(expiry=expiry) print(data_calls.tail()) print(data_puts.tail()) #%% # plot puts v strike, call v strike calls_bid = data_calls['Bid'] calls_ask = data_calls['Ask'] calls_strikes = data_calls['Strike'] calls_mid = (data_calls['Bid'] + data_calls['Ask']) / 2 puts_strikes = data_puts['Strike'] puts_mid = (data_puts['Bid'] + data_puts['Ask']) / 2
def call_data(tickrr,exp_date):
x = Options(ticker,'yahoo')
data= x.get_call_data(expiry=exp_date)
return data
from pandas.io.data import Options
import datetime
aapl = Options('AAPL', 'yahoo')
expiry = datetime.date(2014, 5, 1)
aapl.get_call_data(expiry=expiry)
